Behavior analysis device, behavior analysis method and program

Abstract

To address such a problem that previously, it is not possible to acquire a degree of disturbance of behavior of a user.SOLUTION: A behavior analysis device 7 includes: a receiving unit 72 that receives time-series information including two or more pieces of behavior information in time series, the information associated with time information for specifying a time when a user has behaved and the information for specifying the behavior of the user; a reference management unit 711 that stores the reference information, the source for acquiring the degree of disturbance of the behavior of the user corresponding to the time-series information; a score acquisition unit 732 that acquires a disturbance score, the degree of disturbance of the behavior of the user using the time-series information received by the receiving unit 72 and the reference information of the reference management unit 711; and a score output unit 741 that outputs the disturbance score acquired by the score acquisition unit 732. The behavior analysis device can thus acquire the disturbance score, the degree of disturbance of the behavior of the user.SELECTED DRAWING: Figure 44

Description

Technical Field

[0001] The present invention relates to a behavior analysis apparatus that acquires the degree of disruption of a user's behavior, and the like.

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the prior art, the degree of disruption of a user's behavior could not be acquired.

Means for Solving the Problems

[0005] The behavior analysis apparatus according to the first invention of the present invention includes a reception unit that receives time-series information including two or more pieces of behavior information in time series, which is information corresponding to time information for specifying when a user has behaved and is information for specifying the user's behavior; a reference management unit that stores reference information serving as a basis for acquiring the degree of disruption of the user's behavior corresponding to the time-series information; a score acquisition unit that acquires a disruption score, which is the degree of disruption of 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 disruption score acquired by the score acquisition unit.

[0006] With such a configuration, a disruption score, which is the degree of disruption of a user's behavior, can be acquired.

[0007] Further, the behavior analysis device of the second invention is a behavior analysis device that, with respect to the first invention, includes self-reference information, which is information based on the user's past time-series information, as the reference information.

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

[0009] Further, the behavior analysis device of the third invention is a behavior analysis device that, with respect to the second invention, uses two or more pieces of teacher data having, as the reference information, an explanatory variable based on the user's past time-series information and a target variable that is disturbance information related to the degree of disturbance of the user's behavior, and performs learning processing of machine learning to obtain a learned model. The score acquisition unit performs prediction processing of machine learning using the time-series information received by the reception unit and the learned model, and obtains a disturbance score.

[0010] With such a configuration, a disturbance score can be obtained by prediction processing of machine learning.

[0011] Further, the behavior analysis device of the fourth invention is a behavior analysis device that, with respect to any one of the first to third inventions, includes at least one type of information among recommended behavior information that identifies recommended or non-recommended behavior and other-reference information based on the behavior information of one or more other persons other than the user, as the reference information.

[0012] With such a configuration, a disturbance score can be obtained using at least one type of recommended behavior information and other-reference information.

[0013] Further, the behavior analysis device of the fifth invention is a behavior analysis device that, with respect to any one of the first to fourth inventions, includes environmental information that identifies the environment of the behavior location, as the user's time-series behavior information and the reference information.

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

[0015] In addition, the behavior analysis apparatus of the sixth invention further includes, for any one of the first to fifth inventions, a determination unit that determines whether the disorder score satisfies the disorder condition, and when the determination unit determines that the disorder condition is satisfied, a factor acquisition unit that acquires a disorder factor related to the behavior information that is the cause of the disorder from the behavior information included in the time-series information received by the reception unit corresponding to the disorder score, and a factor output unit that outputs the disorder factor acquired by the factor acquisition unit.

[0016] With such a configuration, the factors causing the disorder of the user's behavior can be acquired.

[0017] In addition, the behavior analysis apparatus of the seventh invention further includes, with respect to the sixth invention, a recommendation management unit that stores recommendation source information that serves as the basis for information for improving the disorder, in association with each of two or more factor conditions that are conditions related to the disorder factor, and a recommendation acquisition unit that acquires recommendation information using the recommendation source information associated with the factor condition that matches the disorder factor acquired by the factor acquisition unit, and a recommendation output unit that outputs the recommendation information acquired by the recommendation acquisition unit.

