Behavioral analysis device, behavioral analysis method, and program

Abstract

Conventionally, it has not been possible to obtain the degree of disruption of a user's behavior. [Solution] A behavior analysis device 7 including a reception unit 72 that receives time series information including two or more time-series behavioral information that corresponds to time information specifying the time when the user acted and is information specifying the user's behavior, a reference management unit 711 that stores reference information that is the basis for obtaining the degree of disorder of the user's behavior corresponding to the time series information, a score acquisition unit 732 that uses the time series information received by the reception unit 72 and the reference information of the reference management unit 711 to obtain a disorder score that is the degree of disorder of the user's behavior, and a score output unit 741 that outputs the disorder score obtained by the score acquisition unit 732, can obtain a disorder score that is the degree of disorder of the user's behavior.

Description

[Technical field]

[0001] The present invention relates to a behavior analysis device and the like that acquires the degree of disruptive behavior of a user. [Background technology]

[0002] Conventionally, there has been a behavior determination system that determines a resident's behavior, such as sleep, by using energy consumption data (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6470497 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the conventional technology, it is not possible to obtain the degree of disruption of a user's behavior. [Means for solving the problem]

[0005] The behavioral analysis device of the first invention is a behavioral analysis device that includes a reception unit that receives time series information including two or more time-series behavioral information that corresponds to time information that specifies the time when the user performed a behavior and is information that specifies the user's behavior, a reference management unit that stores reference information that is the basis for obtaining the degree of disorder of the user's behavior corresponding to the time series information, a score acquisition unit that uses the time series information received by the reception unit and the reference information in the reference management unit to obtain a disorder score that is the degree of disorder of the user's behavior, and a score output unit that outputs the disorder score obtained by the score acquisition unit.

[0006] With this configuration, a disorder score that indicates the degree of disorder in the user's behavior can be obtained.

[0007] Furthermore, in contrast to the first invention, the behavior analysis device of the second invention is a behavior analysis device in which the reference information includes own reference information that is information based on the user's past time-series information.

[0008] With this configuration, the disturbance score can be obtained using the self-reference information.

[0009] Furthermore, in the behavioral analysis device of the third invention, compared to the second invention, the reference information is a learning model obtained by performing a machine learning learning process using two or more teacher data having explanatory variables based on the user's past time-series information and a target variable which is disturbance information related to the degree of disturbance of the user's behavior, and the score acquisition unit is a behavioral analysis device that performs a machine learning prediction process using the time-series information and learning model received by the receiving unit to obtain a disturbance score.

[0010] With this configuration, a disturbance score can be obtained through machine learning prediction processing.

[0011] In addition, the behavioral analysis device of the fourth invention is a behavioral analysis device in which, compared to any one of the first to third inventions, the reference information includes one or more types of information among recommended behavior information that identifies recommended or non-recommended behavior, and other reference information based on behavioral information of one or more other persons other than the user.

[0012] With this configuration, the disorder score can be obtained by using one or more of the recommended behavior information and the other person's reference information.

[0013] In addition, the behavior analysis device of the fifth invention is a behavior analysis device according to any one of the first to fourth inventions, in which the user's time-series behavior information and reference information include environmental information that specifies the environment of the behavior location.

[0014] With this configuration, the disturbance score can be obtained by also using the environmental information.

[0015] In addition, the behavioral analysis device of the sixth invention is a behavioral analysis device according to any one of the first to fifth inventions, further comprising a judgment unit which judges whether the disturbance score satisfies the disturbance condition, a factor acquisition unit which, when the judgment unit judges that the disturbance condition is satisfied, acquires a disturbance factor related to the behavioral information that is a cause of the disturbance from the behavioral information contained in the time-series information received by the reception unit corresponding to the disturbance score, and a factor output unit which outputs the disturbance factor acquired by the factor acquisition unit.

[0016] With this configuration, the cause of the user's disruptive behavior can be obtained.

[0017] In addition, the behavioral analysis device of the seventh invention is a behavioral analysis device that, compared to the sixth invention, further includes a recommendation acquisition unit that refers to a recommendation management unit in which recommendation source information that is the source of information for improving a disturbance is stored in correspondence with each of two or more factor conditions that are conditions related to disturbance factors, and acquires recommendation information using the recommendation source information that corresponds to the factor conditions that match the disturbance factors acquired by the factor acquisition unit, and a recommendation output unit that outputs the recommendation information acquired by the recommendation acquisition unit.

[0018] With this configuration, recommendations can be made to improve the user's irregular behavior.

[0019] In addition, the behavioral analysis device of the eighth invention is a behavioral analysis device comprising: a score acquisition unit that acquires a first disorder score and a second disorder score, which are disorder scores at each of two time periods; an improvement degree acquisition unit that acquires an improvement degree, which is information regarding the difference between the first disorder score and the second disorder score and is information specifying the degree of improvement of the disorder; and an improvement degree output unit that outputs the improvement degree acquired by the improvement degree acquisition unit.

[0020] With this configuration, the degree of improvement in the user's irregular behavior can be obtained.

[0021] In addition, the behavioral analysis device of the ninth invention is a behavioral analysis device in which, compared to the eighth invention, the improvement degree acquisition unit acquires the improvement degree when the first disorder score and the second disorder score satisfy an improvement degree output condition.

[0022] With this configuration, the degree of improvement in the user's irregular behavior can be output at an appropriate timing.

[0023] In addition, the behavioral analysis device of the tenth invention is a behavioral analysis device according to any one of the first to ninth inventions, further comprising: a score acquisition unit that acquires a first disorder score and a second disorder score, which are disorder scores at each of two time periods, and a judgment unit that judges whether the first disorder score and the second disorder score satisfy a recovery condition; a recovery period acquisition unit that acquires a recovery period, which is the difference between first time information corresponding to the first disorder score and second time information corresponding to the second disorder score, when the judgment unit judges that the recovery condition is satisfied; and a recovery period output unit that outputs the recovery period acquired by the recovery period acquisition unit.

[0024] With this configuration, the recovery period from a disturbance in the user's behavior can be obtained.

[0025] In addition, the behavioral analysis device of the eleventh invention is a behavioral analysis device according to any one of the first to tenth inventions, further comprising a long-term score acquisition unit that acquires a long-term disorder score specifying the degree of long-term behavioral disorder using the two or more disorder scores acquired by the score acquisition unit, and a long-term score output unit that outputs the long-term disorder score acquired by the long-term score acquisition unit, wherein the receiving unit receives two or more pieces of time series information and the score acquisition unit acquires a disorder score for each of the two or more pieces of time series information.

[0026] With this configuration, a long-term disorder score, which is a measure of the degree of disorder in the user's long-term behavior, can be obtained.

[0027] In addition, the behavioral analysis device of the twelfth invention is a behavioral analysis device that, compared to the eleventh invention, further includes a judgment unit that judges whether each of two or more disorder scores satisfies a disorder condition, and the long-term score acquisition unit acquires the number of times that the judgment unit judges that the disorder condition is satisfied, and acquires the long-term disorder score using the number of times.

[0028] With this configuration, an appropriate long-term disorder score, which is a measure of the degree of disorder in the user's long-term behavior, can be obtained.

[0029] In addition, the behavior analysis device of the thirteenth invention is the behavior analysis device of any one of the first to twelfth inventions, further comprising a time acquisition unit that acquires a time, a location acquisition unit that acquires location information corresponding to the time, and a behavior estimation unit that uses two or more pieces of behavior source information including the location information corresponding to 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 the receiving unit is the behavior analysis device that receives the behavior information for each of the two or more time periods acquired by the behavior estimation unit.

[0030] With this configuration, the user's behavior can be estimated using location information associated with time.

[0031] In addition, the behavioral analysis device of the fourteenth invention, compared to the thirteenth invention, further includes a receiving unit that receives radio waves including a device identifier that identifies the communication device from each of the 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 behavioral analysis device that acquires a terminal location, which is location information of the terminal device, using the radio wave intensities of the three or more communication devices that the type determination unit has determined to be fixed terminals.

[0032] With this configuration, the location of the terminal device indoors or the like can be easily obtained. Effect of the Invention

[0033] According to the behavior analysis device of the present invention, it is possible to obtain a disorder score, which is the degree of disorder in a user's behavior. [Brief description of the drawings]

[0034] [Figure 1] A conceptual diagram of an information system A including a location information production device 1 according to the first embodiment. [Diagram 2] Block diagram of the same-location information production device 1 [Diagram 3] A flowchart for explaining an example of the operation of the same-location information production device 1. [Figure 4] A flowchart for explaining an example of a simultaneous sequence intensity acquisition process. [Diagram 5] A flowchart illustrating an example of the fixed information acquisition process. [Figure 6] Flowchart for explaining an example of a process for determining the same type [Figure 7] A chart showing the simultaneous signal strength management table [Figure 8] A diagram showing the location information management table [Figure 9] Conceptual diagram of information system C in embodiment 2 [Figure 10] Block diagram of the terminal device 2 [Figure 11] A flowchart for explaining a first operation example of the terminal device 2. [Figure 12] A flowchart illustrating an example of the movement determination process. [Figure 13] A flowchart illustrating an example of the position estimation process [Figure 14] A flowchart for explaining a second operation example of the terminal device 2. [Figure 15] A flowchart for explaining a second example of the same type determination process [Figure 16] Conceptual diagram of information system D in embodiment 3 [Figure 17] Block diagram of information system D [Figure 18] Block diagram of the behavior acquisition device 3 [Figure 19]A flowchart illustrating an example of the operation of the behavior acquisition device 3. [Figure 20] A flowchart illustrating an example of the action source acquisition process [Figure 21] A flowchart illustrating an example of the position estimation process [Figure 22] 11 is a flowchart illustrating an example of the first behavior estimation process. [Figure 23] 11 is a flowchart illustrating an example of the second behavior estimation process. [Figure 24] 11 is a flowchart illustrating an example of the third behavior estimation process. [Diagram 25] 1 is a flowchart illustrating an example of the first emotion estimation process. [Figure 26] 11 is a flowchart illustrating an example of the second emotion estimation process. [Figure 27] 11 is a flowchart illustrating an example of the third emotion estimation process. [Figure 28] A flowchart illustrating an example of the output configuration process. [Figure 29] 11 is a flowchart illustrating an example of the first behavior learning process. [Diagram 30] 11 is a flowchart illustrating an example of the second behavior learning process. [Diagram 31] 1 is a flowchart illustrating an example of the first emotion learning process. [Diagram 32] 11 is a flowchart illustrating an example of the second emotion learning process. [Diagram 33] A diagram showing the action source management table [Diagram 34] A diagram showing the behavior and emotion management chart [Diagram 35] A diagram showing the same time period information management table [Diagram 36] A diagram showing an example of the output [Figure 37] Conceptual diagram of information system E in embodiment 4 [Figure 38] Block diagram of information system E [Figure 39] Block diagram of the behavior acquisition device 5 [Diagram 40] A flowchart for explaining an example of the operation of the behavior acquisition device 5 [Diagram 41] A flowchart for explaining an example of the operation of the terminal device 6 [Diagram 42] Conceptual diagram of information system F in embodiment 5 [Diagram 43] Block diagram of the information system F [Diagram 44] Block diagram of the behavioral analysis device 7 [Diagram 45] A flowchart for explaining an example of the operation of the behavior analysis device 7. [Diagram 46] A flowchart explaining a first example of the score acquisition process. [Figure 47] A flowchart explaining a second example of the score acquisition process. [Figure 48] A flowchart explaining a third example of the score acquisition process. [Figure 49] A flowchart illustrating an example of the cause acquisition process [Figure 50] A flowchart for explaining an example of the recommendation acquisition process. [Figure 51] A flowchart illustrating an example of the long-term score acquisition process. [Figure 52] A flowchart illustrating an example of the learning process. [Figure 53] FIG. 13 is an example of a same-time-period information management table. [Figure 54] A diagram showing an example of the recommendation management table. [Figure 55] A diagram showing an example of the output [Figure 56] A diagram showing an example of the output [Figure 57] Block diagram of the behavioral analysis device 9 [Figure 58] Block diagram of a computer system according to the above embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0035] Hereinafter, an embodiment of a behavior analysis 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.

[0036] (Embodiment 1) In this embodiment, a location information production device will be described. The location information production device is a device that acquires location information, which will be described later, of a specific location.

[0037] 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.

[0038] 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 for information Z, etc., and is sufficient as long as information Z can be accessed.

[0039] 1 is a conceptual diagram of an information system A including a location information production device 1 in this embodiment. The information system A has the location information production device 1 and three or more communication devices B.

[0040] 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 a device identifier that identifies the communication device B to the other devices. The communication device B is, for example, a Wi-fi router or a communication device using BLE (Bluetooth Low Energy), but it does not matter.

[0041] 2 is a block diagram of the location information production device 1 in this 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.

[0042] The reception unit 12 receives various instructions and information. The various instructions and information are, for example, position information described below. The instructions and information can be inputted by any means, such as a touch panel, a keyboard, a mouse, or a menu screen.

[0043] The position receiving unit 121 receives position information of specific points. The position receiving unit 121 usually receives position information of three or more specific points. The position receiving unit 121 receives, for example, position information that is an input by a user. The position receiving unit 121 reads out the position information from the storage unit 11, for example.

[0044] The specific point is a specific indoor point, but may also be a specific outdoor point. Here, the location information is information that specifies a location indoors or outdoors. The location information is, for example, three-dimensional coordinate values ​​(x, y, z) that indicate a relative position indoors or outdoors, but may also be two-dimensional coordinate values ​​(x, y). The origin of the coordinate values ​​for specifying a relative position indoors or outdoors does not matter. The specific outdoor point is preferably, for example, a point where a GPS signal is difficult to reach, such as in a group of high-rise buildings or in a forest, but does not matter. The location information may be information that allows a person to recognize a location (for example, a character string) or an ID. Such location information may be, for example, a label such as "living room", "work room", "conference room", "east side of the library", or "toy section of a department store".

[0045] The position receiving unit 121 may generate a unique ID. Such a unique ID may be considered as a label and position information.

[0046] It is not necessary for the position receiving unit 121 to receive position information. In this case, the position receiving unit 121 is not necessary.

[0047] 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 normally receives the radio waves including the device identifiers from each of three or more communication devices B in succession.

[0048] The device identifier is information that identifies the communication device B. The device identifier is, for example, the ID of the communication device B or the name of the communication device B. Reception of radio waves can be considered as reception of information.

[0049] The processing unit 14 performs various types of processing. The various types of processing are, for example, processing performed by an intensity acquisition unit 141, a type determination unit 142, and a storage unit 143.

[0050] The strength acquisition unit 141 acquires the strength of radio waves received from each of the three or more communication devices B. The strength acquisition unit 141 acquires the radio wave strength in pairs with the device identifier of the communication device B. The strength acquisition unit 141 acquires radio wave strength in time series. The radio wave strength in time series refers to two or more radio wave strengths that are consecutive in time. It goes without saying that being consecutive in time may include a time interval.

[0051] 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 whose installation location is fixed. A mobile terminal is a communication device whose installation location is not fixed and which moves.

[0052] The type determination unit 142, for example, obtains the degree of variation of two or more radio wave strengths that are consecutive in time in a time series paired with one 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 one device identifier is a mobile terminal. Also, the type determination unit 142, for example, obtains the degree of variation of two or more radio wave strengths that are consecutive in time in a time series paired with one 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 one device identifier is a fixed terminal.

[0053] The degree of dispersion is information indicating the degree of dispersion or change in radio wave strength over time. The degree of dispersion is, for example, a number based on variance, standard deviation, or difference (for example, the difference, the sum of the difference between two consecutive radio wave strengths among three or more consecutive radio wave strengths).

[0054] The type determination unit 142, for example, determines that the communication device B identified by a certain device identifier is a mobile terminal when 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, which is the number of radio wave intensities obtained in a specified period of time.

[0055] The storage unit 143 configures and stores location information having a device identifier and radio wave strength 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 strength for each of the three or more communication devices B.

[0056] The accumulation unit 143 configures and accumulates location information having, for example, a device identifier, radio wave strength, 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 configures and accumulates location information having a device identifier, radio wave strength, 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 in another device. It is preferable that the location information has location information, but it is not necessary that the location information has location information. The location information may be composed of only a device identifier and radio wave strength.

[0057] 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 value, an average value, a maximum value, or a minimum value.

[0058] The storage unit 11 is preferably a non-volatile recording medium, but may also be realized as a volatile recording medium.

[0059] 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 inputted via an input device may be stored in the storage unit 11.

[0060] The reception unit 12 and the position reception 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.

[0061] The receiver 13 is usually realized by a wireless or wired communication means.

[0062] The processing unit 14, the intensity acquisition unit 141, the type determination unit 142, and the storage unit 143 can usually be realized by a processor, a memory, or the like. The processing procedure of the processing unit 14, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, or the like, and the type is not important.

[0063] Next, an example of the operation of the point information production device 1 will be described with reference to the flowchart of FIG.

[0064] (Step S301) The position receiving unit 121 judges whether or not the position information of a specific point has been received. If the position information has been received, the process proceeds to step S302, and if not, the process returns to step S301.

[0065] (Step S302) The accumulation unit 143 acquires the location information accepted in step S301.

[0066] (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 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.

[0067] (Step S304) The processing unit 14 and the like perform a fixed information acquisition process. Return to step S301. The fixed information acquisition process is a process for acquiring the strength of radio waves from a fixed terminal at a point specified by the location information accepted in step S301. An example of the fixed information acquisition process will be described with reference to the flowchart of FIG. 5.

[0068] In the flowchart of Figure 3, it is preferable that a user holding the local location information production device 1 moves to each of the three or more specific locations, and the local location information production device 1 receives location information for each of the three or more specific locations, and repeatedly performs the processes from S301 to S304.

[0069] In the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt occurs to end the process.

[0070] Next, an example of the time-series intensity acquisition process in step S303 will be described with reference to the flowchart in FIG.

[0071] (Step S401) The receiving unit 13 judges whether or not a radio wave has been received from any of the communication devices B. If a radio wave has been received, the process proceeds to step S402, and if not, the process returns to step S401.

[0072] (Step S402) The storage unit 143 acquires a device identifier corresponding to the radio wave received in step S401.

[0073] (Step S403) The intensity acquisition unit 141 acquires the intensity of the radio wave received in step S401.

[0074] (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.

[0075] (Step S405) The accumulation unit 143 judges whether or not the accumulation condition of the location information is met. If the accumulation condition is met, the process returns to the upper process, and if not, the process returns to step S401. The accumulation condition is, for example, that a threshold time or more has elapsed since the location information of the specific location was accepted, that a threshold or more number of radio wave intensities paired with each of three or more device identifiers have been accumulated, etc.

[0076] Next, an example of the fixed information acquisition process in step S304 will be described with reference to the flowchart in FIG.