[0018] With such a configuration, recommendations for improving the disorder of the user's behavior can be made.

[0019] In addition, the behavior analysis apparatus of the eighth invention further includes, for any one of the first to seventh inventions, a score acquisition unit that acquires a first disorder score and a second disorder score that are disorder scores at two respective times, and an improvement degree acquisition unit that acquires an improvement degree that is information regarding the difference between the first disorder score and the second disorder score and that specifies 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 such a configuration, the degree of improvement of the disorder of the user's behavior can be acquired.

[0021] Further, in the behavior analysis apparatus of the ninth invention, for the eighth invention, when the first disturbance score and the second disturbance score satisfy the improvement degree output condition, the improvement degree acquisition unit is a behavior analysis apparatus that acquires the improvement degree.

[0022] With such a configuration, it is possible to output the improvement degree of the disturbance of the user's behavior at an appropriate timing.

[0023] Further, in the behavior analysis apparatus of the tenth invention, for any one of the first to ninth inventions, the score acquisition unit acquires the first disturbance score and the second disturbance score, which are the disturbance scores at two respective times, and determines whether the first disturbance score and the second disturbance score satisfy the recovery condition. A determination unit, and when the determination unit determines that the recovery condition is satisfied, a recovery period acquisition unit that acquires a recovery period, which is the difference between the first time information associated with the first disturbance score and the second time information associated with the second disturbance score, and a recovery period output unit that outputs the recovery period acquired by the recovery period acquisition unit. It is a behavior analysis apparatus further comprising.

[0024] With such a configuration, it is possible to acquire the recovery period from the disturbance of the user's behavior.

[0025] Further, in the behavior analysis apparatus of the eleventh invention, for any one of the first to tenth inventions, the reception unit receives two or more time-series information, and the score acquisition unit acquires the disturbance scores for the two or more time-series information, and uses the two or more disturbance scores acquired by the score acquisition unit. It is a behavior analysis apparatus further comprising a long-term score acquisition unit that acquires a long-term disturbance score that specifies the degree of long-term behavior disturbance, and a long-term score output unit that outputs the long-term disturbance score acquired by the long-term score acquisition unit.

[0026] With such a configuration, it is possible to acquire a long-term disturbance score, which is the degree of long-term behavior disturbance of the user.

[0027] Further, the behavior analysis apparatus of the twelfth invention further includes a determination unit that determines whether or not each of two or more disturbance scores satisfies a disturbance condition with respect to the eleventh invention. The long-term score acquisition unit is a behavior analysis apparatus that acquires the number of times the determination unit determines that the disturbance condition is satisfied, and uses the number of times to acquire a long-term disturbance score.

[0028] With such a configuration, it is a long-term disturbance score that represents the degree of disturbance of the user's long-term behavior, and an appropriate long-term disturbance score can be obtained.

[0029] Further, the behavior analysis apparatus of the thirteenth invention further includes a time acquisition unit that acquires time, a position acquisition unit that acquires position information associated with the time, and behavior estimation that acquires behavior information that specifies the user's behavior in a time zone specified by the time included in two or more pieces of behavior source information including the position information associated with the time with respect to any one of the first to twelfth inventions. And a reception unit that receives the behavior information in each of two or more time zones acquired by the behavior estimation unit.

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

[0031] Further, the behavior analysis apparatus of the fourteenth invention further includes a reception unit that receives radio waves including device identifiers that identify communication devices from three or more communication devices with respect to the thirteenth invention, and for each of the three or more communication devices, a strength acquisition unit that acquires time-series radio wave intensities, and a type determination unit that determines whether each of the three or more communication devices is a fixed terminal fixed or a mobile terminal moving using the time-series radio wave intensities acquired by the strength acquisition unit. The position acquisition unit is a behavior analysis apparatus that acquires a terminal position, which is position information of the terminal device, using the radio wave intensities of the three or more communication devices determined by the type determination unit to be fixed terminals.

[0032] With such a configuration, the position of the terminal device indoors or the like can be easily acquired.

Advantages of the Invention

[0033] According to the behavior analysis device of the present invention, a disturbance score, which is the degree of disturbance of the user's behavior, can be obtained.

Brief Description of Drawings

[0034]

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Modes for Carrying Out the Invention

[0035] Hereinafter, embodiments of a motion analysis device and the like will be described with reference to the drawings. In the embodiments, components denoted by the same reference numerals perform the same operations, and thus the description thereof 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 acquired from information X, or information X can be acquired from information Y, and the method of association is not limited. 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] Also, in this specification, selecting or determining information Z means acquiring information Z, acquiring a pointer to information Z, acquiring the ID of information Z, setting a flag for information Z, etc., as long as information Z can be accessed.

[0039] FIG. 1 is a conceptual diagram of an information system A including a location information production device 1 in this embodiment. The information system A includes a 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 for identifying the communication device B to other devices. The communication device B is, for example, a Wi-fi router or a communication device using BLE (Bluetooth Low Energy), but it is not limited.

[0041] Figure 2 is a block diagram of the location information production device 1 in the present embodiment. The location information production device 1 includes a storage unit 11, a reception unit 12, a receiving unit 13, and a processing unit 14. The reception unit 12 includes a position reception unit 121. The processing unit 14 includes an intensity acquisition unit 141, a type determination unit 142, and a storage unit 143.

[0042] The reception unit 12 receives various instructions and information. The various instructions and information are, for example, the location information described later. The input means for the various instructions and information can be anything, such as a touch panel, a keyboard, a mouse, or a menu screen.

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

[0044] The specific location is a specific location indoors, but it can also be a specific location outdoors. Here, the position information is information that specifies a position indoors or outdoors. The position information is, for example, three-dimensional coordinate values (x, y, z) that indicate a relative position indoors or outdoors, but two-dimensional coordinate values (x, y) are also acceptable. Note that the origin of the coordinate values for specifying the relative position indoors or outdoors is not limited. The specific location outdoors is preferably a location where GPS signals are difficult to reach, such as among high-rise buildings or in the middle of a forest, but it is not limited. The position information can also be information (such as a character string) or an ID that a person can recognize a place. Such position information can also be labels such as "living room", "workroom", "meeting room", "east side of the library", "toy section of the department store", etc.

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

[0046] The position reception unit 121 does not necessarily have to receive position information. In such a case, the position reception unit 121 is unnecessary.

[0047] The receiving unit 13 receives radio waves including device identifiers from three or more communication devices B at a specific location. The receiving unit 13 usually continuously receives radio waves including device identifiers from three or more communication devices B.

[0048] The device identifier is information for identifying 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. It can be considered that the reception of radio waves is the reception of information.

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

[0050] The intensity acquisition unit 141 acquires the intensity of the radio waves received from each of the three or more communication devices B for each of the three or more communication devices B. The intensity acquisition unit 141 acquires the radio wave intensity in pairs with the device identifier of the communication device B. The intensity acquisition unit 141 acquires the radio wave intensity in time series. The radio wave intensity in time series is two or more radio wave intensities that are continuous in time. It goes without saying that being continuous in time may have a time interval.

[0051] The type determination unit 142 uses the radio wave intensity in time series acquired by the intensity acquisition unit 141 to determine whether each of the three or more communication devices B is a fixed terminal or a mobile terminal. A fixed terminal is a communication device whose installation position is fixed. A mobile terminal is a communication device whose installation position is not fixed and is moving.

[0052] The type determination unit 142, for example, acquires the degree of variation of two or three or more radio wave intensities that are continuous in time and paired with one device identifier, and if the degree of variation is greater than or equal to a threshold value, determines that the communication device B identified by the one device identifier is a mobile terminal. Also, the type determination unit 142, for example, acquires the degree of variation of two or three or more radio wave intensities that are continuous in time and paired with one device identifier, and if the degree of variation is less than or equal to a threshold value, determines that the communication device B identified by the one device identifier is a fixed terminal.