[0077] (Step S501) The type determination unit 142 assigns 1 to a counter i.

[0078] (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 process.

[0079] (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.

[0080] (Step S504) If the result of the determination in step S503 is a "fixed terminal", the process proceeds to step S505; if the result is a "mobile terminal", the process proceeds to step S508.

[0081] (Step S505) The accumulation unit 143 acquires two or more pieces of radio field strength paired with the i-th device identifier from a buffer (not shown).

[0082] (Step S506) The accumulation unit 143 acquires two or more representative values ​​of radio field strength.

[0083] (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 field intensity acquired in step S506 in association with the location information accepted in step S301.

[0084] (Step S508) The type determination unit 142 increments the counter i by 1. The process returns to step S502.

[0085] In the flowchart of FIG. 5, in step S506, the accumulation unit 143 may acquire a new, latest radio wave strength instead of the representative value of two or more radio wave strengths.

[0086] Next, an example of the type determination process in step S503 will be described with reference to the flowchart in FIG.

[0087] (Step S601) The type determination unit 142 acquires two or more pieces of radio field strength that are paired with the i-th device identifier in step S502 from a buffer (not shown).

[0088] (Step S602) The type determination unit 142 acquires the degree of variation in the two or more radio wave intensities acquired in step S601.

[0089] (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 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, and if the degree of variation is equal to or more than the threshold value or greater than the threshold value, the process proceeds to step S605.

[0090] (Step S604) The type determination unit 142 determines the type of the communication device B as "fixed terminal." Then, the process returns to the upper level process.

[0091] (Step S605) The type determination unit 142 determines the type of the communication device B as "mobile terminal." Then, the process returns to the upper level process.

[0092] A specific operation example of the point information production device 1 in this embodiment will be described below. Here, the accumulation condition is that a predetermined time (for example, 3 minutes) has passed since the position information of a specific point was accepted.

[0093] Assume that user A is, for example, indoors (for example, user A's home or a department store that user A often visits) and has input location information (x1, y1) to the location information production device 1.

[0094] Next, the position receiving unit 121 of the point information production device 1 receives the position information (x1, y1) of the specific point. Next, the storage unit 143 acquires the received position information (x1, y1) in a buffer (not shown).

[0095] Then, the receiving unit 13 receives radio waves including the device identifier from each of the three or more communication devices B for a predetermined time (e.g., three minutes). Then, the storage unit 143 acquires the device identifier contained in the received radio waves. Also, the intensity acquisition unit 141 acquires the intensity of the received radio waves. Next, the storage unit 143 adds the acquired radio wave intensity to a buffer (not shown) in association with the acquired device identifier. 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 is a table for a specific location indicated by the position information (x1, y1).

[0096] 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 that have an "ID", "device identifier", and "time-series radio wave strength". "ID" is information that identifies a record. "Time-series radio wave strength" is the radio wave strength that is continuous over time. "R 11 " "R 12 " "R 21 " etc. indicates radio wave strength.

[0097] After the management table of FIG. 7 is constructed, a predetermined time (e.g., 3 minutes) has elapsed since the location information (x1, y1) of a specific location was accepted by the location accepting unit 121, and therefore the storage unit 143 determines that the storage conditions for the location information are met.

[0098] Next, the type determination unit 142 acquires the degree of variation in the 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, it is assumed that the type determination unit 142 determines that the communication device B identified by the device identifier "device 1, device 3, device 4, device 6, ..." is a fixed terminal, and determines that the communication device B identified by the device identifier "device 2, device 5, ..." is a mobile terminal.

[0099] Next, the storage unit 143 configures radio wave strength information by pairing the device identifier of communication device B, which is a fixed terminal, with a representative value of radio wave strength. Then, the storage unit 143 stores each of the multiple pieces of radio wave strength information in association with the location information (x1, y1). Through this process, a record of "ID=1" in the location information management table of FIG. 8 is configured. Note that the storage unit 143 may store only the device identifier and radio wave strength. In such a case, each record in FIG. 8 does not have location information. Also, in such a case, the location receiving unit 121 does not need to receive location information of a specific location.

[0100] 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 "ID", "device identifier", and "signal strength information". "Signal strength information" has "device identifier" and "signal strength".

[0101] Through the above process, the location information of the specific location 1 at the position indicated by the location information (x1, y1) is accumulated.

[0102] User A, taking the point information production device 1, moves to specific point 2, the position indicated by the position information (x2, y2), and does the same as above. As a result, the point information production device 1 configures and stores a record for "ID=2" in the point information management table of FIG. 8. Furthermore, user A, taking the point information production device 1, moves to one or more specific points including specific point 3, and does the same as above. As a result, the point information production device 1 configures and stores a record (not shown) for "ID=3" in the point information management table of FIG. 8.

[0103] As described above, according to this embodiment, it is possible to obtain point information for obtaining the indoor position of a terminal device. In other words, according to this embodiment, it is possible to produce three or more pieces of point information for obtaining the indoor position of a terminal device.

[0104] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. 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 for realizing the location information production device 1 in this embodiment is a program as follows. 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 including a device identifier that identifies the communication device from each of three or more communication devices at the specific location, 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 that is a fixed communication device or a mobile terminal that is a moving communication device, and a storage unit that configures and stores location information having the device identifier and radio wave intensity 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.

[0105] (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."

[0106] 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.

[0107] 9 is a conceptual diagram of an information system C according to this embodiment. The information system C includes one or more terminal devices 2 and three or more communication devices B.

[0108] 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 smart watch, a so-called personal computer, or the like, and the type of the terminal device 2 is not important.

[0109] 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 an intensity acquisition means 2341, a point determination means 2342, and a position acquisition means 2343. The output unit 24 includes a position output unit 241.

[0110] Various types of information are stored in the storage unit 21. The various types of information are, for example, location information, which will be described later.

[0111] 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.

[0112] 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 of FIG. 8.

[0113] It should be noted that the terminal device 2 does not need to have the point information storage unit 211. In such a case, the terminal device 2 refers to the point information storage unit 211 of an external device (not shown) to acquire the terminal position, which will be described later.

[0114] The receiving unit 22 receives radio waves including a device identifier for identifying the communication device B from each of the three or more communication devices B. The receiving unit 22 normally performs the same function as the receiving unit 13 described above. The receiving unit 22 normally receives radio waves including a device identifier from each of the three or more communication devices B. The receiving unit 22 normally receives radio waves continuously.

[0115] The processing unit 23 performs various types of processing. The various types of processing are, for example, processing performed by an intensity acquisition unit 231, a type determination unit 232, a movement determination unit 233, and a position acquisition unit 234.

[0116] The intensity acquisition unit 231 acquires radio wave intensity in time series 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.

[0117] 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.

[0118] The movement determination unit 233 determines whether the terminal device 2 is moving or stopped, and acquires a movement determination result that is a result of the determination. The movement determination result is, for example, "moving" or "stopped".

[0119] 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 by a gyro and time-series position information.

[0120] For example, if the acceleration by the gyro is "0" or less than a threshold, the movement determination unit 233 obtains a movement determination result of "stopped". For example, if the acceleration by the gyro is equal to or greater than a threshold, the movement determination unit 233 obtains a movement determination result of "moving".

[0121] 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 are stopped if there is no change in the time-series radio wave strength of the communication devices B, and acquires a movement determination result of “stopped.”

[0122] The location acquisition unit 234 acquires radio wave intensities of three or more communication devices B that the type determination unit 232 has determined to be fixed terminals, and acquires the terminal location, which is location information of the terminal device 2 indoors, using the radio wave intensities of the fixed terminals.

[0123] The location acquisition unit 234, for example, acquires radio wave intensities 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 by a fingerprint method.

[0124] 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".

[0125] 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 wave 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.

[0126] The strength acquisition means 2341 acquires the radio field strengths of three or more communication devices that the type determination unit 232 has determined to be fixed terminals, in association with the device identifiers.

[0127] The location determination means 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 corresponding to each of the three or more device identifiers acquired by the intensity acquisition means 2341 and the similarity condition.

[0128] The point determination means 2342, for example, acquires a first radio wave intensity vector which is a vector whose elements are radio wave intensities paired with each of 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 radio wave intensities corresponding to each of three or more device identifiers acquired by the intensity acquisition means 2341. The point determination means 2342, for example, acquires a similarity between the two radio wave intensity vectors, and acquires point information corresponding to the first radio wave intensity vector when the similarity is equal to or greater than a threshold value.

[0129] The position acquiring means 2343 acquires position information contained in the one or more pieces of point information determined by the point determining means 2342, and acquires the terminal position using the one or more pieces of position information.

[0130] The output unit 24 outputs various information, such as the terminal position and an indoor map.

[0131] Here, output is a concept that includes display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage on a recording medium, and handing over the processing results to other processing devices or other programs, etc.

[0132] 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 clearly indicating the position identified by the terminal position.

[0133] 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.

[0134] 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 inputted via an input device may be stored in the storage unit 21, etc.

[0135] The receiving unit 22 is usually realized by a wireless or wired communication means.

[0136] The processing unit 23, the intensity acquisition unit 231, the type determination unit 232, the movement determination unit 233, the position acquisition unit 234, the intensity acquisition means 2341, the location determination means 2342, and the position acquisition means 2343 can usually be realized by a processor, a memory, or the like. The processing procedure of the processing unit 23, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, or the like, and the type is not important.

[0137] The output unit 24 and the position output unit 241 can be realized, for example, by driver software for an output device such as a display or a speaker, or by driver software for an output device and an output device, or the like.

[0138] Next, a first operation example of the terminal device 2 will be described with reference to the flowchart of FIG.

[0139] (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.

[0140] (Step S1102) If the determination result in step S1101 is "stopped", the process proceeds to step S1103, and if the determination result is "moving", the process returns to step S1101.

[0141] (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 of FIG.

[0142] (Step S1104) The type determination unit 232, the location determination means 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.

[0143] (Step S1105) The position acquisition unit 2343 performs a position estimation process to acquire the terminal position. An example of the position estimation process will be described with reference to the flowchart of FIG.

[0144] (Step S1106) The position output unit 241 outputs the terminal position acquired in step S1105. Then, the process returns to step S1101.

[0145] In the flowchart of FIG. 11, the process ends when the power is turned off or an interrupt occurs to end the process.

[0146] Next, an example of the movement determination process in step S1101 will be described with reference to the flowchart in FIG.

[0147] (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).

[0148] (Step S1202) The movement determination unit 233 uses the sensor values ​​in a buffer (not shown) to determine whether or not to perform a movement determination. 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 acquisition of the sensor values.

[0149] (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.

[0150] (Step S1204) The movement determining unit 233 determines the movement determination result as "stopped." Then, the process proceeds to step S1206.

[0151] (Step S1205) The movement determining unit 233 determines the movement determination result to be "moving".

[0152] (Step S1206) The movement determination unit 233 clears a buffer (not shown) and returns to the upper level process.

[0153] Next, an example of the position estimation process in step S1105 will be described with reference to the flowchart in FIG.

[0154] (Step S1301) The position acquisition means 2343 acquires three or more pieces of radio wave intensity information (pairs of device identifier and radio wave intensity) of the terminal device 2.

[0155] (Step S1302) The position acquisition means 2343 vectorizes three or more pieces of radio wave intensity information to acquire a radio wave intensity vector. Note that 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).

[0156] (Step S1303) The position obtaining means 2343 assigns 1 to a counter i.

[0157] (Step S1304) The position obtaining means 2343 judges 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, and if not, the process proceeds to step S1309.

[0158] (Step S1305) The position obtaining unit 2343 obtains, from the point information storage unit 211, the i-th radio wave intensity vector contained in the i-th point information.

[0159] (Step S1306) The position acquisition means 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 means 2343 judges whether the similarity satisfies a similarity condition (for example, the similarity is equal to or greater than a threshold). If the similarity satisfies the similarity condition, the process proceeds to step S1307, and if not, the process proceeds to step S1308.

[0160] (Step S1307) The position obtaining unit 2343 obtains the position information and the similarity included in the i-th spot information, and accumulates them in a buffer (not shown).

[0161] (Step S1308) The position obtaining unit 2343 increments the counter i by 1. The process returns to step S1304.

[0162] (Step S1309) The position obtaining means 2343 obtains a terminal position that specifies the position of the terminal device 2, using three or more sets of position information and similarity accumulated in a buffer (not shown). The process returns to the upper level process.

[0163] Next, a second operation example of the terminal device 2 will be described with reference to the flowchart of FIG.

[0164] (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 of FIG.

[0165] (Step S1402) The location determining unit 2342 performs fixed information acquisition processing. An example of the fixed information acquisition processing has been described with reference to the flowchart of FIG.

[0166] (Step S1403) The position acquisition unit 2343 performs a position estimation process to acquire the terminal position. An example of the position estimation process has been described with reference to the flowchart of FIG.

[0167] (Step S1404) The position output unit 241 outputs the terminal position acquired in step S1403. Then, the process returns to step S1401.

[0168] In the flowchart of FIG. 14, the process ends when the power is turned off or an interrupt occurs to end the process.

[0169] 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 with reference to the flowchart of Fig. 15. Note that the first example of the type determination process has been described with reference to the flowchart of Fig. 6.

[0170] (Step S1501) The type determination unit 232 acquires the device identifier of the communication device B whose type is to be determined.

[0171] (Step S1502) The type determination unit 232 determines whether or not the device identifier acquired in step S1501 exists in any of the point information stored in the point information storage unit 211. If the device identifier exists in any of the point information, the process proceeds to step S1503; if the device identifier does not exist, the process proceeds to step S1504.

[0172] (Step S1503) The type determination unit 232 determines the type to be "fixed terminal."

[0173] (Step S1504) The type determination unit 232 determines the type as "mobile terminal."

[0174] A specific example of the operation of the terminal device 2 in this embodiment will be described below.

[0175] It is assumed that user B, holding his / her own terminal device 2, enters an indoor location identified by a location identifier (P). Then, the receiving unit 22 of the terminal device 2 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 device. Then, it is assumed that the processing unit 23 temporarily stores the location information management table in the location information storage unit 211.

[0176] 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.

[0177] That is, the strength acquisition means 2341 performs the time-series strength acquisition process described using the flowchart of FIG. 4, acquires the radio wave strength of each of three or more communication devices B at the point X where the terminal device 2 is located, and configures a time-series radio wave strength management table having the structure shown in FIG. 7.

[0178] 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 configures a time-series radio wave strength management table with the structure shown in FIG.

[0179] 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 in FIG.

[0180] Next, the location determination means 2342 acquires the radio wave strength of "device 1," "device 3," "device 4," "device 6," etc., which are determined to be fixed terminals. Then, the location determination means 2342 acquires a 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,...)." Here, the radio wave strength of communication device B acquired by the location determination means 2342 may be a representative value of two or more radio wave strengths, or may be a single radio wave strength such as the latest radio wave strength of communication device B.

[0181] Next, the position acquisition means 2343 performs the position 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 composed of radio wave intensity information) of each record in FIG. 8. Next, the position acquisition means 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". 11 ,S 12 ,S 13 , ), and the signal strength vector of “ID=2” (S 21 ,S 22 ,S 23 ,···),···). Next, the position acquisition means 2343 acquires a pair of position information and similarity (for example, "(x1,y1),DS1", "(x2,y2),DS2"···) that is paired with the radio wave intensity vector that satisfies the similarity condition. Next, the position acquisition means 2343 acquires the indoor position of point X (x1×DS1 / sum of similarities+x2×DS2 / sum of similarities+···, y1×DS1 / sum of similarities+y2×DS2 / sum of similarities+···). Note that the sum of similarities is "DS1+DS2+···".

[0182] 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 a position indicated by the position information (terminal position) acquired by the position acquisition means 2343.

[0183] As described above, according to this embodiment, the position of the terminal device 2 indoors or outdoors can be easily acquired.

[0184] The process in this embodiment may be realized by software. This software may be distributed by software download or the like. 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 for realizing the terminal device 2 in this embodiment is a program as follows. That is, this program causes a computer to function as a receiver unit that receives radio waves including a device identifier that identifies each of three or more communication devices from each of the 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, 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, a position acquisition unit that acquires a terminal position, which is position information of the terminal device, using the radio wave intensity of the three or more communication devices that the type determination unit has determined to be a fixed terminal, and a position output unit that outputs the terminal position acquired by the position acquisition unit.

[0185] (Embodiment 3) In this embodiment, a behavior acquisition device is described that acquires a user's behavior information and a time period using the user's location information corresponding to the time, and outputs the behavior information corresponding to the time period. It is preferable that the behavior acquisition device further acquires the user's behavior information and a time period using the user's activity data and vital data. The location information used when acquiring the behavior information may be indoor location information acquired by the terminal device 2 described in the second embodiment.

[0186] In the present 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.

[0187] In the present embodiment, a behavior acquisition device that also acquires and outputs emotion information of a user will be described. In the present embodiment, it is preferable to acquire emotion information using past record information.

[0188] In this embodiment, the behavior acquisition device is a terminal. However, as described in embodiment 4, the behavior acquisition device may be a server. In other words, the processes of acquiring behavior information and acquiring emotion information, which will be described later, may be performed by the user's terminal or the server.

[0189] 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.

[0190] Further, in this embodiment, a behavior acquisition device that displays estimated behavior information and confirmed behavior information in a visually distinct manner will be described.

[0191] 16 is a conceptual diagram of an information system D in 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.

[0192] 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 is, for example, a smartphone, a tablet terminal, a smart watch, a so-called personal computer, or the like, and the type is not important.

[0193] The server device 4 is, for example, a device that stores sets of behavior source information and behavior information of two or more users, each set for each time period. The server device 4 is, for example, a device that stores learning information, which will be 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 limited.

[0194] Fig. 17 is a block diagram of an information system D in this embodiment. Fig. 18 is a block diagram of a behavior acquisition device 3.

[0195] 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. 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 vitals 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.

[0196] 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.

[0197] The behavior acquisition device 3 accepts, for example, an output instruction, a confirmation instruction, and information input. An output instruction is an instruction to output output information, which will be described later. An output instruction usually has 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 emotion information. When the estimated behavioral information or emotion information is incorrect, the information input is information for changing the behavioral information or emotion information. The information input is updated behavioral information or updated emotion information.

[0198] Various types of information are stored in the storage unit 31 constituting the behavior acquisition device 3. The various types of information are, for example, learning information (to be described later), a map (to be described later), behavior information (to be described later), location information, a calendar template, behavior information corresponding to two or more behavior conditions, and emotion information corresponding to two or more emotion conditions.

[0199] The calendar template is information that indicates 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.

[0200] 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 corresponds to action information. An example of an action condition is "location information = office AND 8:00 <= time <= 19:00", and the action information corresponding to this action condition is "work". An example of an action condition is "location information = kitchen AND activity data = standing AND 7:00 <= time <= 8:00", and the action information corresponding to this action condition is "cooking". An example of an action condition is "location information = park AND activity data = standing AND 120 <= heart rate", and the action information corresponding to this action condition is "running".