[0053] Note that the degree of variation indicates information regarding the variation or change in the radio wave intensity in a time series. The degree of variation is, for example, a variance, a standard deviation, or a number based on a difference (for example, a difference, or a value obtained by adding the differences between two consecutive radio wave intensities among three or more continuously time-series radio wave intensities).

[0054] The type determination unit 142 is, for example, the number of radio wave intensities acquired within a predetermined time. When the number of time-series, temporally consecutive radio wave intensities paired with a single device identifier is equal to or less than a threshold value, the communication device B identified by the single device identifier is determined to be a mobile terminal.

[0055] The storage unit 143 constructs and stores location information having the device identifier of the communication device B determined by the type determination unit 142 to be a fixed terminal and the radio wave intensity. It is preferable for the storage unit 143 to construct and store location information having the device identifier, the radio wave intensity, and the location information for each of three or more communication devices B.

[0056] The storage unit 143 constructs and stores location information having the device identifier of the communication device B determined by the type determination unit 142 to be a fixed terminal, the radio wave intensity, and the position information of a specific location. It is preferable for the storage unit 143 to construct and store location information having the device identifier, the radio wave intensity, and the position information of a specific location for each of three or more communication devices B. The storage unit 143 stores the location information in the storage unit 11, for example, but it may be stored in another device. The location information preferably has position information, but it may not have position information. The location information may be composed only of the device identifier and the radio wave intensity.

[0057] The radio wave intensity stored by the storage unit 143 is usually a representative value of the time series of radio waves 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 it can also be realized with a volatile recording medium.

[0059] The process of storing information in the storage unit 11 is not limited. For example, information may be stored in the storage unit 11 via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 11, or information input via an input device may be stored in the storage unit 11.

[0060] The reception unit 12 and the position reception unit 121 can be realized by a device driver of an input means such as a touch panel or a keyboard, control software of a menu screen, or the like.

[0061] The reception unit 13 is usually realized by 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 from a processor, a memory, and the like. The processing procedures of the processing unit 14 and the like are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (a dedicated circuit). Note that the processor is a CPU, an MPU, a GPU, or the like, and its type is not limited.

[0063] Next, an operation example of the location information production device 1 will be described using the flowchart of FIG. 3.

[0064] (Step S301) The position reception unit 121 determines whether or not it has received the position information of a specific location. If it has received the position information, it proceeds to step S302, and if it has not received it, it returns to step S301.

[0065] (Step S302) The storage unit 143 acquires the position information received 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 of acquiring the time-series radio wave intensities of radio waves from three or more communication devices B. An example of the time-series intensity acquisition process will be described using the flowchart of FIG. 4.

[0067] (Step S304) The processing unit 14 etc. perform fixed information acquisition processing. Return to step S301. The fixed information acquisition processing is processing for acquiring the intensity of radio waves from the fixed terminal at the point specified by the position information received in step S301. An example of the fixed information acquisition processing will be described using the flowchart of FIG. 5.

[0068] Note that in the flowchart of FIG. 3, it is preferable that the user who holds the location information production device 1 moves to each of three or more specific locations, and the location information production device 1 receives the position information for each of the three or more specific locations and repeatedly performs the processing from S301 to S304.

[0069] Also, in the flowchart of FIG. 3, the processing ends due to an interrupt such as power-off or end of processing.

[0070] Next, an example of the time-series intensity acquisition processing in step S303 will be described using the flowchart of FIG. 4.

[0071] (Step S401) The receiving unit 13 determines whether it has received radio waves from any of the communication devices B. If radio waves are received, proceed to step S402, and if not received, return to step S401.