[0201] An emotion condition is a condition for acquiring emotion information. An emotion condition is a condition using two or more pieces of emotion source information. An emotion condition is associated with emotion information. An emotion condition is, for example, "location information = office AND 8:00 <= time <= 19:00", and emotion information associated with this behavioral condition is "positive". An emotion condition is, for example, "location information = kitchen AND activity data = standing AND 7:00 <= time <= 8:00", and emotion information associated with this behavioral condition is "positive". An activity condition is, for example, "location information = park AND activity data = standing AND 120 <= heart rate", and emotion information associated with this behavioral condition is "negative".

[0202] Learning information is stored in the learning management unit 311. 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 emotion 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.

[0203] The user attribute value is a user's attribute value, such as occupation, family structure, single or married, gender, age, age group, morning or night type, and residential area.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] The behavioral information is information that identifies the user's behavior, such as "work," "watching TV," "walking," "running," "gym," "bath," and "sleep."

[0208] Emotion information is information about the user's emotions. Emotion information is, for example, positive (e.g., "1") or negative (e.g., "0"). Emotion information is, for example, a degree of positivity or negativity. Emotion information is, for example, joy (e.g., "1"), anger (e.g., "2"), sadness (e.g., "3"), and happiness (e.g., "4").

[0209] The action source information is information that is the source for acquiring the action information. The action source information includes location information. It is preferable that the action source information includes activity data or one or more types of vital data. The action source information may include emotion information. The action source information may include one or more past action information. Past action information usually includes the immediately preceding action information. The action source information may include one or more future schedule information of the user. The schedule information is, for example, information stored in a calendar server (e.g., a "Google Calendar (registered trademark) server") not shown. The action source information may include the elapsed time since the user arrived at the same location information. The action source information may include one or more user attribute values.

[0210] The position information is information that specifies the position of the behavior acquisition device 3. The position 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 place information. The place information is information that expresses the meaning of a place. The place information is, for example, indoor place information or outdoor place information. The indoor place information is information that specifies an indoor place. The indoor place information is, for example, "living room", "kitchen", "workroom", or "office". The outdoor place information is information that specifies an outdoor place. The outdoor place information is, for example, "ABC Station", "library", "izakaya", or "point A".

[0211] The physical data is information related to the user's body, such as activity data and vital data.

[0212] The activity data is information that identifies the user's activity. For example, the activity data is "standing" or "on the ground (e.g., sitting)."

[0213] Vital data is information that can be obtained from the user's body. Vital data may be called biological information. Vital data is, for example, heart rate, heart rate variability, blood pressure (upper and / or lower), respiratory rate, and body temperature in a unit time (for example, 1 minute or 30 seconds). Learning information is, for example, a learning model or a correspondence table. A learning model is information configured by a learning process of machine learning using two or more teacher data, and is information used in a prediction process of machine learning. A learning model may be called a learner, a classifier, a classification model, or the like. The machine learning algorithm may be deep learning, random forest, decision tree, SVM, or the like. In addition, for machine learning, for example, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, and TinySVM, or various existing libraries may be used. In addition, when the learning information is a learning model, one or two or more behavior source information of the teacher data is an explanatory variable, and the behavior information is a target variable.

[0214] 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 by a machine learning learning process using behavioral teacher data. The behavioral teacher data has one or more behavioral source information and behavioral information.

[0215] The emotion learning model is a learning model for acquiring emotion information, and is information acquired by machine learning learning processing using emotion teacher data. The emotion teacher data has one or more emotion source information and emotion information.

[0216] The correspondence table is an action correspondence table or an emotion correspondence table. The action correspondence table is a table for acquiring action information. The action correspondence table has two or more action correspondence information. The action correspondence information is information showing the correspondence between one or more action source information and action information. The one or more action source information has, for example, a vector structure. Such a vector is called an action source vector. The action source vector is a vector having one or more action source information as elements. The emotion correspondence table has two or more emotion correspondence information. The emotion correspondence information is information showing the correspondence between one or more emotion source information and emotion information. The one or more emotion source information has, for example, a vector structure. Such a vector is called an emotion source vector. The emotion source vector is a vector having one or more emotion source information as elements.

[0217] A map is stored in the map management unit 312. The map has location information corresponding to one or more pieces of position information. The map is in, for example, KIWI format, but the structure is not important.

[0218] The behavior management unit 313 stores behavior information corresponding to each of 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.

[0219] It is preferable that the behavior information here is capable of being determined whether it is confirmed behavior information or not. For example, a confirmation flag is associated with the behavior information. The confirmation flag is a flag indicating that the behavior information is confirmed. Being capable of being determined whether it is confirmed behavior information or not means, for example, that the storage area for confirmed behavior information and the storage area for unconfirmed behavior information are different. Any other method for making it possible to determine whether the behavior information is confirmed or not is not required.

[0220] The receiving unit 32 receives radio waves including a device identifier for identifying the communication device B from one or more communication devices B. The receiving unit 32 normally receives radio waves from three or more communication devices B. The receiving unit 32 has the same functions as the receiving unit 22.

[0221] The processing unit 33 performs various types of processing. The various types of processing are, for example, processing performed by the intensity acquisition unit 231, the type determination unit 232, the time acquisition unit 331, and the like.

[0222] The time acquisition unit 331 acquires the time. For example, the time acquisition unit 331 acquires the time from a clock not shown. For example, the time acquisition unit 331 receives the time from the server device 4 or a device not shown. The time may be hours, minutes, and seconds, or may be hours and minutes. The time may include one or more pieces of information from "year", "month", and "day". The time acquisition unit 331 may acquire the day of the week. The day of the week may be considered to be information included in the acquired time.

[0223] 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 process as the position acquisition unit 234. In particular, when the behavior acquisition device 3 is located indoors, etc., and a GPS signal cannot be received, it is preferable that the position acquisition unit 332 performs the same process as the position acquisition unit 234. It is preferable that the position acquisition unit 332 has a GPS receiver. The position acquisition unit 332 acquires position information by, for example, the 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.

[0224] 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.

[0225] 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 in association with the time acquired by the time acquisition unit 331. The process of acquiring the activity data is a known technique.

[0226] 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 a time acquired by the time acquisition unit 331. The process of acquiring vital sign data is a known technique.

[0227] 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 in a time period that is identified by the time of each of the two or more pieces of behavior source information. It is preferable that the behavior source information also includes activity data. It is also preferable that the behavior source information also includes one or two or more types of vital data.

[0228] The behavior inferrer 335 detects a behavior condition that matches two or more pieces of behavior source information including, for example, position information associated with a time, and obtains, from the storage unit 31, behavior information that pairs with the behavior condition.

[0229] For example, when the user is sitting at a desk indoors at home at 13:15, the behavior estimation unit 335 acquires behavior information "work." When the user is sitting indoors in the living room at home at 20:17, the behavior estimation unit 335 acquires behavior information "watching TV." When the user acquires location information "izakaya" at 20:17, the behavior estimation unit 335 acquires behavior information "drinking party."

[0230] The behavior estimation unit 335 may acquire behavior information for a time period by using the behavior learning information of the learning management unit 311 and two or more pieces of behavior source information including location information corresponding to time. Hereinafter, the processing of the behavior estimation unit 335 in the case where the learning information is a learning model and in the case where the learning information is a correspondence table will be described.

[0231] The behavior estimation unit 335 may acquire one or more user attribute values, acquire from the learning management unit 311 behavioral learning information that pairs with a user attribute value condition that matches the one or more user attribute values, and acquire behavioral information using the behavioral learning information. (1) When the behavioral learning information is a behavioral learning model

[0232] The behavior estimation unit 335 acquires the learning model of the learning management unit 311. The behavior estimation unit 335 also acquires a time and one or more pieces of behavior source information corresponding to 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.

[0233] 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 behavior information. (2) When the behavioral learning information is a behavior correspondence table

[0234] The behavior estimation unit 335 acquires a time and behavior source information corresponding to the time. Next, the behavior estimation unit 335 acquires a behavior source vector having the time and the behavior source information as elements. Next, the behavior estimation unit 335 calculates the similarity between the behavior source vector and the behavior source vector included in each of two or more behavior correspondence information included in the behavior correspondence table. Next, the behavior estimation unit 335 acquires behavior information paired with the behavior source vector having the maximum similarity from the behavior correspondence table. Note that the behavior estimation unit 335 does not need to acquire behavior information even if the similarity is maximum, if the similarity is equal to or less than a threshold value or is less than a threshold value.

[0235] The feeling estimation unit 336 acquires feeling information related to the user's feeling in a time period by using the behavior information or the behavior source information.

[0236] The emotion estimation unit 336 detects an emotion condition that matches two or more pieces of emotion source information including, for example, location information associated with a time, and acquires emotion information that pairs with the emotion condition from the storage unit 31.

[0237] For example, when sitting at a desk indoors at home at 1:15 p.m., the emotion deduction unit 336 acquires emotion information "positive." When sitting indoors in the living room of the home at 8:17 p.m., the emotion deduction unit 336 acquires emotion information "positive." When acquiring location information "izakaya" at 8:17 p.m., the emotion deduction unit 336 acquires emotion information "positive."

[0238] The emotion estimation unit 336 acquires emotion information for a time period using the emotion learning information of the learning management unit 311 and the behavior information acquired by the behavior estimation unit 335 or one or more pieces of behavior source information that are the basis for acquiring the behavior information. Note that the behavior information or one or more pieces of behavior source information are referred to as emotion source information here because they are used to acquire emotion information.

[0239] The emotion deduction unit 336 may acquire one or more user attribute values, acquire emotion learning information that pairs with a user attribute value condition that matches the one or more user attribute values ​​from the learning management unit 311, and acquire emotion information using the emotion learning information.

[0240] The emotion estimation unit 336 will now explain the processing of the behavior estimation unit 335 in the cases where the emotion learning information is an emotion learning model and where the emotion correspondence table is used. (1) When emotion learning information is an emotion learning model

[0241] The emotion deduction unit 336 acquires an emotion learning model from the learning management unit 311. The emotion deduction unit 336 also acquires one or more pieces of action source information or action information corresponding to the time. Next, the emotion deduction unit 336 provides the acquired one or more pieces of action source information or action information and the emotion learning model to a module that performs machine learning prediction processing, executes the module, and acquires emotion information.

[0242] Note that, 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 emotion learning information is an emotion correspondence table

[0243] The emotion deduction unit 336 acquires one or more pieces of action source information or action information corresponding to the time. Next, the emotion deduction unit 336 acquires an emotion source vector having the one or more pieces of action source information or action information as elements. Next, the emotion deduction unit 336 calculates the similarity between the emotion source vector and the emotion source vector of each of the two or more pieces of correspondence information in the emotion correspondence table. Next, the emotion deduction unit 336 acquires emotion information paired with the emotion source vector having the maximum similarity from the emotion correspondence table. Note that the emotion deduction unit 336 does not need to acquire emotion information when the similarity is equal to or less than a threshold value even if the similarity is maximum.

[0244] 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 (for example, (latitude, longitude)).

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] The configuration unit 339 configures 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, for example. It is preferable that the two or more pieces of behavioral information are arranged in the output information so that the confirmed behavioral information and the unconfirmed behavioral information are visually distinguishable. It is preferable that the configuration unit 339 configures output information visually indicating emotion information corresponding to each of two or more time periods. It is preferable that the configuration unit 339 configures output information in which, for example, a time period in which emotion information is "positive" and a time period in which emotion information is "negative" are visually distinguishable. It is preferable that the configuration unit 339 configures output information so that, for example, the background color of the time period in which emotion information is "positive" is different from the background color of the time period in which emotion information is "negative".

[0250] The output unit 34 outputs various types of information, such as behavior information, emotion information, and location information.

[0251] 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.

[0252] The behavior output unit 341 outputs behavior information for each of one or more time periods.

[0253] 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.

[0254] It is preferable that the behavior output unit 341 outputs two or more pieces of behavior information so that confirmed behavior information and unconfirmed behavior information can be visually distinguished.

[0255] 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.

[0256] Various types of information are stored in the server storage unit 41 constituting the server device 4. The various types of information are, for example, the above-mentioned learning information, behavior source information and time corresponding to each of two or more user identifiers, two or more behavior teacher data, and two or more emotion teacher data.

[0257] The server receiving unit 42 receives various instructions and information. The various instructions and information are, for example, instructions to transmit information. The information here is, for example, learning information and behavior source information.

[0258] The server processing unit 43 performs various types of processing. The various types of processing are, for example, learning processing. The learning processing are, for example, behavior learning processing and emotion learning processing.

[0259] The behavior learning process is a process of acquiring a behavior learning model using two or more behavior teacher data. For example, the server processing unit 43 provides two or more behavior teacher data to a machine learning learning processing module, executes the module, acquires a behavior learning model, and accumulates 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 behavior information, and two or more negative examples, which are teacher data not including behavior information, to the machine learning learning processing module for each of two or more candidates of behavior information, executes the module, acquires a behavior learning model for each candidate of behavior information, and accumulates it in the server storage unit 41 in association with the behavior information. For example, the server processing unit 43 acquires a behavior correspondence table, which is a table in which each of two or more behavior teacher data is a record, and accumulates it in the server storage unit 41.

[0260] The emotion learning process is a process of acquiring an emotion learning model using two or more emotion teacher data. The server processing unit 43, for example, provides two or more 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. For example, the server processing unit 43 provides two or more positive examples, which are teacher data including emotion information, and two or more negative examples, which 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 emotion teacher data is a record, and stores it in the server storage unit 41.

[0261] The server transmission 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.

[0262] 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.

[0263] 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 inputted via an input device may be stored in the storage unit 31, etc.

[0264] The receiving unit 32, the server receiving unit 42, and the server transmitting unit 44 are typically realized by wireless or wired communication means.

[0265] The processing unit 33, the time acquisition unit 331, the position acquisition unit 332, the activity acquisition unit 333, the vitals acquisition unit 334, the behavior estimation unit 335, the emotion estimation unit 336, the location acquisition unit 337, the accumulation unit 338, the configuration unit 339, and the server processing unit 43 can usually be realized by a processor, a memory, or the like. The processing procedure of the processing unit 33, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor is a CPU, an MPU, a GPU, etc., and the type does not matter.

[0266] 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 a combination of driver software for an output device and an output device, etc.

[0267] 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.

[0268] (Step S1901) Processing unit 33 judges 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. Note that processing unit 33 may always judge to acquire information, or may judge to acquire information when a flag indicating that information is to be acquired is stored in storage unit 31. There are no restrictions on the conditions for such judgment.

[0269] (Step S1902) The time acquiring unit 331 acquires the time from a clock (not shown). Here, the time acquiring unit 331 may acquire the day of the week.

[0270] (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.

[0271] (Step S1904) The behavior estimation unit 335 estimates behavior information for identifying 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. 22 to 24.

[0272] (Step S1905) The location acquiring unit 337 judges whether or not the behavior information has been acquired in step S1904. If the behavior information has been acquired, the process proceeds to step S1907, and if the behavior information has not been acquired, the process proceeds to step S1906.

[0273] (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.

[0274] (Step S1907) The storage unit 338 determines whether the behavior information stored in a buffer (not shown) most recently stored 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.

[0275] (Step S1908) The accumulation unit 338 associates the acquired behavior information, etc. with the time acquired in step S1902, and accumulates them in a buffer (not shown).

[0276] (Step S1909) The feeling estimation unit 336 performs a process of estimating feeling information. An example of such feeling estimation process will be described with reference to the flowcharts of Figs. 25 to 27.

[0277] (Step S1910) Accumulation section 338 decides whether or not emotion information was acquired in step S1909. If emotion information was acquired, processing proceeds to step S1911, and if emotion information was not acquired, processing returns to step S1901.

[0278] (Step S1911) Accumulation section 338 accumulates the emotion information acquired in step S1909 in a buffer (not shown) in association with the time acquired in step S1902. Processing returns to step S1901.

[0279] (Step S1912) The accumulation unit 338 accumulates the time acquired in step S1902 and the acquired behavioral information and the like in a buffer (not shown) in association with each other.

[0280] (Step S1913) The accumulation unit 338 acquires the immediately preceding behavior information and the like.

[0281] (Step S1914) The storage unit 338 acquires a time period specified by two or more times corresponding to the immediately preceding behavioral information or the like.

[0282] (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., with a user identifier, and store them in association with each other. In this case, it is preferable that the storage destination is the server device 4.

[0283] (Step S1916) The behavior acquisition device 3 judges 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.

[0284] (Step S1917) The configuration unit 339 configures output information using the behavior information and the like of the behavior management unit 313. An example of such output configuration processing will be described with reference to the flowchart of FIG.

[0285] (Step S1918) The output unit 34 outputs the output information constructed in step S1917. The process returns to step S1901.

[0286] (Step S1919) The behavior acquisition device 3 judges whether or not information input has been accepted for the output information being output. If information input has been accepted, the process proceeds to step S1920, and if not, the process proceeds to step S1923.

[0287] (Step S1920) Processing unit 33 determines whether the information received in step S1919 is a confirmation instruction for estimated behavior information or estimated emotion information. If it is a confirmation instruction, the process proceeds to step S1921, and if it is not a confirmation instruction, the process proceeds to step S1922.

[0288] (Step S1921) The accumulation unit 338 performs processing for confirming the behavioral information or emotion information corresponding to the confirmation instruction. Return to step S1901. Note that this processing is, for example, processing for associating a confirmation flag with the behavioral information corresponding to the confirmation instruction or the emotion information corresponding to the confirmation instruction.

[0289] (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 emotion information. Then, the storage unit 338 updates the estimated behavioral information or emotion information corresponding to the input information to the input behavioral information or emotion information. Furthermore, the storage unit 338 performs processing for finalizing the behavioral information or emotion information.

[0290] (Step S1923) The behavior acquisition device 3 judges whether or not a learning instruction has been accepted. If a learning instruction has been accepted, the process proceeds to step S1924, and if not, the process returns to step S1901.

[0291] (Step S1924) A learning unit (not shown) or a learning device (not shown) of the behavior acquisition device 3 configures behavior learning information using two or more behavior teacher data including behavior information, etc., and accumulates the information in the learning management unit 311. An example of such behavior learning processing will be described with reference to the flowcharts of Figs. 29 and 30.

[0292] (Step S1925) A learning unit (not shown) or a learning device (not shown) of the behavior acquisition device 3 configures emotion learning information using two or more pieces of emotion teacher data including emotion information, etc., and accumulates the emotion learning information in the learning management unit 311. Return to step S1901. An example of such emotion learning processing will be described with reference to the flowcharts of Figs. 31 and 32.

[0293] In the flowchart of FIG. 19, the process ends when the power is turned off or an interrupt occurs to end the process.

[0294] Next, an example of the action source acquisition process in step S1903 will be described with reference to the flowchart in FIG.