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

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

[0074] (Step S404) The storage unit 143 appends 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 storage unit 143 determines whether or not it meets the storage conditions for the location information. If it meets the storage conditions, it returns to the upper-level process. If it does not meet the conditions, it returns to Step S401. Note that the storage conditions are, for example, that a threshold time or more has elapsed since receiving the position information of a specific location, or that the number of radio wave intensities equal to or greater than a threshold value paired with three or more device identifiers has been stored, etc.

[0076] Next, an example of the fixed information acquisition process in Step S304 will be described using the flowchart of FIG. 5.

[0077] (Step S501) The type determination unit 142 substitutes 1 for the counter i.

[0078] (Step S502) The type determination unit 142 determines whether or not the i-th device identifier exists in a buffer (not shown). If the i-th device identifier exists, it proceeds to Step S503. If the i-th device identifier does not exist, it returns to the upper-level 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 using the flowchart of FIG. 6.

[0080] (Step S504) If the determination result in Step S503 is "fixed terminal", it proceeds to Step S505. If it is "mobile terminal", it proceeds to Step S508.

[0081] (Step S505) The storage unit 143 acquires two or more radio wave intensities paired with the i-th device identifier from a buffer (not shown).

[0082] (Step S506) The storage unit 143 acquires a representative value of the two or more radio wave intensities.

[0083] (Step S507) The storage unit 143 stores, in the storage unit 11, a pair of the i-th device identifier and the representative value of the radio wave intensity acquired in step S506, in association with the position information received in step S301.

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

[0085] Note that in the flowchart of FIG. 5, in step S506, the storage unit 143 may newly acquire the intensity of the latest radio wave instead of the representative values of two or more radio wave intensities.

[0086] Next, an example of the type determination process in step S503 will be described using the flowchart of FIG. 6.

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

[0088] (Step S602) The type determination unit 142 acquires the degree of variation of 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 the threshold or less than the threshold. If the degree of variation is equal to or less than the threshold or less than the threshold, it proceeds to step S604; if it is equal to or greater than the threshold or greater than the threshold, it proceeds to step S605.

[0090] (Step S604) The type determination unit 142 sets the type of the communication device B as a "fixed terminal". It returns to the upper-level process.

[0091] (Step S605) The type determination unit 142 sets the type of the communication device B as a "mobile terminal". It returns to the upper-level process.

[0092] Hereinafter, a specific operation example of the location information production device 1 in the present embodiment will be described. Here, it is assumed that the accumulation condition is that a predetermined time (for example, 3 minutes) has elapsed since the position information of a specific location was received.

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

[0094] Next, the position reception unit 121 of the location information production device 1 receives the position information (x1, y1) of the specific location. Next, the accumulation unit 143 acquires the received position information (x1, y1) into a buffer (not shown).

[0095] Then, the reception unit 13 receives radio waves including device identifiers from each of three or more communication devices B for a predetermined time (for example, 3 minutes). And the accumulation unit 143 acquires the device identifiers included in the received radio waves. Also, the intensity acquisition unit 141 acquires the intensity of the received radio waves. Next, the accumulation unit 143 adds the acquired radio wave intensity to the 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 at the specific location indicated by the position information (x1, y1).

[0096] The time-series radio wave intensity management table is a table that manages the time-series radio wave intensities for each communication device B. The time-series radio wave intensity management table is a table that manages two or more records having "ID", "device identifier", and "time-series radio wave intensity". "ID" is information for identifying a record. "Time-series radio wave intensity" is radio wave intensity that is temporally continuous. "R 11 ", "R 12 ", "R 21 ", etc. are radio wave intensities.

[0097] After the management table of FIG. 7 is configured, since a predetermined time (e.g., 3 minutes) has elapsed after the position information (x1, y1) of a specific location is received by the position reception unit 121, the storage unit 143 determines that it meets the storage conditions for the location information.

[0098] Next, the type determination unit 142 obtains 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. And assume that the type determination unit 142 determines that the communication device B identified by the device identifiers "device 1, device 3, device 4, device 6,..." is a fixed terminal, and determines that the communication device B identified by the device identifiers "device 2, device 5,..." is a mobile terminal.