[0295] (Step S2001) The processing unit 33 judges whether or not the receiving unit 32 has acquired a GPS signal. If the GPS signal has been acquired, the process proceeds to step S2002, and if the GPS signal has not been acquired, the process proceeds to step S2003.

[0296] (Step S2002) The position acquisition unit 332 acquires absolute position information based on the GPS signal received by the receiving unit 32.

[0297] (Step S2003) The position acquisition unit 332 acquires position information. An example of such a position estimation process will be described with reference to the flowchart in FIG.

[0298] (Step S2004) The activity acquiring unit 333 acquires activity data of the user.

[0299] (Step S2005) The vital sign acquiring unit 334 acquires one or more types of vital sign data of the user.

[0300] (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 the one in time.

[0301] (Step S2007) Behavior estimation unit 335 judges whether or not to use emotion information for behavior estimation processing. 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 behavior estimation processing is usually determined in advance.

[0302] (Step S2008) The feeling estimation unit 336 acquires feeling information. An example of such feeling estimation processing will be described with reference to the flowcharts of Figs. 25 to 27.

[0303] (Step S2009) The activity inferrer 335 acquires the elapsed time from the start of the new activity identified by the new activity information.

[0304] (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 behavior estimation processing. It returns to the upper level processing. Here, the configured information is usually a set 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 from among time, day of the week, location information, activity data, vital data, past behavior information, emotion information, and elapsed time.

[0305] 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.

[0306] 20, the behavior estimation unit 335 may acquire one or more user attribute values, and may 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 location estimation process in step S2003 will be described with reference to the flowchart in FIG. 21. In the flowchart in FIG. 21, the description of the same steps as those in FIG. 13 will be omitted. Also, the location estimation process in step S2003 may be the same process as that in the flowchart in FIG. 13.

[0307] (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 the similarity in a buffer (not shown). Then, the process proceeds to step S1308.

[0308] (Step S2102) The location acquisition unit 332 acquires location information included in the spot information paired with the maximum similarity. The process returns to the upper level process. The acquired location information is the terminal location.

[0309] 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 by machine learning prediction processing using one behavior learning model. In other words, the flowchart in Fig. 22 illustrates a process for estimating behavior information by machine learning multi-value classification prediction processing.

[0310] (Step S2201) The activity estimation unit 335 acquires two or more types of activity source information (eg, activity source vector) acquired in step S1903.

[0311] (Step S2202) The behavior estimation unit 335 acquires a behavior learning model from the learning management unit 311.

[0312] (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.

[0313] (Step S2204) The behavior estimation unit 335 acquires estimated behavior information and a score, which are the execution results in step S2203.

[0314] (Step S2205) The behavior estimation unit 335 judges whether or not the score acquired in step S2204 is equal to or greater than a threshold value. If it is equal to or greater than the threshold value, the process proceeds to step S2206, and if it is less than the threshold value, the process proceeds to step S2207.

[0315] (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 processing.

[0316] (Step S2207) The behavior inferrer 335 acquires the behavior information of “empty”. Then, the process returns to the upper level process.

[0317] In the flowchart of FIG. 22, the processes from step S2205 to step S2207 do not have to be performed.

[0318] 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 is a process for estimating behavior information by machine learning prediction processing using a behavior learning model for each of two or more behavior information candidates. In other words, the flowchart in Fig. 22 is a process for estimating behavior information by machine learning binary classification prediction processing.

[0319] (Step S2301) The behavior estimation unit 335 acquires two or more types of behavior source information acquired in step S1903.

[0320] (Step S2302) The behavior estimation unit 335 assigns 1 to a counter i.

[0321] (Step S2303) The behavior estimation unit 335 judges 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, and if not, the process proceeds to step S2309.

[0322] (Step S2304) The behavior estimation unit 335 acquires, from the learning management unit 311, the i-th behavior learning model that is paired with the i-th behavior information candidate.

[0323] (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.

[0324] (Step S2306) The behavior estimation unit 335 judges whether or not the execution result in step S2305 is “true.” If it is “true,” the process proceeds to step S2307, and if it is “false,” the process proceeds to step S2308.

[0325] (Step S2307) The behavior inferrer 335 temporarily accumulates the score, which is a part of the execution result in step S2305, in a buffer (not shown) in association with the i-th behavior information candidate.

[0326] (Step S2308) The behavior estimation unit 335 increments the counter i by 1. The process returns to step S2303.

[0327] (Step S2309) The behavior estimation unit 335 acquires the maximum score and judges 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, and if the maximum score is less than the threshold, the process proceeds to step S2311.

[0328] (Step S2310) The behavior estimation unit 335 selects the behavior information paired with the maximum score, and returns to the upper level process.

[0329] (Step S2311) The behavior estimation unit 335 acquires behavior information of “empty”. Then, the process returns to the upper level process.

[0330] In the flowchart of FIG. 23, the processes from step S2308 to step S2311 do not have to be performed.

[0331] Next, an example of the third behavior inference process in step S1904 will be described with reference to the flowchart in Fig. 24. The flowchart in Fig. 24 illustrates a process for inferring behavior information using a behavior correspondence table.

[0332] (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.

[0333] (Step S2402) The behavior estimation unit 335 assigns 1 to a counter i.

[0334] (Step S2403) The behavior estimation unit 335 judges 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, and if not, the process proceeds to step S2407.

[0335] (Step S2404) The behavior estimation unit 335 acquires the i-th behavior source vector included in the i-th behavior correspondence information.

[0336] (Step S2405) The behavior estimation unit 335 obtains a similarity between the behavior origin vector obtained in step S2403 and the behavior origin vector obtained in step S2404, and associates the similarity with the i-th behavior correspondence information.

[0337] (Step S2406) The behavior estimation unit 335 increments the counter i by 1. The process returns to step S2403.

[0338] (Step S2407) The behavior estimation unit 335 acquires the maximum similarity.

[0339] (Step S2408) The behavior estimation unit 335 judges 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.

[0340] (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.

[0341] (Step S2410) The behavior inferrer 335 acquires behavior information of “empty”. Then, the process returns to the upper level process.

[0342] In the flowchart of FIG. 24, the processes from step S2408 to step S2411 do not have to be performed.

[0343] Next, an example of the first emotion estimation process in step S1909 will be described with reference to 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.

[0344] (Step S2501) The emotion estimation unit 336 acquires two or more kinds of behavior source information acquired in step S1903. Here, each of the two or more kinds of behavior source information is emotion source information. In addition, the two or more kinds of emotion source information is, for example, an emotion source vector.

[0345] (Step S2502) The feeling estimation unit 336 acquires an emotion learning model from the learning management unit 311.

[0346] (Step S2503) The feeling 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.

[0347] (Step S2504) The feeling estimation unit 336 acquires the estimated feeling information and score, which are the execution results in step S2503.

[0348] (Step S2505) The feeling estimation unit 336 judges 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.

[0349] (Step S2506) Feeling estimation unit 336 acquires the feeling information acquired in step S2504 as feeling information to be output. Then, the process returns to the upper level process.

[0350] (Step S2507) The feeling estimation unit 336 acquires emotion information of “Sky”. The process returns to the upper level process.

[0351] In the flowchart of FIG. 25, the processes from step S2505 to step S2507 do not have to be performed.

[0352] Next, an example of the second emotion estimation process in step S1909 will be described with reference to 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.

[0353] (Step S2601) The feeling estimation unit 336 acquires two or more types of emotion source information acquired in step S1903.

[0354] (Step S2602) The feeling estimation unit 336 assigns 1 to a counter i.

[0355] (Step S2603) Feeling estimation unit 336 determines whether or not a candidate for the i-th emotion information exists, with reference to learning management unit 311. If a candidate for the i-th emotion information exists, the process proceeds to step S2604, and if not, the process proceeds to step S2609.

[0356] (Step S2604) The emotion estimation unit 336 acquires, from the learning management unit 311, the i-th emotion learning model paired with the i-th emotion information candidate.

[0357] (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.

[0358] (Step S2606) The feeling estimation unit 336 judges whether the execution result in step S2605 is “true” or not. If it is “true”, the process proceeds to step S2607, and if it is “false”, the process proceeds to step S2608.

[0359] (Step S2607) The feeling 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.

[0360] (Step S2608) The feeling estimation unit 336 increments the counter i by 1. The process returns to step S2603.

[0361] (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.

[0362] (Step S2610) The emotion estimation unit 336 selects the emotion information paired with the maximum score and returns to the upper level process.

[0363] (Step S2611) The feeling estimation unit 336 acquires emotion information of “Sky”. The process returns to the upper level process.

[0364] In the flowchart of FIG. 26, the processes from step S2608 to step S2611 do not have to be performed.

[0365] Next, an example of the third emotion estimation process in step S1909 will be described with reference to the flowchart in Fig. 27. The flowchart in Fig. 27 illustrates a process for estimating emotion information using an emotion correspondence table.

[0366] (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.

[0367] (Step S2702) The feeling estimation unit 336 assigns 1 to a counter i.

[0368] (Step S2703) The emotion estimation unit 336 judges 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, and if not, the process proceeds to step S2707.

[0369] (Step S2704) The emotion estimation unit 336 acquires the i-th emotion source vector included in the i-th emotion correspondence information.

[0370] (Step S2705) The emotion estimation unit 336 acquires a similarity between the emotion source vector acquired in step S2703 and the emotion source vector acquired in step S2704, and associates it with the i-th emotion correspondence information.

[0371] (Step S2706) The feeling estimation unit 336 increments the counter i by 1. The process returns to step S2703.

[0372] (Step S2707) The feeling estimation unit 336 acquires the maximum similarity.

[0373] (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 procedure goes to step S2709, and if it is less than the threshold, the procedure goes to step S2710.

[0374] (Step S2709) The emotion estimation unit 336 acquires emotion information associated with the i-th emotion association information paired with the maximum similarity. The process returns to the upper level process.

[0375] (Step S2710) The feeling estimation unit 336 acquires emotion information of “Sky”. The process returns to the upper level process.

[0376] In the flowchart of FIG. 27, the processes from step S2708 to step S2711 do not have to be performed.

[0377] Next, an example of the output configuration process in step S1917 will be described with reference to the flowchart in FIG.

[0378] (Step S2801) The configuration unit 339 acquires a template of a calendar from the storage unit 31.

[0379] (Step S2802) The configuration unit 339 assigns 1 to the counter i.

[0380] (Step S2803) The configuration unit 339 judges whether or not there is an i-th time period stored in the behavior management unit 313. If the i-th time period exists, the process proceeds to step S2804, and if not, the process returns to the upper level process. Note that the behavior management unit 313 stores behavior information and emotion information in association with each of one or more time periods.

[0381] (Step S2804) The configuration unit 339 judges whether or not 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, and if it is not included, the process proceeds to step S2809.

[0382] (Step S2805) The configuration unit 339 acquires, from the behavior management unit 313, behavior information paired with the i-th time period.

[0383] (Step S2806) Configuration section 339 acquires emotion information paired with the i-th time period from behavior management section 313. Note that emotion information does not necessarily have to be acquired here.

[0384] (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.

[0385] (Step S2808) The composition unit 339 places the time period information composed in step S2807 at the position in the calendar specified by the i-th time period.

[0386] (Step S2809) The configuration unit 339 increments the counter i by 1. The process returns to step S2803.

[0387] Next, an example of the first behavior learning process in step S1924 will be described with reference to the flowchart in Fig. 29. The behavior learning process is performed by, for example, 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 of a multi-value classification.

[0388] (Step S2901) The learning unit assigns 1 to a counter i.

[0389] (Step S2902) The learning unit judges 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.

[0390] (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.

[0391] (Step S2904) The learning unit increments the counter i by 1. The process returns to step S2902.

[0392] (Step S2905) The learning unit provides two or more pieces of behavior teacher data stored in a buffer (not shown) to a machine learning learning processing module, executes the module, and acquires a behavior learning model.

[0393] (Step S2906) The learning unit accumulates the behavior learning model acquired in step S2905 in the learning management unit 311.

[0394] In the flowchart of Figure 29, the learning unit may accumulate an behavior correspondence table in the learning management unit 311, in a buffer not shown, in which two or more behavior teacher data are records (behavior correspondence information), without performing the learning processing of steps S2905 and S2906.

[0395] 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.

[0396] (Step S3001) The learning unit assigns 1 to a counter i.

[0397] (Step S3002) The learning unit judges 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, and if not, the process returns to the upper level process.

[0398] (Step S3003) The learning unit acquires the i-th type of behavior information.

[0399] (Step S3004) The learning unit 313 acquires two or more positive examples that are teacher data in the behavior management unit 313 and are behavior teacher data that include the i-th type of behavior information.

[0400] (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 contain the i-th type of behavior information.

[0401] (Step S3006) The learning unit provides the two or more positive examples acquired in step S3004 and the two or more negative examples acquired in step S3005 to a machine learning learning processing module, executes the module, and acquires a behavior learning model.

[0402] (Step S3007) The learning unit stores the behavior learning model acquired in step S3006 in the learning management unit 311, paired with the i-th type of behavior information.

[0403] (Step S3008) The learning unit increments the counter i by 1. The process returns to step S3002.

[0404] Next, an example of the first emotion learning process in step S1925 will be described with reference to the flowchart in Fig. 31. Note that the behavior learning process is performed by, for example, 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 an emotion learning model for multi-value classification.

[0405] (Step S3101) The learning unit assigns 1 to a counter i.

[0406] (Step S3102) The learning section judges whether or not the i-th emotion information, etc. exists in the behavior management section 313. If the i-th emotion information, etc. exists, the process proceeds to step S3103, and if not, the process proceeds to step S3105.

[0407] (Step S3103) The learning unit creates emotion teacher data using the i-th emotion information, etc., 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.

[0408] (Step S3104) The learning unit increments the counter i by 1. The process returns to step S3102.

[0409] (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.

[0410] (Step S3106) The learning unit accumulates the emotion learning model acquired in step S3105 in the learning management unit 311.

[0411] In the flowchart in FIG. 31, the learning unit may accumulate an emotion correspondence table in a buffer (not shown) in learning management unit 311, in which two or more emotion teacher data are records (emotion correspondence information), without performing the learning processes in steps S3105 and S3106.

[0412] Next, an example of the second emotion learning process in step S1925 will be described using the flowchart in Fig. 32. The second emotion learning process is a process for acquiring an emotion learning model for binary classification for each of two or more emotion information candidates.

[0413] (Step S3201) The learning unit assigns 1 to a counter i.

[0414] (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, and if not, the process returns to the upper level processing.

[0415] (Step S3203) The learning unit acquires the i-th type of emotion information.

[0416] (Step S3204) The learning unit obtains two or more positive examples, which are emotion teacher data in the behavior management unit 313 and which are emotion teacher data including the i-th type of emotion information.

[0417] (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 contain emotion information of the i-th type.

[0418] (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.

[0419] (Step S3207) The learning unit accumulates the emotion learning model acquired in step S3206 in learning management unit 311, paired with the i-th type of emotion information.

[0420] (Step S3208) The learning unit increments the counter i by 1. The process returns to step S3202.

[0421] A specific operation example of the information system D in this embodiment will be described below. Now, the storage unit 31 of the server device 4 stores a behavior learning model acquired by a machine learning learning process using a large number of teacher data including the behavior source information of one or more users. Also, the storage unit 31 stores an emotion learning model acquired by a machine learning learning process using a large number of teacher data including the emotion source information of one or more users.

[0422] In addition, in the storage unit 31 of the behavior acquisition device 3, which is a terminal (e.g., a smart watch) held by the user "U1", a large number of behavior origin 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 origin management table shown in Figure 33.

[0423] The action source management table (Figure 33) is a record having "ID", "time information", "physical data", "location information", and "elapsed time". "ID" is information that identifies the record. "Time information" is information that specifies the time, and in this case, 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 action began.

[0424] It is assumed that the user "U1" inputs an output instruction to the behavior acquisition device 3. The behavior acquisition device 3 then accepts the output instruction.

[0425] Next, the behavior estimation unit 335, for example, accesses 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.

[0426] Next, the behavior estimation unit 335 configures a behavior origin vector having time information, body data, position 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 origin vector and the behavior learning model to a machine learning prediction processing module and executes the module. Then, the behavior estimation unit 335 acquires behavior information "A1" for the behavior origin vector from the record "ID=1" to the record "ID=289", and acquires behavior information "A2" for the behavior origin vector from "ID=290" onwards to the record up to "ID=N". Then, the accumulation unit 338 accumulates the behavior information and the behavior confirmation flag "0" in the behavior management unit 313 for each record. Note that "0" of the behavior confirmation flag and the emotion confirmation flag indicates that they are not confirmed, and "1" indicates that they are confirmed.

[0427] In addition, the emotion deduction unit 336 configures an emotion origin vector having time information, body data, location information, elapsed time, behavior information, etc. for each record in FIG. 33 by the process described using the flowchart in FIG. 25, for example. 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 origin vector and the emotion learning model to a machine learning prediction processing module and executes the module. Then, the emotion deduction unit 336 acquires emotion information "E1" for the behavior origin vector from the record "ID=1" to the record "ID=289", and acquires emotion information "E2" for the behavior origin vector from the record "ID=290" onwards to the record up to "ID=N". Then, the accumulation unit 338 accumulates emotion information and emotion confirmation flag "0" for each record in the behavior management unit 313.

[0428] 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".

[0429] Next, the storage unit 338 stores, for each record in FIG. 33 and FIG. 34, the time (for example, T 001 ,···,T 002 ,T 289 ) time period (e.g., "T 001 From T 289 "TZ1") is acquired, and the time period and behavioral information are stored in association with each other.

[0430] 33 and 34, the storage unit 338 stores the time (for example, T 001 ,···,T 002 ,T 289 ) time period (e.g., "T 001 From T 289 " ("TZ1"), and stores the time period and emotion information in association with each other. At this stage, the behavior confirmation flag and emotion confirmation flag corresponding to each time period are both "0". An example of the stored information is shown in Figure 35. Figure 35 is a time period information management table. The time period information management table has one or more records that have an "ID", "time period", "behavior information", "behavior confirmation flag", "emotion information", and "emotion confirmation flag".

[0431] Then, the configuration unit 339 performs the process described with reference to the flowchart in FIG. 28 using one or more sets of time zones, behavioral information, and emotion information accumulated by the accumulation unit 338, configures time zone information for each piece of behavioral information, arranges it in a calendar template, and configures output information.

[0432] 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 a calendar is displayed.

[0433] Then, if the behavioral information for each time period is correct, the user "U1" inputs a "confirmation command" for the displayed behavioral information, and if the estimated behavioral information is incorrect, the user "U1" inputs the correct behavioral information. Also, if the emotion information for each time period is correct, the user "U1" inputs a "confirmation command" for the output emotion information, and if the estimated emotion information is incorrect, the user "U1" inputs the correct emotion information. The "behavioral information", "behavioral confirmation flag", "emotion information", and "emotion confirmation flag" in FIG. 35 are changed by the user's input.

[0434] As described above, according to this embodiment, the user's behavior can be estimated using location information corresponding to time.

[0435] 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.

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

[0437] Moreover, according to this embodiment, the user's emotions during the action can be estimated.

[0438] Moreover, according to this embodiment, the user's behavior can be estimated with higher accuracy by using past records.

[0439] 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.

[0440] Furthermore, according to this embodiment, when the user's behavior cannot be estimated, the location where the user was can be output.

[0441] 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.

[0442] The process in this embodiment may be realized by software. This software may be distributed by software download or the like. 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 for realizing the information system D in this embodiment is a program as follows. That is, this program is a program for making a computer function as a time acquisition unit that acquires time, a position acquisition unit that acquires position information corresponding to the time, a behavior estimation unit that acquires behavior information that identifies the user's behavior in a time period identified by the time held by each of the two or more behavior source information using two or more behavior source information including the position information corresponding to the time, and a behavior output unit that outputs the behavior information in the time period.

[0443] (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 behavior information and emotion information of a user using location information and the like received from a terminal device of the user.

[0444] 37 is a conceptual diagram of an information system E in 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.

[0445] The behavior acquisition device 5 is a server, for example, a cloud server or an ASP server, but the type is not important. 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.

[0446] 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 smart watch, 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, receives behavior information and emotion information from the behavior acquisition device 5, and outputs the information. Note that the device that transmits the behavior source information and emotion source information to the behavior acquisition device 5 may be a different device from the device that receives behavior information and emotion information from the behavior acquisition device 5 and outputs the information.

[0447] Fig. 38 is a block diagram of an information system E in this embodiment. Fig. 39 is a block diagram of a behavior acquisition device 5.

[0448] 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 vitals 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.

[0449] The terminal device 6 includes a terminal storage unit 61, a terminal reception unit 62, a terminal reception unit 63, a device 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 vitals acquisition unit 334, and a location acquisition unit 337.

[0450] Various types of information are stored in the storage unit 51 constituting the behavior acquisition device 5. The various types of information are, for example, the above-mentioned learning information and the above-mentioned behavior information.

[0451] The receiving unit 52 receives various instructions and information from the terminal device 6. The various instructions and information are, for example, position information, activity data, vital data, output instructions, confirmation instructions, behavior information to be corrected, and emotion information to be corrected.

[0452] It is preferable that the receiving unit 52 receives the location information, activity data, vital data, etc. from the terminal device 6 all at once. It is preferable that the receiving unit 52 receives the location information, etc. associated with a user identifier all at once. The user identifier is information for identifying a 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.

[0453] The location acquisition unit 521 receives location information from the terminal device 6. Such location information is associated with time. When the location acquisition unit 521 receives location information, it is preferable that the location acquisition unit 521 acquires the time from a clock (not shown) and associates the time with the location information. It is preferable that such location information is associated with a user identifier.

[0454] The activity acquisition unit 522 receives activity data from the terminal device 6. Such activity data is associated with time. When the activity acquisition unit 522 receives activity data, it is preferable that the activity acquisition unit 522 acquires the time from a clock (not shown) and associates the time with the activity data. It is preferable that such activity data is associated with a user identifier.

[0455] 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.

[0456] The processing unit 53 performs various types of processing. The various types of processing are, for example, processing performed by the time acquisition unit 331, the behavior estimation unit 335, the emotion estimation unit 336, and the accumulation unit 338.

[0457] The transmission unit 54 transmits various types of information to the terminal device 6. The various types of information are, for example, estimated behavior information, estimated emotion information, and output information constructed by the construction unit 339.

[0458] The behavior output unit 341 transmits to the terminal device 6 the behavior information acquired by the behavior estimation unit 335 and associated with the time period.

[0459] The emotion output unit 342 transmits to the terminal device 6 emotion information acquired by the emotion estimation unit 336 and associated with the time period.

[0460] Various types of information are stored in the terminal storage unit 61 constituting the terminal device 6. The various types of information are, for example, position information, activity data, and vital data.

[0461] The terminal receiving unit 62 receives various instructions and information, such as an output instruction, a confirmation instruction, behavioral information corrected by the user from the estimated behavioral information, and emotion information corrected by the user from the estimated emotion information.

[0462] The means for inputting various instructions and information may be any means, such as a touch panel, a keyboard, a mouse, or a menu screen.

[0463] 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.

[0464] The device processing unit 64 performs various types of processing, such as processing for changing instructions, information, etc. received by the device receiving unit 62 into instructions, information, etc. with a structure to be transmitted, processing for changing information received by the device receiving unit 63 into a structure for outputting information, etc.

[0465] 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.

[0466] The terminal output unit 66 outputs various types of information, such as output information, behavioral information, and emotion information.

[0467] 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.

[0468] 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 inputted via an input device may be stored in the storage unit 51, etc.

[0469] 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.

[0470] The processing unit 53 and the terminal processing unit 64 can usually be realized by a processor, a memory, etc. The processing procedure of the processing unit 53, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type is not important.

[0471] 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.

[0472] The terminal output unit 66 may or may not 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 a combination of driver software for an output device and an output device, etc.

[0473] Next, an example of the operation of the behavior acquisition device 5 will be described with reference to the flowchart in Fig. 40. In the flowchart in Fig. 40, the description of the same steps as in Fig. 19 will be omitted.

[0474] (Step S4001) The receiving unit 52 judges 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, and 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.

[0475] (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 include one or more pieces of information of the year, month, and day.

[0476] (Step S4003) The storage unit 338 stores the position information and the like 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.

[0477] (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 (e.g., "from Dec. 17, 2023 to Dec. 23, 2023") that specifies the period for which behavioral information is to be acquired.

[0478] (Step S4005) The behavior estimation unit 335 assigns 1 to a counter i.

[0479] (Step S4006) The behavior estimation unit 335 judges 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.

[0480] (Step S4007) The behavior estimation unit 335 judges 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 is usually the case where the behavior information has already been estimated using the behavior source information.

[0481] (Step S4008) The behavior estimation unit 335 increments the counter i by 1. The process returns to step S4006.

[0482] (Step S4009) The receiving unit 52 judges 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. is, for example, a confirmation instruction, behavioral information to be corrected, or emotion information to be corrected.

[0483] (Step S4010) The receiving unit 52 judges 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.

[0484] (Step S4011) The transmission unit 54 transmits the output information constructed in step S1917 to the terminal device 6. Return to step S4001.

[0485] In the flowchart of FIG. 40, the process ends when the power is turned off or an interrupt occurs to end the process.

[0486] 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.

[0487] (Step S4101) The terminal transmitting unit 65 obtains the user identifier from the terminal storage unit 61, and transmits to the behavior acquisition apparatus 5 the position information and the like obtained in step S1903 in association with the user identifier.

[0488] (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.

[0489] (Step S4103) The terminal receiving unit 63 judges 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.

[0490] (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. Return to step S1901.

[0491] (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.

[0492] The information etc. is, for example, a confirmation instruction, changed behavioral information, and changed emotion information. The confirmation instruction includes information specifying the behavioral information to be confirmed or the emotion information to be confirmed. The changed behavioral information corresponds to information specifying the behavioral information to be modified. The changed emotion information corresponds to information specifying the emotion information to be modified.

[0493] In the flowchart of FIG. 41, the process ends when the power is turned off or an interrupt occurs to end the process.

[0494] As described above, according to this embodiment, the user's behavior can be estimated using location information corresponding to time.

[0495] 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.

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

[0497] Moreover, according to this embodiment, the user's emotions during the action can be estimated.

[0498] Moreover, according to this embodiment, the user's behavior can be estimated with higher accuracy by using past records.

[0499] 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.

[0500] Furthermore, according to this embodiment, when the user's behavior cannot be estimated, the location where the user was can be output.

[0501] Furthermore, the software for realizing 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 corresponding to the time, a behavior estimation unit that acquires behavior information that identifies a user's behavior in a time period identified by the time contained in each of the two or more behavior source information by using two or more pieces of behavior source information including the location information corresponding to the time, and a behavior output unit that outputs the behavior information in the time period.

[0502] (Embodiment 5) In this embodiment, a behavior analysis device that acquires and outputs a disorder score that specifies the degree of disorder in a user's behavior is described. In particular, in this embodiment, a behavior analysis device that acquires and outputs a disorder score using a user's time-series behavior information and reference information is described. Furthermore, the reference information is, for example, one or more types of information selected from information based on the user's past time-series information, a learning model based on the time-series behavior information, recommended behavior information, and other person reference information. The reference information may include, for example, environmental information.

[0503] In this embodiment, a behavior analysis device that acquires and outputs causes of behavioral irregularities will be described.

[0504] In this embodiment, a behavior analysis device that makes recommendations for improving behavioral irregularities will be described.

[0505] In this embodiment, a behavior analysis device that obtains and outputs an improvement degree of a behavioral disorder will be described. In this embodiment, a behavior analysis device that obtains and outputs an improvement degree when an improvement degree output condition is satisfied will be described.

[0506] In this embodiment, a behavior analysis device that acquires and outputs a recovery period from a behavioral disturbance will be described.

[0507] In this embodiment, a behavior analysis device that obtains and outputs a long-term disorder score will be described.

[0508] 42 is a conceptual diagram of an information system F in this embodiment. The information system F includes a behavior analysis device 7, one or more terminal devices 8, and two or more communication devices B.

[0509] The behavior analysis device 7 is a device that acquires one or more pieces of information among a disorder score described later, a disorder factor described later, recommendation information described later, an improvement degree described later, a recovery period described later, and a long-term disorder score described later, and transmits the information to the terminal device 8. The behavior analysis device 7 is a device that receives behavior source information such as location information from the user's terminal device 8, estimates the user's behavior information using the behavior source information, and transmits the behavior information to the terminal device 8. The behavior analysis device 7 is a device that receives emotion source information from the user's terminal device 8, estimates the user's emotion information using the emotion source information, and transmits the behavior information to the terminal device 8. The behavior analysis device 7 is usually a server, for example, a cloud server or an ASP server, but the type is not limited.

[0510] However, the behavior analysis device 7 may be a terminal device. In such a case, the behavior analysis device 7 may have all or part of the functions of the terminal device 2 or the functions of the behavior acquisition device 3. In such a case, the behavior analysis device 7 may be, for example, a smartphone, a tablet terminal, a smart watch, a so-called personal computer, or the like, and the type of the device is not important.

[0511] The terminal device 8 is a terminal used by a user. The terminal device 8 may be, for example, a smartphone, a tablet terminal, a smart watch, a so-called personal computer, or the like, and the type of the terminal device 8 does not matter. The terminal device 8 is, for example, a terminal that transmits behavior source information and emotion source information including location information and the like to the behavior analysis device 7, receives a disorder score described later, a disorder factor described later, recommendation information described later, an improvement degree described later, a recovery period described later, a long-term disorder score described later, behavior information, emotion information, and the like from the behavior analysis device 7, and outputs the information. Note that the device that transmits the behavior source information and emotion source information to the behavior analysis device 7 may be a device different from the device that receives the disorder score, disorder factor, and the like from the behavior analysis device 7 and outputs the information.

[0512] Fig. 43 is a block diagram of an information system F in this embodiment. Fig. 44 is a block diagram of a behavior analysis apparatus 7.

[0513] The behavior analysis device 7 includes a storage unit 71, a reception unit 72, a processing unit 73, and an output unit 74. The behavior analysis device 7 may include all or some of the components included in the behavior acquisition device 5. When the behavior analysis device 7 is a standalone device, the behavior analysis device 7 may include all or some of the components included in the behavior acquisition device 3 or the terminal device 2.

[0514] The storage unit 71 includes a learning management unit 311, a behavior management unit 313, a criterion management unit 711, and a recommendation management unit 712. The reception unit 72 includes a position acquisition unit 521, an activity acquisition unit 522, and a vital acquisition unit 523. The processing unit 73 includes a time acquisition unit 331, a behavior estimation unit 335, an emotion estimation unit 336, an accumulation unit 338, a configuration unit 339, a learning unit 731, a score acquisition unit 732, a long-term score acquisition unit 733, a judgment unit 734, a factor acquisition unit 735, a recommendation acquisition unit 736, an improvement degree acquisition unit 737, and a recovery period acquisition unit 738. The output unit 74 includes a behavior output unit 341, an emotion output unit 342, a score output unit 741, a long-term score output unit 742, a factor output unit 743, a recommendation output unit 744, an improvement degree output unit 745, and a recovery period output unit 746.

[0515] The terminal device 8 includes a terminal storage unit 61, a terminal reception unit 62, a terminal reception unit 83, a device processing unit 64, a terminal transmission unit 65, and a terminal output unit 86. 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 vitals acquisition unit 334, and a location acquisition unit 337.

[0516] Various types of information are stored in the storage unit 71 constituting the behavior analysis device 7. The various types of information are, for example, the above-mentioned learning information, the above-mentioned behavior information, the below-mentioned reference information, the below-mentioned recommendation information, the below-mentioned learning model, and the below-mentioned various conditions. It goes without saying that the various conditions may be embedded in the program.

[0517] One or more pieces of reference information are stored in the reference management unit 711. The reference information may be managed for each of two or more users. The reference information is associated with, for example, a user identifier.

[0518] The reference information is information that serves as a reference when acquiring a disorder score, which is the degree of disorder in a user's behavior. Disorder in behavior may also be referred to as disorder in daily life. The reference information is information that serves as a basis for acquiring the degree of disorder in a user's behavior corresponding to time-series information. The reference information is, for example, one or more types of information selected from self-reference information, learning model, recommended behavior information, and other-person reference information. The reference information also includes, for example, environmental reference information.

[0519] The disorder score is information that specifies the degree of disorder in behavior. Disorder in behavior may be referred to as disorder in daily life. The disorder score may be, for example, 0 to 100, or a 5-point scale or a 10-point scale. The disorder score may be a 2-point scale (disordered or not). The disorder score may also be a degree of well-being of daily life (degree of well-being).

[0520] The disorder score may be the degree of disorder rather than the degree of disorder. The degree of disorder can be said to be the degree of good living.

[0521] Examples of reference information, namely, self-reference information, learning model, recommended behavior information, other-person reference information, and environmental reference information, will be described in detail below. (1) Self-standard information

[0522] The self-reference information is information based on the past time-series information of a user. The reference information is, for example, a collection of average (normal) behavior information and time information of a user for a predetermined period (for example, one day, one week). The reference information is, for example, a collection of behavior information and time information when a user is not disturbed for a predetermined period. The behavior information in the reference information may correspond to the time (length) during which the behavior was performed. The behavior information in the reference information has, for example, the start time and end time of the behavior. The reference information is, for example, a vector. The vector is, for example, (time of behavior information 1, start time of behavior information 1, end time of behavior information 1, time of behavior information 2, start time of behavior information 2, end time of behavior information 2, . . ., time of behavior information n, start time of behavior information n, end time of behavior information n). The vector is, for example, (time-related information of behavior information 1, time-related information of behavior information 2, . . ., time-related information of behavior information n). Time-related information is one or more of the following information: time, start time, or end time. Examples of "behavioral information 1", "behavioral information 2", "behavioral information 3", and so on, are "sleep", "breakfast", "travel", "work", "shopping", "dinner", "drinking party", and "game". (2) Learning model,

[0523] The learning model is a model acquired by performing a machine learning learning process using two or more teacher data. The teacher data here has explanatory variables based on past time series information of one or more users and a target variable that is disturbance information regarding the degree of disturbance of the user's behavior. The two or more teacher data are data for creating a learning model used for one user, and may be information based on the time series information of the one user. In addition, the two or more teacher data are data for creating a learning model used for two or more users, and may be information based on the time series information of one or more users including other users. The learning model is usually a model created by the learning unit 731.

[0524] The disturbance information is information on the disturbance of the user's behavior. The disturbance information is, for example, a disturbance score, information indicating whether or not the user is disturbed, information indicating the level of physical well-being, information indicating whether or not the user is in good physical condition, information indicating the level of mental well-being, information indicating whether or not the user is in good mental condition, information indicating whether or not the user is in good mental and physical condition overall. The disturbance information is, for example, information input by the user. The disturbance information is, for example, a score obtained from one or more types of vital data of the user (for example, heart rate, heart rate variability, blood pressure (upper or lower), respiratory rate per unit time, body temperature) or information indicating whether or not the user is in good physical condition. Note that the technology for obtaining the score or information indicating whether or not the user is in good physical condition from the vital data is a publicly known technology. The disturbance information may be information input by the user in association with time-series information.

[0525] The learning model is information configured by the learning process of machine learning, and is information used in the prediction process of machine learning. The learning model may be called a learner, a classifier, a classification model, etc. The machine learning algorithm may be deep learning, random forest, decision tree, SVR, SVM, etc. In addition, for machine learning, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, fastText, TinySVM, etc., and various existing libraries can be used.

[0526] In addition, a learning model based on the time series information of one user may be considered to be an example of self-reference information. A learning model based on the time series information of one or more users including others may be considered to be an example of other-reference information. (3) Recommended Action Information

[0527] The recommended behavior information is information that specifies a behavior that is recommended. The recommended behavior information is, for example, information that specifies common sense that is unlikely to cause behavioral disorders, and information that specifies common sense that is likely to cause behavioral disorders. The recommended behavior information that specifies a recommended behavior is, for example, "number of meals = 3 times / day", "6 hours <= sleep time <= 10 hours", "20:00 <= bedtime <= midnight", and "5:00 <= wake-up time <= 9:00". The recommended behavior information that specifies a behavior that is not recommended is, for example, "number of drinking parties >= 3 times / week", "drinking parties for 3 consecutive days", and "game time >= 3 hours / day". Note that the recommended behavior information may be information that specifies a behavior that is not recommended. However, in this specification, the recommended behavior information is usually described as information that specifies a recommended behavior. (4) Reference information from others

[0528] The other-reference information is information based on the behavioral information of one or more other persons other than the user. The other-reference information is usually information based on the behavioral information of two or more persons. The other-reference information may include self-reference information. The other-reference information is, for example, a collection of average behavioral information and time information of two or more users over a predetermined period (for example, one day, one week). The data structure of the other-reference information is, for example, the same as the data structure of the self-reference information. It is preferable that the criteria management unit 711 stores other person criteria information corresponding to two or more user attribute value conditions. (5) Environmental standard information

[0529] Environmental standard information is environmental information that serves as a standard. Environmental information is information that specifies the environment of the place of activity. Environmental information is, for example, information that specifies the weather, temperature, humidity, and amount of ultraviolet rays. Environmental standard information is, for example, appropriate weather of "sunny," appropriate temperature range of "15 degrees <= temperature <= 25 degrees," appropriate humidity range of "40% <= humidity <= 60%," and appropriate amount of ultraviolet rays.