[0099] Next, the storage unit 143 constructs radio wave intensity information by pairing the device identifier of the communication device B that is a fixed terminal with the representative value of the radio wave intensity. Then, the storage unit 143 stores a plurality of pieces of each radio wave intensity information in association with the position information (x1, y1). By such processing, the 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 the radio wave intensity. In such a case, each record in FIG. 8 does not have position information. Also, in such a case, the position reception unit 121 does not need to receive the position information of the specific location.

[0100] The location information management table is a table for managing location information. The location information management table stores a plurality of records that are records associated with the position information and have "ID", "device identifier", and "radio wave intensity information". "Radio wave intensity information" has "device identifier" and "radio wave intensity".

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

[0102] User A moves to specific location 2 indicated by the location information (x2, y2) with the location information production device 1 and performs the same operations as above. As a result, the location information production device 1 configures and stores the record with "ID = 2" in the location information management table of FIG. 8. Further, User A moves to each of one or more specific locations including specific location 3 with the location information production device 1 and performs the same operations as above. As a result, the location information production device 1 configures and stores the record (not shown) of the transition to "ID = 3" in the location information management table of FIG. 8.

[0103] As described above, according to the present embodiment, location information for acquiring the position of the terminal device indoors can be obtained. That is, according to the present embodiment, three or more pieces of location information for acquiring the position of the terminal device indoors can be produced.

[0104] Note that the processing in the present embodiment may be realized by software. And this software may be distributed by software download or the like. Also, this software may be recorded on a recording medium such as a CD-ROM and distributed. Note that this also applies to other embodiments in this specification. The software for realizing the location information production device 1 in the present embodiment is a program as follows. That is, this program causes a computer to function as a position reception unit that receives position information of a specific location, a reception unit that receives radio waves including a device identifier for identifying the communication device from each of three or more communication devices at the specific location, an intensity acquisition unit that acquires the time-series radio wave intensity for each of the three or more communication devices, a type determination unit that determines whether each of the three or more communication devices for which the intensity acquisition unit has acquired the time-series radio wave intensity 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, radio wave intensity, and the position information of the specific location of each of the three or more communication devices determined by the type determination unit to be a fixed terminal.

[0105] (Embodiment 2) In the present embodiment, a terminal device that receives radio waves from three or more communication devices B determines the type of each communication device B using the intensity of radio waves in time series, and uses the radio wave intensity only from the communication device B whose type is "fixed terminal" to obtain and output a terminal position indicating the position of the terminal device indoors. The terminal device will be described.

[0106] Also, in the present embodiment, a terminal device that determines whether the terminal device is moving or stopped, uses the determination result to obtain and output the terminal position will be described.

[0107] FIG. 9 is a conceptual diagram of the information system C in the present embodiment. The information system C includes one or two or more terminal devices 2 and three or more communication devices B.

[0108] The terminal device 2 is a terminal capable of acquiring position information indoors. The terminal device 2 is, for example, a smartphone, a tablet terminal, a smartwatch, a so-called personal computer, etc., and its type is not limited.

[0109] FIG. 10 is a block diagram of the terminal device 2 in the present embodiment. The terminal device 2 includes a storage unit 21, a reception unit 22, a processing unit 23, and an output unit 24. The storage unit 21 includes a location 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 location 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, the location information described later.

[0111] Three or more pieces of location information are stored in the location information storage unit 211. It is preferable that the three or more pieces of location information in the location information storage unit 211 are the information accumulated by the location information production device 1.

[0112] Each of the three or more location information in the location information storage unit 211 has, for example, location information of a specific location, a device identifier, and a radio wave intensity. It is preferable that three or more radio wave intensity information are associated with each of the three or more location information. The radio wave intensity information has a device identifier and a radio wave intensity. The three or more radio wave intensity information may constitute a radio wave intensity vector. The radio wave intensity vector is a vector using the 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). In the location information storage unit 211, for example, a location information management table having the structure of FIG. 8 is stored.