[0530] The recommendation management unit 712 stores recommendation source information in association with one or more factor conditions. A factor condition is a condition related to a disruption factor. The factor condition may be the same as the disruption condition. A disruption factor is a factor that disrupts behavior. Disruption factors are, for example, "sleep" (e.g., short sleep time, late bedtime, late wake-up time), "breakfast" (e.g., late breakfast, skipping breakfast), and "drinking parties" (frequent drinking parties).

[0531] The recommendation source information is information that is the source of the recommendation information. The recommendation source information is information for constituting the recommendation information. The recommendation source information has, for example, one or more variables. For example, a disturbance factor or element information corresponding to the disturbance factor is substituted into the variable. The recommendation source information may be recommendation information. The recommendation information is information that is recommended to a user to improve the disturbance of the user's behavior. The recommendation information is, for example, a character string, an audio, a still image, or a video, and the data type is not important.

[0532] The reception unit 72 receives one or more pieces of time-series information. There is no restriction on the method by which the reception unit 72 receives the time-series information. The reception unit 72 only needs to acquire the time-series information.

[0533] Time series information is chronological behavioral information of a user. Time series information includes two or more pieces of chronological behavioral information. Behavior information is information that identifies a user's behavior. Behavior information corresponds to time information, for example. Time information is information that identifies the time when a user performed an action. Time information is, for example, a start time and an end time. Time information is, for example, an hour.

[0534] The receiving unit 72 receives, for example, the behavior information acquired by the behavior estimation unit 335 for each of two or more time periods.

[0535] Here, acceptance typically refers to the reception of information transmitted via a wired or wireless communication line, but may also be a concept including the acceptance of information input from an input device such as a keyboard, mouse, or touch panel, and the acceptance of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

[0536] The processing unit 73 performs various types of processing. The various types of processing are, for example, processing performed by the time acquisition unit 331, the behavior estimation unit 335, the emotion estimation unit 336, the accumulation unit 338, the configuration unit 339, the learning unit 731, the score acquisition unit 732, the long-term score acquisition unit 733, the judgment unit 734, the factor acquisition unit 735, the recommendation acquisition unit 736, the improvement degree acquisition unit 737, and the recovery period acquisition unit 738.

[0537] The learning unit 731 creates a learning model using two or more pieces of teacher data, and accumulates it in the reference management unit 711. The learning unit 731 creates a learning model by machine learning learning processing using two or more pieces of teacher data. The teacher data here has explanatory variables based on past time-series information of one or more users, and a target variable that is disturbance information related to the degree of disturbance in the user's behavior.

[0538] The learning unit 731, for example, provides two or more pieces of teacher data having explanatory variables based on the past time-series information of each of one or more users and a target variable that is disturbance information related to the degree of disturbance of the user's behavior to a module that performs a machine learning learning process, executes the module, and acquires a learning model. As described above, any machine learning algorithm can be used.

[0539] The score acquiring unit 732 acquires a disorder score, which is a degree of disorder in the user's behavior, by using the time-series information accepted by the accepting unit 72 and the reference information of the reference managing unit 711. The score acquiring unit 732 usually acquires a disorder score, which is a score related to the difference between the time-series information accepted by the accepting unit 72 and the reference information of the reference managing unit 711.

[0540] The score acquiring unit 732 may acquire a first disturbance score and a second disturbance score which are disturbance scores in each of the two time periods. The score acquiring unit 732 may acquire a disturbance score for each of two or more pieces of time-series information.

[0541] Below, examples of the processing of the score acquisition unit 732 will be described for each of the cases where the reference information is self-reference information, learning model, recommended behavior information, and other-person reference information. (1) Self-standard information

[0542] The score acquisition unit 732 acquires difference information regarding the difference between the time-series information for a predetermined period (for example, one day, one week) accepted by the acceptance unit 72 and the self-reference information for the predetermined period, and acquires a larger disturbance score as the difference information increases. (1-1) When the self-reference information is a vector (self-reference vector)

[0543] The score acquiring unit 732 acquires a vector from the time-series information for a predetermined period (for example, one day or one week) accepted by the accepting unit 72. Such a vector is called a test vector. Next, the score acquiring unit 732 acquires a larger disturbance score as the difference between the test vector and the self reference vector becomes larger.

[0544] For example, the score acquiring unit 732 acquires the distance between the inspection vector and the self reference vector. Next, the score acquiring unit 732 acquires the disturbance score by the distance or an increasing function with the distance as a parameter.

[0545] Also, for example, the score acquisition unit 732 acquires the difference between each element of the acquired inspection vector and each element of the self reference vector for each element, and for each element, if the difference between the elements is greater than or equal to a threshold value, counts up the disturbance score to acquire the final disturbance score.

[0546] Also, for example, the score acquiring unit 732 acquires the difference between each element of the inspection vector and each element of the self reference vector for each element, and acquires a disturbance score that is the sum of the absolute values ​​of the differences for each element. (1-2) When self-reference information is a set of pairs of behavioral information and time information

[0547] The score acquiring unit 732 judges, for each piece of behavior information contained in the time-series information for a predetermined period received by the receiving unit 72, whether or not the behavior information exists in the personal reference information.

[0548] For example, if there is behavioral information that does not exist in the self-reference information, the score acquisition unit 732 counts up the disorder score. The behavioral information that does not exist in the self-reference information is, for example, information on behavior that is not normally performed, and may be a cause of behavioral disorder.

[0549] Furthermore, the score acquiring unit 732 acquires a difference (for example, one or more pieces of information among a difference in length of time, a difference in start time, and a difference in end time) between time information paired with the behavior information included in the time-series information for the predetermined period accepted by the accepting unit 72 and time information paired with the behavior information included in the self-reference information. The larger the difference, the larger the disturbance score acquired by the score acquiring unit 732.

[0550] Furthermore, the score acquiring unit 732 acquires the frequency of behavioral information (e.g., "meal" and "drinking party") contained in the time-series information for the predetermined period accepted by the accepting unit 72. The score acquiring unit 732 acquires the frequency of behavioral information in the self-reference information. Next, when the difference between the two frequencies is equal to or greater than a threshold, the score acquiring unit 732 acquires a large disorder score. (2) Learning Model

[0551] The score acquisition unit 732 performs machine learning prediction processing using the time-series information and the learning model accepted by the acceptance unit 72, and acquires a disturbance score.

[0552] For example, the score acquiring unit 732 configures a test vector from the time-series information accepted by the accepting unit 72. The test vector here is, for example, (time of behavior information 1, start time of behavior information 1, end time of behavior information 1, time of behavior information 2, start time of behavior information 2, end time of behavior information 2, . . ., time of behavior information n, start time of behavior information n, end time of behavior information n), or (time-related information of behavior information 1, time-related information of behavior information 2, . . ., time-related information of behavior information n), but the structure is not important.

[0553] Next, the score acquisition unit 732 provides the inspection vector and the learning model to a module that performs machine learning prediction processing, executes the module, and acquires a disturbance score. The score acquisition unit 732 provides the vector and the learning model to a module that performs machine learning prediction processing, executes the module, acquires information on whether or not the vector is disturbed, and when an objective variable of "disturbed" is acquired, acquires a score returned by the module that performs the prediction processing, and acquires a disturbance score based on the score. Such a disturbance score is a score returned by the module, or a score acquired by an increasing function with the score returned by the module as a parameter. For example, when an objective variable of "not disturbed" is acquired, the score acquisition unit 732 acquires a disturbance score of "0".

[0554] In addition, the module that performs the machine learning prediction process may be a module that returns a disturbance score, or a module that returns a predicted value that is the basis of the disturbance score.

[0555] Further, the algorithm for the machine learning prediction process may be any of deep learning, random forest, decision tree, SVM, etc. However, it is preferable that the algorithm for the machine learning prediction process is a random forest. This is because the random forest can obtain the influence of each explanatory variable given to the output target variable. Behavioral information corresponding to explanatory variables with a large influence constitutes a disturbance factor described later. An explanatory variable with a large influence is, for example, an explanatory variable with the highest influence ranking, an influence ranking of N or higher, or an influence equal to or greater than a threshold value. (3) Recommended Action Information (3-1) When the recommended action information is a vector (recommended action vector)

[0556] The score acquiring unit 732 acquires an inspection vector from time-series information for a predetermined period (for example, one day or one week) accepted by the accepting unit 72. Next, the score acquiring unit 732 acquires a larger disturbance score as the difference between the inspection vector and the recommended action vector becomes larger.

[0557] For example, the score acquiring unit 732 acquires the distance between the test vector and the recommended action vector, and then acquires a disturbance score using the distance or an increasing function with the distance as a parameter.

[0558] Also, for example, the score acquisition unit 732 acquires the difference between each element of the test vector and each element of the recommended action vector for each element, and for each element, if the difference between the elements is equal to or greater than a threshold, counts up the disorder score to acquire a final disorder score.

[0559] Also, for example, the score acquiring unit 732 acquires the difference between each element of the test vector and each element of the recommended action vector for each element, and acquires a disturbance score that is the sum of the absolute values ​​of the differences for each element. (3-2) When the recommended action information is a set of pairs of action information and time information

[0560] For example, the score acquiring unit 732 determines whether or not the behavior information exists in the recommended behavior information for each piece of behavior information contained in the time-series information for a predetermined period received by the receiving unit 72. When the recommended behavior information is information for identifying a recommended behavior, the score acquiring unit 732 acquires a larger disorder score when the behavior information does not exist in the recommended behavior information than when the behavior information exists in the recommended behavior information. When the recommended behavior information is information for identifying a non-recommended behavior, the score acquiring unit 732 acquires a larger disorder score when the behavior information exists in the recommended behavior information than when the behavior information does not exist in the recommended behavior information.

[0561] For example, the score acquiring unit 732 acquires time information (e.g., time, start time, end time) paired with the behavior information contained in the time-series information for the predetermined period accepted by the accepting unit 72. For each behavior information, the score acquiring unit 732 determines whether or not the time information paired with the behavior information satisfies a condition (e.g., a suitable range of sleep time) of the time information paired with the behavior information contained in the recommended behavior information. When the condition of the time information paired with the behavior information contained in the recommended behavior information is not satisfied, the score acquiring unit 732 acquires a larger disturbance score than when the condition is satisfied. (4) Reference information from others (4-1) When the other-reference information is a vector (other-reference vector)

[0562] The score acquiring unit 732 acquires a test vector from time-series information for a predetermined period (for example, one day or one week) accepted by the accepting unit 72. Next, the score acquiring unit 732 acquires a larger disturbance score as the difference between the test vector and the other's reference vector becomes larger.

[0563] For example, the score acquiring unit 732 acquires the distance between the test vector and the other reference vector. Next, the score acquiring unit 732 acquires the disorder score by the distance or an increasing function with the distance as a parameter.

[0564] Also, for example, the score acquisition unit 732 acquires the difference between each element of the test vector and each element of the other reference vector for each element, and counts up the disturbance score for each element if the difference between the elements is greater than or equal to a threshold value, thereby acquiring the final disturbance score.

[0565] Also, for example, the score acquiring unit 732 acquires the difference between each element of the test vector and each element of the other reference vector for each element, and acquires a disorder score that is the sum of the absolute values ​​of the differences for each element. (4-2) When other-reference information is a set of pairs of action information and time information

[0566] The score acquiring unit 732 judges, for each piece of behavior information contained in the time-series information for a predetermined period received by the receiving unit 72, whether or not the behavior information exists in the other person reference information.

[0567] If there is behavior information that does not exist in the other person's reference information, the score acquiring unit 732 increments the disorder score. The behavior information that does not exist in the other person's reference information is information on behavior that is not usually performed by two or more people.

[0568] Furthermore, the score acquiring unit 732 acquires a difference (for example, one or more pieces of information among a difference in length of time, a difference in start time, and a difference in end time) between the time information paired with the behavior information included in the time-series information for the predetermined period accepted by the accepting unit 72 and the time information paired with the behavior information present in the other person's reference information. The score acquiring unit 732 acquires a larger disturbance score the larger the difference is.

[0569] Furthermore, the score acquiring unit 732 acquires the frequency of behavioral information (e.g., "meal" and "drinking party") contained in the time-series information for the predetermined period accepted by the accepting unit 72. The score acquiring unit 732 acquires the frequency of behavioral information in the other person reference information. Next, when the difference between the two frequencies is equal to or greater than a threshold, the score acquiring unit 732 acquires a large disorder score. Furthermore, the score acquisition unit 732 may, for example, acquire a comparison result of scores for two or more periods, and acquire a score based on the comparison result. For example, the score acquisition unit 732 acquires the change (for example, difference, ratio) in the score for the same month of the previous year (June 2023 and June 2024) or for one week in the previous month, and acquires a score using a function with the change as a parameter. Such a score is called a change score, and the greater the degree of improvement in the score, the greater the change score acquired.

[0570] The long-term score acquiring unit 733 acquires a long-term disorder score that specifies the degree of long-term behavioral disorder using two or more disorder scores acquired by the score acquiring unit 732. The long-term here is, for example, three months (each season), one year, one month, etc. The long-term is a period longer than one day. The long-term is usually a period of one week or more.

[0571] The long-term score acquiring unit 733 acquires the number of times that the judgment unit 734 judges that the disturbance condition is satisfied, and acquires a long-term disturbance score using the number of times. The long-term score acquiring unit 733 acquires a larger long-term disturbance score as the number of times that the judgment unit 734 judges that the disturbance condition is satisfied increases.

[0572] The judgment unit 734 judges whether the disturbance score acquired by the score acquisition unit 732 satisfies a disturbance condition. The disturbance condition is a condition for acquiring a disturbance factor. For example, the disturbance condition is that the disturbance score is equal to or greater than a threshold value.

[0573] The disturbance factor is information about behavioral information that causes disturbance. The disturbance factor usually includes behavioral information. It also includes time information. The disturbance factor is, for example, information obtained using behavioral information corresponding to explanatory variables with a large degree of influence obtained when a prediction process is performed using a random forest. The disturbance factor is, for example, information obtained using behavioral information in which difference information identifying a difference from reference information is equal to or greater than a threshold value. The disturbance factor may be information about only specific behavioral information (for example, "sleep", "drinking party", "meal"). The disturbance factor may be information about behavioral information excluding specific behavioral information (for example, "movement").

[0574] The judgment unit 734 judges whether the first disorder score and the second disorder score satisfy a recovery condition. The recovery condition is a condition for acquiring a recovery period. The recovery condition is, for example, that the disorder condition is no longer met, that the disorder condition is satisfied and then no longer satisfied, that the improvement degree is equal to or greater than a threshold, that the improvement degree is equal to or greater than a threshold and the second disorder score is equal to or less than a threshold, or that the second disorder score is equal to or less than a threshold.

[0575] The determination unit 734 determines whether each of the two or more disturbance scores satisfies a disturbance condition. The disturbance condition is a condition for the factor acquisition unit 735 to acquire a disturbance factor. The disturbance condition is, for example, that the disturbance score is equal to or greater than a threshold value.

[0576] The factor acquiring unit 735 acquires a disturbance factor related to the behavioral information that is a cause of disturbance, which is time-series information corresponding to the disturbance score, from the behavioral information contained in the time-series information accepted by the accepting unit 72. It is preferable that the factor acquiring unit 735 acquires the disturbance factor when the judging unit 734 judges that the disturbance condition is satisfied.

[0577] The factor acquisition unit 735 acquires behavioral information corresponding to explanatory variables having a large degree of influence acquired as a result of prediction processing using random forest, for example, and acquires disturbance factors having the behavioral information and time information paired with the behavioral information.

[0578] The factor acquiring unit 735 acquires behavioral information that has become a factor in increasing the disorder score acquired by the score acquiring unit 732 to the extent that the adoption condition is satisfied, for example. The factor acquiring unit 735 acquires a disorder factor having the behavioral information and time information paired with the behavioral information. The adoption condition is a condition for behavioral information to be adopted as a disorder factor. An example of the adoption condition is that the increase in the disorder score is equal to or greater than a threshold. An example of the adoption condition is that the number of occurrences of specific behavioral information to be adopted as a disorder factor in a predetermined period is equal to or greater than a threshold (for example, the number of drinking parties is 3 or more times / week).

[0579] The recommendation acquisition unit 736 refers to the recommendation management unit 712 and acquires recommendation source information corresponding to the factor condition that matches the disturbance factor acquired by the factor acquisition unit 735 from the recommendation management unit 712. The recommendation acquisition unit 736 acquires recommendation information by substituting the disturbance factor into the recommendation information that is the recommendation source information or into the variable portion of the recommendation source information.

[0580] The improvement degree acquiring unit 737 acquires the improvement degree by using the first disturbance score and the second disturbance score. The first disturbance score and the second disturbance score are disturbance scores acquired by the score acquiring unit 732 by using time-series information at different times.

[0581] The degree of improvement is information that specifies the degree of improvement of the disorder. The degree of improvement is usually information regarding the difference between the first disorder score and the second disorder score. For example, when the disorder score is information indicating the degree of disorder, the degree of improvement is "first disorder score - second disorder score", and when the disorder score is information indicating the goodness of the behavior, the degree of improvement is "second disorder score - first disorder score".

[0582] It is preferable that the improvement degree acquisition unit 737 acquires the improvement degree when the first disorder score and the second disorder score satisfy an improvement degree output condition. Note that the improvement degree output condition is, for example, that the improvement degree is equal to or greater than a threshold value, or that the improvement degree is equal to or less than a threshold value.

[0583] The recovery period acquisition unit 738 acquires a recovery period that is a difference between the first time information corresponding to the first disturbance score and the second time information corresponding to the second disturbance score. It is preferable that the recovery period acquisition unit 738 acquires the recovery period when the judgment unit 734 judges that the recovery condition is satisfied. The recovery condition is a condition for acquiring the recovery period. For example, the recovery condition is that the second disturbance score does not satisfy the disturbance condition.

[0584] The output unit 74 outputs various types of information, such as a disorder score, a long-term disorder score, behavior information, emotion information, or location information.

[0585] Here, output usually means output to terminal device 8, but it may also be a concept including display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage on a recording medium, and handing over the processing results to other processing devices or other programs.

[0586] The score output unit 741 outputs the disorder score acquired by the score acquisition unit 732. There is no limit to the manner in which the disorder score is output. It is preferable that the score output unit 741 outputs the disorder score in association with a user identifier. It is preferable that the score output unit 741 outputs the disorder score for each predetermined period (for example, one week, one day).

[0587] The long-term score output unit 742 outputs the long-term disorder score acquired by the long-term score acquisition unit 733. There is no limitation on the output form of the long-term disorder score. It is preferable that the long-term score output unit 742 outputs the long-term disorder score in association with the user identifier.

[0588] The factor output unit 743 outputs one or more disturbance factors acquired by the factor acquisition unit 735. It is preferable that the factor output unit 743 outputs one or more disturbance factors in association with the time-series information.