[0113] Note that the terminal device 2 may not have the location information storage unit 211. In such a case, the terminal device 2 refers to the location information storage unit 211 of an external device (not shown) and acquires the terminal position 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 usually performs the same function as the above-described receiving unit 13. The receiving unit 22 usually receives radio waves including a device identifier from each of the three or more communication devices B. The receiving unit 22 usually receives radio waves continuously.

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

[0116] The intensity acquisition unit 231 acquires time-series radio wave intensities for each of the three or more communication devices B. The intensity acquisition unit 231 acquires the radio wave intensity based on the radio waves received by the receiving unit 22. Note that the intensity acquisition unit 231 performs the same function as the above-described intensity acquisition unit 141.

[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 intensities acquired by the intensity acquisition unit 231. The type determination unit 232 performs the same function as the above-described type determination unit 142.

[0118] The movement determination unit 233 determines whether the terminal device 2 is in motion or stationary, and obtains a movement determination result that is the result of this determination. The movement determination result is, for example, "in motion" or "stationary".

[0119] The movement determination unit 233 obtains, for example, the sensor information of the terminal device 2, and uses the sensor information to obtain a movement determination result. The sensor information is, for example, the acceleration by a gyroscope and the time-series position information.

[0120] The movement determination unit 233 obtains, for example, a movement determination result of "stationary" if the acceleration by a gyroscope is "0" or below a threshold value. The movement determination unit 233 obtains, for example, a movement determination result of "in motion" when the acceleration by a gyroscope is equal to or greater than the threshold value.

[0121] The movement determination unit 233 determines that it is stationary when there is no change in the time-series radio wave intensities of one or two or more communication devices B, for example, using the time-series radio wave intensities of each of three or more communication devices B obtained by the intensity acquisition unit 231, and obtains a movement determination result of "stationary".

[0122] The position acquisition unit 234 obtains the 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 radio wave intensities of the fixed terminals to obtain a terminal position that is the position information of the terminal device 2 indoors.

[0123] The position acquisition unit 234 obtains, for example, the radio wave intensities of three or more communication devices B that the type determination unit 232 has determined to be fixed terminals, refers to three or more location information in the location information storage unit 211 using the three or more radio wave intensities, and obtains the terminal position by the fingerprint method.

[0124] It is preferable for the position acquisition unit 234 to obtain position information using the movement determination result. For example, it is preferable for the position acquisition unit 234 to obtain the terminal position only when the movement determination result is "stationary".

[0125] The location acquisition unit 234 may acquire the terminal location using the location information including the device identifier only when the device identifier corresponding to the radio wave received by the reception unit 22 is included in the location information of the location information storage unit 211. This is because the location information of the location information storage unit 211 is the location information of the fixed terminal.

[0126] The intensity acquisition means 2341 acquires the radio wave intensities of three or more communication devices determined by the type determination unit 232 to be fixed terminals in association with the device identifiers.

[0127] The location determination means 2342 determines one or more pieces of location information satisfying the similarity condition with the radio wave intensities associated with each of the three or more device identifiers acquired by the intensity acquisition means 2341 from the location information of the location information storage unit 211.

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

[0129] The location acquisition means 2343 acquires the location information included in each of the one or more pieces of location information determined by the location determination means 2342, and acquires the terminal location using the one or more pieces of location information.

[0130] The output unit 24 outputs various types of information. The various types of information are, for example, here the terminal location and the indoor map.

[0131] Here, output includes concepts such as display on a display, projection using a projector, printing by a printer, sound output, transmission to an external device, storage in a recording medium, and delivery of a processing result to another processing device or another program.

[0132] The position output unit 241 outputs the terminal position acquired by the position acquisition unit 234. For example, the position output unit 241 displays a symbol indicating the position specified by the terminal position on an indoor map.

[0133] The storage unit 21 and the location information storage unit 211 are preferably non-volatile recording media, but can also be realized with volatile recording media.