[0589] The recommendation output unit 744 outputs one or more pieces of recommendation information acquired by the recommendation acquisition unit 736 .

[0590] The improvement degree output unit 745 outputs the degree of improvement acquired by the improvement degree acquisition unit 737 .

[0591] The recovery period output unit 746 outputs the recovery period acquired by the recovery period acquisition unit 738 .

[0592] The terminal receiving unit 83 constituting the terminal device 8 receives various types of information. The terminal receiving unit 83 receives various types of information from the behavior analysis device 7. The various types of information are, for example, behavioral information, emotion information, disorder score, long-term disorder score, disorder factor, recommendation information, improvement degree, or recovery period.

[0593] The terminal output unit 86 outputs various types of information, such as behavioral information, emotion information, disturbance score, long-term disturbance score, disturbance factor, recommendation information, improvement degree, or recovery period.

[0594] The storage unit 71, the learning management unit 311, the criteria management unit 711, and the recommendation management unit 712 are preferably non-volatile recording media, but may also be realized using volatile recording media.

[0595] There is no restriction on the process by which information is stored in the storage unit 71 etc. For example, information may be stored in the storage unit 71 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 71 etc., or information inputted via an input device may be stored in the storage unit 71 etc.

[0596] The reception unit 72 is preferably realized by a wireless or wired communication means, but may also be realized by a means for receiving broadcasts, a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

[0597] The processing unit 73, learning unit 731, score acquisition unit 732, long-term score acquisition unit 733, judgment unit 734, factor acquisition unit 735, recommendation acquisition unit 736, improvement degree acquisition unit 737, and recovery period acquisition unit 738 can usually be realized by a processor, memory, etc. The processing procedure of the processing unit 73, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor is a CPU, MPU, GPU, etc., and the type does not matter.

[0598] The output unit 74, which includes the behavior output unit 341, emotion output unit 342, score output unit 741, long-term score output unit 742, factor output unit 743, recommendation output unit 744, improvement degree output unit 745, and recovery period output unit 746, is preferably realized by wireless or wired communication means, but may also be realized by driver software for an output device such as a display or speaker, or driver software for an output device and an output device, etc.

[0599] The terminal receiving unit 83 is usually realized by a wireless or wired communication means, but may be realized by a means for receiving a broadcast.

[0600] The terminal output unit 86 may or may not include an output device such as a display, a speaker, etc. The terminal output unit 86 may be realized by driver software for an output device, or a combination of driver software for an output device and an output device, etc.

[0601] Next, an example of the operation of the behavior analysis device 7 will be described with reference to the flowchart in Fig. 45. The flowchart in Fig. 45 does not include the same processes as those in the behavior acquisition device 5. The processes that are the same as those in the behavior acquisition device 5 are the processes in the flowchart in Fig. 40.

[0602] (Step S4501) The processing unit 73 judges whether or not the score acquisition condition is satisfied. If the score acquisition condition is satisfied, the process proceeds to step S4502, and if the score acquisition condition is not satisfied, the process proceeds to step S4519.

[0603] The score acquisition condition is a condition for acquiring the disordered score. The score acquisition condition is, for example, that a predetermined time (e.g., 24:00 every day) has arrived, that the reception unit 72 has received a score acquisition instruction from a user, or that the reception unit 72 has received time-series information. The score acquisition instruction received by the reception unit 72 includes a user identifier. Furthermore, the time-series information received by the reception unit 72 is associated with the user identifier.

[0604] (Step S4502) The processing unit 73 assigns 1 to a counter i.

[0605] (Step S4503) The processing unit 73 judges whether or not there is an i-th user for which a disorder score is to be obtained. If there is an i-th user, the process proceeds to step S4504, and if there is no i-th user, the process returns to step S4501.

[0606] (Step S4504) The score acquisition unit 732 acquires the disorder score of the i-th user. An example of such score acquisition processing will be described with reference to the flowcharts of FIGS.

[0607] (Step S4505) The score output unit 741 accumulates the disorder score acquired in step S4504 in the behavior management unit 313 in association with the i-th user identifier and the time-series information used in acquiring the disorder score.

[0608] (Step S4506) The judgment unit 734 judges whether or not the disorder score acquired in step S4504 matches the disorder condition. If the disorder condition is matched, the process proceeds to step S4507, and if the disorder condition is not matched, the process proceeds to step S4512.

[0609] (Step S4507) The cause acquiring unit 735 acquires the cause of the irregular behavior of the user. An example of the cause acquiring process will be described with reference to the flowchart of FIG.

[0610] (Step S4508) The recommendation acquiring unit 736 acquires recommendation information. An example of such recommendation acquiring processing will be described with reference to the flowchart in FIG.

[0611] (Step S4509) The processing unit 73 or a component not shown in the figure configures output information. The output information is information that is output. The output information has a disturbance score. The output information here has, for example, one or more disturbance factors and one or more recommendation information.

[0612] (Step S4510) The output unit 74 outputs the output information configured in step S4509. The output unit 74 transmits the output information to, for example, the i-th user.

[0613] (Step S4511) The processing unit 73 increments the counter i by 1. The process returns to step S4503.

[0614] (Step S4512) Improvement degree obtaining unit 737 obtains the past disorder score of the i-th user. The past disorder score is preferably the most recent disorder score.

[0615] (Step S4513) The improvement degree acquisition unit 737 judges whether or not the disorder score acquired in step S4504 and the disorder score acquired in step S4512 satisfy an improvement degree output condition. If the improvement degree output condition is satisfied, the process proceeds to step S4514, and if not, the process proceeds to step S4515.

[0616] (Step S4514) The improvement degree acquisition unit 737 acquires an improvement degree by using the disorder score acquired in step S4504 and the disorder score acquired in step S4512. The improvement degree output unit 745 accumulates the improvement degree in the behavior management unit 313 in association with the i-th user identifier and the time-series information used in acquiring the disorder score.

[0617] (Step S4515) The judgment unit 734 judges whether or not the disorder score acquired in step S4504 and the disorder score acquired in step S4512 satisfy the recovery condition. If the recovery condition is satisfied, the process proceeds to step S4516, and if the recovery condition is not satisfied, the process proceeds to step S4517.

[0618] (Step S4516) The recovery period acquisition unit 738 acquires a recovery period which is the difference between the time information corresponding to the disorder score acquired in step S4504 and the time information corresponding to the disorder score acquired in step S4512. The recovery period output unit 746 accumulates the recovery period in the behavior management unit 313 in association with the i-th user identifier and the time-series information used in acquiring the disorder score.

[0619] (Step S4517) The processing unit 73 or a component not shown in the figure composes output information. The output information has a disorder score. The output information here has, for example, an improvement degree and a recovery period.

[0620] (Step S4518) The output unit 74 outputs the output information constructed in step S4517. The output unit 74 transmits the output information to, for example, the i-th user.

[0621] (Step S4519) The processing unit 73 judges whether or not the long-term score acquisition condition is satisfied. If the long-term score acquisition condition is satisfied, the process proceeds to step S4520, and if the score acquisition condition is not satisfied, the process proceeds to step S4525. The long-term score acquisition condition may be, for example, that a disorder score has been accumulated for a predetermined period of time, or that an instruction from a user has been accepted.

[0622] (Step S4520) The processing unit 73 assigns 1 to the counter i.

[0623] (Step S4521) Processing unit 73 judges whether or not there is an i-th user for which a long-term disorder score is to be obtained. If there is an i-th user, the process proceeds to step S4522, and if there is no i-th user, the process returns to step S4501.

[0624] (Step S4522) The long-term score acquisition unit 733 acquires the long-term disorder score of the i-th user. An example of such long-term score acquisition processing will be described with reference to the flowchart in FIG.

[0625] (Step S4523) The long-term score output section 742 accumulates the long-term disorder score acquired in step S4522 in the behavior management section 313 in association with the i-th user.

[0626] (Step S4524) The processing unit 73 increments the counter i by 1. The process returns to step S4503.

[0627] (Step S4525) The processing unit 73 judges whether or not the learning condition is satisfied. If the learning condition is satisfied, the process proceeds to step S4526, and if the score acquisition condition is not satisfied, the process returns to step S4501. The learning condition is a condition for creating a learning model. The learning condition is, for example, that the number of user time-series information is equal to or greater than a threshold, or that an instruction from the user is accepted.

[0628] (Step S4526) The processing unit 73 assigns 1 to the counter i.

[0629] (Step S4527) The processing unit 73 judges whether or not there exists an i-th user for which a learning model is to be created. If there exists an i-th user, the process proceeds to step S4528, and if not, the process returns to step S4521.

[0630] (Step S4528) The learning unit 731 performs learning processing using the time-series information of the i-th user to acquire a learning model. An example of such learning processing will be described with reference to the flowchart of FIG.

[0631] (Step S4529) The output unit 74 stores the learning model acquired in step S4528 in the reference management unit 711 in association with the i-th user.

[0632] (Step S4530) The processing unit 73 increments the counter i by 1. The process returns to step S4527.

[0633] In the flowchart of FIG. 45, the process ends when the power is turned off or an interrupt occurs to end the process.

[0634] Next, a first example of the score acquisition process in step S4504 will be described with reference to the flowchart in Fig. 46. The first example of the score acquisition process is a case where a disturbance score is acquired based on the similarity between an inspection vector based on the user's time-series information and reference information in the case of a vector.

[0635] (Step S4601) The score acquiring unit 732 acquires time-series information of the target user. The time-series information is, for example, time-series information paired with the user identifier of the target user, or time-series information accepted by the accepting unit 72.

[0636] (Step S4602) Score acquisition unit 732 judges whether or not to use environmental information to acquire the disturbance score. If environmental information is to be used, the process proceeds to step S4603, and if environmental information is not to be used, the process proceeds to step S4604.

[0637] (Step S4603) The score acquiring unit 732 acquires environmental information paired with the time-series information of the target user.

[0638] (Step S4604) The score acquiring unit 732 uses the time-series information of the target user, or the time-series information of the target user and the environmental information to construct a vector. Such a vector is called a test vector.

[0639] The test vector is, for example, (time of behavior information 1, start time of behavior information 1, end time of behavior information 1, time of behavior information 2, start time of behavior information 2, end time of behavior information 2,..., time of behavior information n, start time of behavior information n, end time of behavior information n, environmental information 1,..., environmental information m). For example, environmental information 1 is the weather, and environmental information m is the temperature. Note that m is a natural number greater than or equal to 1.

[0640] (Step S4605) The score acquiring unit 732 acquires the reference vector of the reference managing unit 711.

[0641] (Step S4606) Score obtaining section 732 calculates the distance (for example, cos Θ) between the test vector and the reference vector.

[0642] (Step S4607) The score acquisition unit 732 acquires the disturbance score by an increasing function with the distance acquired in step S4606 as a parameter. Then, the process returns to the upper process.

[0643] Next, a second example of the score acquisition process in step S4504 will be described with reference to the flowchart of Fig. 47. In the flowchart of Fig. 47, the description of the same steps as in the flowchart of Fig. 46 will be omitted. The second example of the score acquisition process is a case based on machine learning.

[0644] (Step S4701) The score acquiring unit 732 acquires a learning model from the reference managing unit 711. Here, it is preferable that the score acquiring unit 732 acquires a learning model paired with the user identifier of the target user.

[0645] (Step S4702) The score acquisition unit 732 provides the test vector and the learning model acquired in step S4701 to a module that performs machine learning prediction processing, executes the module, and acquires a prediction result. The prediction result is, for example, a disorder score. The prediction result is, for example, a score returned by the module indicating whether or not there is disorder.

[0646] (Step S4703) The score acquisition unit 732 acquires the disturbance score based on the prediction result, and returns to the upper process.

[0647] For example, if the prediction result is “not disturbed (e.g., “0”)”, the score acquisition unit 732 acquires a disturbance score of “0”, and if the prediction result is “disturbed (e.g., “1”)”, the score acquisition unit 732 acquires a disturbance score calculated using an increasing function with the score returned by the module as a parameter.

[0648] Next, a third example of the score acquisition process in step S4504 will be described with reference to the flowchart in Fig. 48. In the flowchart in Fig. 48, the description of the same steps as those in the flowchart in Fig. 46 will be omitted. Note that the third example is a case where a difference between each element information of the time-series information and the element information of the reference information is determined.

[0649] (Step S4801) The score acquiring unit 732 acquires reference information including time-series information from the reference managing unit 711. The reference information may include one or more pieces of environmental information. For example, the one or more pieces of environmental information are "<Weather> Sunny <Temperature> 15 degrees to 25 degrees <Humidity> 40% to 60%."

[0650] (Step S4802) The score obtaining unit 732 assigns 1 to the counter i.

[0651] (Step S4803) The score acquisition unit 732 judges whether or not the i-th element information exists in the acquired time-series information of the target user, etc. If the i-th element information exists, the process proceeds to step S4804, and if not, the process proceeds to step S4811.

[0652] (Step S4804) The score acquiring unit 732 acquires the i-th element information from the time-series information of the target user, etc. The element information is behavior information and time information, or environment information.

[0653] (Step S4805) The score acquisition unit 732 judges whether or not the behavioral information or environmental information acquired in step S4804 exists in the reference information. If it exists in the reference information, the process proceeds to step S4806, and if it does not exist, the process proceeds to step S4809.

[0654] (Step S4806) The score acquiring unit 732 acquires the difference between the time information paired with the behavior information in the i-th element information and the time information paired with the behavior information in the reference information, or the difference between the environmental information (e.g., temperature) in the i-th element information and the environmental information (e.g., temperature) in the reference information. The score acquiring unit 732 temporarily accumulates the difference in association with the i-th element information.

[0655] The difference is, for example, one or more of a time difference, a start time difference, and an end time difference. The difference is, for example, a difference in environmental information or whether the environmental information is within the range of the conditions of the environmental information included in the standard information.

[0656] (Step S4807) The score acquisition unit 732 judges whether or not the difference acquired in step S4806 satisfies the addition condition. If the addition condition is satisfied, the process proceeds to step S4808, and if the addition condition is not satisfied, the process proceeds to step S4809.

[0657] The addition condition is a condition for increasing the disorder score. For example, the addition condition is that the difference is equal to or greater than a threshold. For example, the addition condition is that the condition of the element information in the reference information is not satisfied. For example, the condition of the element information is "sleep time is 6 to 9 hours."

[0658] (Step S4808) The score acquisition unit 732 adds α to the disorder score. The process proceeds to step S4810. Note that the initial value of the disorder score is, for example, "0". Also, α may be a fixed positive number, or may be a positive number that varies depending on the magnitude of the difference or the element information.

[0659] (Step S4809) The score acquisition unit 732 adds β to the disorder score. Note that β may be a fixed positive number, or may be a positive number that differs depending on the element information.

[0660] (Step S4810) The score obtaining unit 732 increments the counter i by 1. The process returns to step S4803.

[0661] (Step S4811) The score obtaining unit 732 assigns 1 to the counter j.

[0662] (Step S4812) The score acquisition unit 732 judges whether or not the jth overall condition exists. If the jth overall condition exists, the process proceeds to step S4813; if not, the process returns to the upper level process. The overall condition is a condition related to one or more element information in the entire time-series information. For example, the overall condition is "the time-series information including a collection of behavioral information for one week includes behavioral information "drinking party" four or more times," or "the time-series information including a collection of behavioral information for one week includes behavioral information "sleep" with a duration of less than six hours three or more times."

[0663] (Step S4813) The score acquisition unit 732 judges whether or not the time-series information satisfies the j-th overall condition. If the j-th overall condition is satisfied, the process proceeds to step S4814, and if not, the process proceeds to step S4815.

[0664] (Step S4814) The score acquisition unit 732 adds γ to the disorder score. Note that γ may be a fixed positive number, or may be a positive number that differs depending on the element information.

[0665] (Step S4815) The score obtaining unit 732 increments the counter j by 1. The process returns to step S4812.

[0666] Next, an example of the cause acquisition process in step S4507 will be described with reference to the flowchart in FIG.

[0667] (Step S4901) The cause acquiring unit 735 assigns 1 to a counter i.

[0668] (Step S4902) The factor acquiring unit 735 judges whether or not the i-th element information exists in the time-series information. If the i-th element information exists, the process proceeds to step S4903, and if not, the process returns to the upper level process.

[0669] (Step S4903) The factor acquiring unit 735 acquires element information contained in the reference information corresponding to the i-th element information. The factor acquiring unit 735 acquires the difference between the two pieces of element information.

[0670] (Step S4904) The cause acquiring unit 735 judges whether or not the difference acquired in step S4903 satisfies the adoption condition. If the adoption condition is satisfied, the process proceeds to step S4905, and if not, the process proceeds to step S4906.

[0671] (Step S4905) The factor acquiring unit 735 temporarily stores the i-th element information in a buffer (not shown). The i-th element information is information constituting a disturbance factor.

[0672] (Step S4906) The cause obtaining unit 735 increments the counter i by 1. The process returns to step S4902.

[0673] Next, an example of the recommendation acquisition process in step S4508 will be described with reference to the flowchart in FIG.

[0674] (Step S5001) The recommendation obtaining unit 736 assigns 1 to a counter i.

[0675] (Step S5002) The recommendation acquisition unit 736 judges whether or not the i-th disturbance factor exists in the buffer (not shown) in which the disturbance factors temporarily accumulated in step S4905 are stored. If the i-th disturbance factor exists, the process proceeds to step S5004, and if not, the process returns to the upper process.

[0676] (Step S5003) The recommendation acquiring unit 736 acquires element information corresponding to the i-th disturbance factor.

[0677] (Step S5004) The recommendation obtaining unit 736 assigns 1 to a counter j.

[0678] (Step S5005) The recommendation acquiring unit 736 determines whether or not the jth factor condition exists in the recommendation managing unit 712. If the jth factor condition exists, the process proceeds to step S5006, and if not, the process proceeds to step S5010.

[0679] (Step S5006) The recommendation acquisition unit 736 judges whether or not the element information acquired in step S5003 satisfies the j-th factor condition. If the element information satisfies the j-th factor condition, the process proceeds to step S5007. If the element information does not satisfy the j-th factor condition, the process proceeds to step S5009.

[0680] (Step S5007) The recommendation acquiring unit 736 acquires recommendation source information paired with the j-th factor condition from the recommendation managing unit 712.

[0681] (Step S5008) The recommendation acquiring unit 736 configures recommendation information using the recommendation source information and the element information acquired in step S5003, and temporarily accumulates the recommendation information in a buffer (not shown).

[0682] (Step S5009) The recommendation obtaining unit 736 increments the counter j by 1. The process returns to step S5005.

[0683] (Step S5010) The recommendation obtaining unit 736 increments the counter i by 1. The process returns to step S5002.

[0684] Next, an example of the long-term score acquisition process in step S4522 will be described with reference to the flowchart in FIG.

[0685] (Step S5101) The long-term score acquiring unit 733 acquires, from the behavior management unit 313, all disorder scores for a predetermined period that are paired with the user identifier of the target user.

[0686] (Step S5102) The long-term score acquiring unit 733 detects a disorder score that matches the disorder condition among the disorder scores acquired in step S5101, and acquires the number of the disorder scores. The disorder score is the number of times the user's behavior is disordered in a predetermined period of time.

[0687] (Step S5103) The long-term score acquiring unit 733 acquires a long-term disorder score using all the disorder scores and the number of disorder scores that match the disorder condition. The process returns to the upper level process. Note that the long-term score acquiring unit 733 acquires a larger long-term disorder score as the disorder score is larger and as the number of disorder scores that match the disorder condition is larger.

[0688] Next, an example of the learning process in step S4528 will be described with reference to the flowchart in FIG.

[0689] (Step S5201) The learning unit 731 assigns 1 to a counter i.

[0690] (Step S5202) The learning unit 731 judges whether or not the i-th time-series information to be learned exists in the behavior management unit 313. If the i-th time-series information exists, the process proceeds to step S5203, and if not, the process proceeds to step S5208.

[0691] (Step S5203) The learning unit 731 acquires the i-th time-series information from the behavior management unit 313.

[0692] (Step S5204) The learning unit 731 constructs a vector from the i-th time-series information. Such a vector is called an explanatory variable vector.

[0693] (Step S5205) The learning unit 731 acquires disturbance information corresponding to the i-th time-series information. Note that the disturbance information is stored in the behavior management unit 313, for example, in a pair with the time-series information.

[0694] (Step S5206) The learning unit 731 acquires teacher data having explanatory variable vectors and disturbance information, and temporarily stores the teacher data in a buffer (not shown).

[0695] (Step S5207) The learning unit 731 increments the counter i by 1. The process returns to step S5202.

[0696] (Step S5208) The learning unit 731 provides two or more pieces of teacher data in a buffer (not shown) to a module that performs machine learning learning processing, executes the module, and obtains a learning model. The process returns to the upper level process.

[0697] A specific example of the operation of the information system F in this embodiment will be described below.

[0698] It is now assumed that the behavior management unit 313 of the behavior analysis device 7 stores a time zone information management table shown in FIG. 53. The time zone information management table is a table that manages behavior information for each time zone of one or more users. The time zone information management table has one or more records having "ID", "date", "time zone", "behavior information", and "disorder score". For convenience, FIG. 53 is a table that manages behavior information of one user (user U). It is also assumed here that the disorder score is acquired for the time zone from waking up to before the next wake-up (on a daily basis).

[0699] 53 is information accumulated in the behavior management unit 313 by the behavior acquisition device 5. Each record in FIG. 53 may be information input by the user U.

[0700] It is also assumed that a reference vector is stored in reference management unit 711 of behavior analysis apparatus 7. The reference vector may be a self reference vector, an other-person reference vector, or a recommended behavior vector.

[0701] The recommendation management unit 712 also stores a recommendation management table shown in Fig. 54. The recommendation management table is a table having one or more factor conditions and recommendation source information paired with the factor conditions.

[0702] In this situation, two specific examples will be described below. Specific Example 1 is a case where a disorder score and recommendation information are output. Specific Example 2 is a case where an improvement degree and a recovery period are output.

[0703] (Example 1) It is assumed that the reception unit 72 of the behavior analysis device 7 has received time-series information, which is information from waking up to sleeping (until the next wake-up) including the set of date, time period, and behavioral information of "ID=538-548" in Fig. 53, paired with the user identifier of user U. Then, the processing unit 73 determines that the score acquisition condition (receiving time-series information of the time period from waking up to the next wake-up) is met.

[0704] Next, it is assumed that the score acquiring unit 732 acquires a disorder score of "6" for the user U by the above-mentioned score acquiring process (see FIG. 46, FIG. 47, or FIG. 48) using the accepted time-series information of the user U. Here, it is assumed that the disorder score can take any rank from "0" to "10." It is also assumed that the disorder condition is "disorder score >= 5." Next, the score output unit 741 associates the disorder score "6" with the behavioral information, etc., and accumulates it as an attribute value of the "disorder score" in FIG. 53.

[0705] Next, the determination unit 734 determines that the acquired disorder score "6" matches the disorder condition "disorder score >= 5".

[0706] Next, the factor acquiring unit 735 acquires the time period "0:30-5:00", the behavioral information "sleep", and the reference information corresponding to "sleep" in the reference information of the reference management unit 711 (for example, the time difference of "1.5 hours" from "<time> 6 hours to 10 hours <start time> until 23:00" and the start time difference of "1.5 hours" totaling "3 hours". Then, the factor output unit 743 temporarily accumulates the time period "0:30-5:00", the behavioral information "sleep", and the difference "3 hours" in a buffer not shown. In other words, the factor acquiring unit 735 acquires the disturbance factor "sleep".

[0707] Next, the recommendation acquisition unit 736 acquires recommendation information. It is determined that the information temporarily stored in a buffer (not shown) by the factor output unit 743 satisfies the factor condition of "ID=1" in FIG. 54. The recommendation acquisition unit 736 acquires recommendation source information of "ID=1" in FIG. 54. The recommendation acquisition unit 736 also acquires element information "<behavioral information> sleep <time period> 0:30-5:00" that pairs with the disturbance factor "sleep". The recommendation acquisition unit 736 assigns the acquired element information "<behavioral information> sleep <time period> 0:30-5:00" to the variable <element information> of the recommendation source information to configure recommendation information.

[0708] Next, the processing unit 73 composes output information having a disorder score of "6" and recommendation information.

[0709] Next, the output unit 74 transmits the constructed output information to the user U. The output unit 74 transmits the output information to, for example, an email address of the user U. The output unit 74 outputs the output information on, for example, a mobile application of the terminal device 8 of the user.

[0710] Then, the terminal device 8 of the user U outputs, for example, the disorder score and recommendation information as shown in FIG.

[0711] (Example 2) Next, it is assumed that one week has passed since the disorder score "6". Then, for each day of that week, the score acquisition unit 732 acquires disorder scores of "5", "4", "3", "3", "2", "2", and "0" for each day using the accumulated time-series information, etc. Then, it is assumed that the score output unit 741 accumulates disorder scores of "5", "4", "3", "3", "2", "2", and "0" in association with the user U after the disorder score "6", respectively.

[0712] Then, the determining unit 734 determines that the improvement output condition "after the disturbance condition is satisfied, the disturbance score becomes 0" is satisfied.

[0713] Next, the improvement degree acquisition unit 737 acquires a disturbance score of "6" when the disturbance condition is met from a situation where the disturbance condition is not met, and a disturbance score of "0" when the improvement degree output condition is met, thereby acquiring an improvement degree of "6-0=6".

[0714] In addition, the judgment unit 734 judges that the disturbance score "6" obtained when the disturbance condition is met and the recent disturbance score "0" satisfy the recovery condition (the disturbance score becoming "0" after the disturbance condition is met).

[0715] Next, the recovery period acquisition section 738 acquires the recovery period "7 days", which is the difference between the date "10 / 20" included in the time information corresponding to the disorder score "6" and the date "10 / 27" included in the time information corresponding to the disorder score.

[0716] Next, the processing unit 73 obtains the disorder score "0", the improvement degree "6", and the recovery period "7 days", and configures output information having the information. The output information has the disorder score, the improvement degree, and the recovery period.

[0717] Next, the output unit 74 transmits the constructed output information to the user U. The output unit 74 transmits the output information to, for example, an email address of the user U. The output unit 74 outputs the output information on, for example, a mobile application of the terminal device 8 of the user.

[0718] Then, the terminal device 8 of the user U outputs the disorder score, the degree of improvement, and the recovery period, for example, as shown in FIG.

[0719] As described above, according to this embodiment, a disorder score, which is the degree of disorder in the user's behavior, can be obtained. Note that, according to this embodiment, for example, the disorder score can be obtained by using self-reference information. Also, according to this embodiment, for example, the disorder score can be obtained by a machine learning prediction process. Also, according to this embodiment, for example, the disorder score can be obtained by using one or more of recommended behavior information and other-reference information. Also, according to this embodiment, for example, the disorder score can be obtained by using environmental information as well.

[0720] Moreover, according to this embodiment, the cause of the disturbance in the user's behavior can be acquired.

[0721] Moreover, according to this embodiment, recommendations can be made to improve irregular user behavior.

[0722] Furthermore, according to the present embodiment, the degree of improvement in the user's irregular behavior can be obtained. Note that, according to the present embodiment, the degree of improvement in the user's irregular behavior can be output at an appropriate timing when the improvement output condition is satisfied.

[0723] As described above, behavior analysis apparatus 7 in this embodiment may be a terminal. A block diagram in the case where behavior analysis apparatus 7 is a terminal is shown in FIG. 57. This behavior analysis apparatus becomes behavior analysis apparatus 9. In other words, behavior analysis apparatus 9 has the functions of behavior acquisition apparatus 3.

[0724] Moreover, the software for realizing the behavior analysis device 7 in this embodiment is the following program. In other words, this program is a program for causing a computer that can access a reference management unit in which reference information serving as a basis for acquiring the degree of disorder in a user's behavior to function as: a reception unit that receives time-series information including two or more pieces of time-series behavior information that is information associated with time information that identifies the time when the user performed a behavior and is information that identifies the behavior of the user; a score acquisition unit that acquires a disorder score that is the degree of disorder in the user's behavior using the time-series information received by the reception unit and the reference information of the reference management unit; and a score output unit that outputs the disorder score acquired by the score acquisition unit.

[0725] FIG. 58 is a block diagram of a computer system 300 that executes the programs described in this specification to realize the behavior analysis device 7 and the like according to the various embodiments described above.

[0726] In FIG. 58, computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0727] 58, computer 301 includes, in addition to CD-ROM drive 3012, MPU 3013, bus 3014 connected to CD-ROM drive 3012 etc., ROM 3015 for storing programs such as a boot-up program, RAM 3016 connected to MPU 3013 for temporarily storing instructions of application programs and providing temporary storage space, and hard disk 3017 for storing application programs, system programs, and data. Although not shown here, computer 301 may further include a network card for providing connection to a LAN.

[0728] A program for causing computer system 300 to execute functions such as those of behavior analysis apparatus 7 of the above-mentioned 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 transmitted 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.

[0729] The program does not necessarily include an operating system (OS) that causes computer 301 to execute the functions of behavior analysis device 7 of the above-mentioned embodiment, or a third-party program. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner and produces desired results. How computer system 300 operates is well known, and a detailed description will be omitted.

[0730] 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 transmitting step (processing that is performed only by hardware).

[0731] The program may be executed by a single computer or a plurality of computers. That is, the program may be executed by a centralized processing or a distributed processing.

[0732] 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.

[0733] 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.

[0734] 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. [Industrial Applicability]

[0735] INDUSTRIAL APPLICABILITY As described above, the behavior analysis device according to the present invention has the effect of being able to obtain a disorder score, which is the degree of disorder in a user's behavior, and is useful as a behavior analysis device, etc. [Explanation of symbols]

[0736] F. Information Systems 7, 9 Behavior analysis device 71 Storage area 72 Reception 73 Processing section 74 Output section 54 Transmitter 711 Standards Management Department 712 Recommendation Management Department 731 Learning Department 732 Score Acquisition Department 733 Long-term score acquisition section 734 Judgment Department 735 Factor Acquisition Department 736 Recommendation Acquisition Department 737 Improvement degree acquisition part 738 Recovery Period Acquisition Department 741 Score Output Unit 742 Long-term score output section 743 Factor Output Unit 744 Recommendation Output Unit 745 Improvement level output section 746 Recovery Period Output Unit

Claims

1. A reception unit that receives time series information including two or more pieces of time-series behavior information that is information corresponding to time information that specifies a time when a user performed an action and is information that specifies the action of the user; a reference management unit that stores reference information that is used to acquire a degree of disorder of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disorder score, which is a degree of disorder in the user's behavior, and which is information regarding a difference between the time-series information accepted by the acceptance unit and the reference information of the reference management unit; a score output unit that outputs the disturbance score acquired by the score acquisition unit, A determination unit that determines whether the disorder score satisfies a disorder condition; a factor acquisition unit that acquires, when the determination unit determines that the disturbance condition is satisfied, a disturbance factor related to the behavioral information that is a factor of the disturbance from the behavioral information included in the time-series information accepted by the acceptance unit that corresponds to the disturbance score; a factor output unit that outputs the disturbance factor acquired by the factor acquisition unit.

2. A reception unit that receives time series information including two or more pieces of time-series behavior information that is information corresponding to time information that specifies a time when a user performed an action and is information that specifies the action of the user; a reference management unit that stores reference information that is used to acquire a degree of disorder of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disorder score, which is a degree of disorder in the user's behavior, and which is information regarding a difference between the time-series information accepted by the acceptance unit and the reference information of the reference management unit; a score output unit that outputs the disturbance score acquired by the score acquisition unit, The score acquisition unit, Obtaining a first disturbance score and a second disturbance score, which are disturbance scores in each of the two time periods; an improvement degree acquiring unit that acquires an improvement degree, which is information regarding a difference between the first disturbance score and the second disturbance score and is information specifying a degree of improvement of the disturbance; and an improvement degree output unit that outputs the degree of improvement acquired by the improvement degree acquisition unit.

3. A reception unit that receives time series information including two or more pieces of time-series behavior information that is information corresponding to time information that specifies a time when a user performed an action and is information that specifies the action of the user; a reference management unit that stores reference information that is used to acquire a degree of disorder of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disorder score, which is a degree of disorder in the user's behavior, and which is information regarding a difference between the time-series information accepted by the acceptance unit and the reference information of the reference management unit; a score output unit that outputs the disturbance score acquired by the score acquisition unit, The score acquisition unit, Obtaining a first disturbance score and a second disturbance score, which are disturbance scores in each of the two time periods; a determination unit that determines whether the first disorder score and the second disorder score satisfy a recovery condition; a recovery period acquisition unit that acquires a recovery period that is a difference between first time information corresponding to the first disturbance score and second time information corresponding to the second disturbance score when the judgment unit judges that the recovery condition is satisfied; a recovery period output unit that outputs the recovery period acquired by the recovery period acquisition unit.

4. A reception unit that receives time series information including two or more pieces of time-series behavior information that is information corresponding to time information that specifies a time when a user performed an action and is information that specifies the action of the user; a reference management unit that stores reference information that is used to acquire a degree of disorder of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disorder score, which is a degree of disorder of the user's behavior, and which is information regarding a difference between the time-series information accepted by the acceptance unit and the reference information of the reference management unit; The reception unit is Accepts two or more time series information, The score acquisition unit, Obtain a disturbance score for each of the two or more pieces of time-series information; a long-term score acquiring unit that acquires a long-term disorder score that specifies a degree of long-term behavior disorder using two or more of the disorder scores acquired by the score acquiring unit; The behavioral analysis device further comprises a long-term score output unit that outputs the long-term disorder score acquired by the long-term score acquisition unit.

5. A reception unit that receives time series information including two or more pieces of time-series behavior information that is information corresponding to time information that specifies a time when a user performed an action and is information that specifies the action of the user; a reference management unit that stores reference information that is used to acquire a degree of disorder of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disorder score, which is a degree of disorder in the user's behavior, and which is information regarding a difference between the time-series information accepted by the acceptance unit and the reference information of the reference management unit; a score output unit that outputs the disturbance score acquired by the score acquisition unit, A time acquisition unit that acquires the time; a location acquisition unit that acquires location information corresponding to the time; and a behavior estimation unit that acquires behavior information that identifies the behavior of the user in a time period that is identified by the time contained in each of the two or more pieces of behavior source information, using the two or more pieces of behavior source information including the location information associated with the time, The reception unit is A behavior analysis device that receives the behavior information for each of two or more time periods acquired by the behavior estimation unit.

6. A reception unit that receives time series information including two or more pieces of time-series behavior information that is information corresponding to time information that specifies a time when a user performed an action and is information that specifies the action of the user; a reference management unit that stores reference information that is used to acquire a degree of disorder of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disorder score, which is a degree of disorder in the user's behavior, and which is information regarding a difference between the time-series information accepted by the acceptance unit and the reference information of the reference management unit; a score output unit that outputs the disturbance score acquired by the score acquisition unit, A behavior analysis device, wherein the time series information and the reference information include environmental information that specifies the environment of the behavior location.

7. The score acquisition unit, Obtaining difference information regarding a difference between the time-series information and the reference information, and obtaining a larger disturbance score as the difference information is larger. Alternatively, a machine learning prediction process is performed using the reference information, which is a learning model acquired by performing a machine learning learning process using two or more teacher data having explanatory variables based on the user's past time-series information and a target variable which is disorder information related to the degree of disorder in the user's behavior, and the time-series information, to obtain the disorder score. The behavioral analysis device according to claim 1 .

8. The behavior analysis device according to claim 1 , wherein the reference information includes self-reference information that is information based on past time-series information of the user.

9. The behavioral analysis device according to claim 1 , wherein the standard information includes one or more types of information among recommended behavior information that identifies recommended or non-recommended behavior, and other standard information based on behavioral information of one or more other persons other than the user.

10. a recommendation acquisition unit that refers to a recommendation management unit in which recommendation source information that is a source of information for improving the disturbance is stored in association with each of two or more factor conditions that are conditions related to a disturbance factor, and acquires recommendation information using the recommendation source information that corresponds to the factor condition that matches the disturbance factor acquired by the factor acquisition unit; The behavior analysis device according to claim 1 , further comprising: a recommendation output unit that outputs the recommendation information acquired by the recommendation acquisition unit.

11. The improvement degree acquisition unit, The behavior analysis device according to claim 2 , wherein the improvement degree is obtained when the first disorder score and the second disorder score satisfy an improvement degree output condition.

12. A determination unit that determines whether each of the two or more disorder scores satisfies a disorder condition, The long-term score acquisition unit 5. The behavior analysis device according to claim 4, wherein the determination unit obtains the number of times that it has been determined that the disorder condition is satisfied, and obtains the long-term disorder score using the number of times.

13. a receiving unit that receives radio waves including a device identifier for identifying each of the three or more communication devices from the communication devices; a signal strength acquisition unit that acquires a time series of radio wave strength 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 or a mobile terminal using the time-series radio wave intensity acquired by the intensity acquisition unit, The position acquisition unit is The behavior analysis device according to claim 5 , wherein the type determination unit acquires a terminal location, which is location information of a terminal device held by the user, by using radio wave intensity of three or more of the communication devices that the type determination unit determines to be fixed terminals.

14. A behavioral analysis method comprising all steps performed by the behavioral analysis device according to any one of claims 1 to 6.

15. Computer, A program for causing the behavioral analysis device according to any one of claims 1 to 6 to function as such.

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