[0134] The process of storing information in the storage unit 21 or the like is not limited. For example, information may be stored in the storage unit 21 or the like via a recording medium, or information transmitted via a communication line or the like may be stored in the storage unit 21 or the like, or information input via an input device may be stored in the storage unit 21 or the like.

[0135] The receiving unit 22 is usually realized by 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 from a processor, a memory, and the like. The processing procedures of the processing unit 23 and the like are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may also be realized by hardware (dedicated circuit). Note that the processor may be a CPU, an MPU, a GPU, or the like, and its type is not limited.

[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 the output device and the output device.

[0138] Next, a first operation example of the terminal device 2 will be described with reference to the flowchart of FIG. 11.

[0139] (Step S1101) The movement determination unit 233 determines whether the terminal device 2 is in motion or stopped. An example of such movement determination processing will be described using the flowchart of FIG. 12.

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

[0141] (Step S1103) The intensity acquisition means 2341 performs time-series intensity acquisition processing. An example of the time-series intensity acquisition processing was described using the flowchart of FIG. 4.

[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 was described using the flowchart of FIG. 5.

[0143] (Step S1105) The position acquisition means 2343 performs position estimation processing to acquire the terminal position. An example of the position estimation processing will be described using the flowchart of FIG. 13.

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

[0145] Note that in the flowchart of FIG. 11, the process ends due to a power-off or a processing end interrupt.

[0146] Next, an example of the movement determination processing in Step S1101 will be described using the flowchart of FIG. 12.

[0147] (Step S1201) The movement determination unit 233 acquires the sensor value (e.g., acceleration) of the terminal device 2 and temporarily stores it in a buffer (not shown).

[0148] (Step S1202) The movement determination unit 233 determines whether to perform movement determination by using the sensor values of a buffer (not shown). If movement determination is to be performed, it proceeds to step S1203. If movement determination is not to be performed, it returns to step S1201. Note that the movement determination unit 233 may always perform movement determination, or for example, may perform movement determination after accumulating a predetermined number or more of sensor values 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 determines whether the terminal device 2 is in motion or stopped by using one or two or more sensor values of a buffer (not shown). If it is stopped, it proceeds to step S1204. If it is in motion, it proceeds to step S1205.

[0150] (Step S1204) The movement determination unit 233 sets the movement determination result to "stopped". It proceeds to step S1206.

[0151] (Step S1205) The movement determination unit 233 sets the movement determination result to "in motion".

[0152] (Step S1206) The movement determination unit 233 clears the buffer (not shown). It returns to the upper-level processing.

[0153] Next, an example of the position estimation process in step S1105 will be described with reference to the flowchart of FIG. 13.

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

[0155] (Step S1302) The position acquisition means 2343 vectorizes the three or more radio wave intensity information to acquire a radio wave intensity vector. Note that the radio wave intensity vector is, for example, (the radio wave intensity of device identifier 1, the radio wave intensity of device identifier 2, the radio wave intensity of device identifier 3, ··· the radio wave intensity of device identifier n).

[0156] (Step S1303) The position acquisition means 2343 substitutes 1 for the counter i.

[0157] (Step S1304) The position acquisition means 2343 determines whether the i-th location information exists in the location information storage unit 211. If the i-th location information exists, it proceeds to step S1305; if it does not exist, it proceeds to step S1309.

[0158] (Step S1305) The position acquisition means 2343 acquires the i-th radio wave intensity vector of the i-th location information from the location information storage unit 211.

[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 determines whether the similarity satisfies the similarity condition (for example, the similarity is equal to or greater than a threshold value). If the similarity satisfies the similarity condition, it proceeds to step S1307; if it does not, it proceeds to step S1308.

[0160] (Step S1307) The position acquisition means 2343 acquires the position information and the similarity of the i-th location information and accumulates them in a buffer (not shown).

[0161] (Step S1308) The position acquisition means 2343 increments the counter i by 1. It returns to step S1304.

[0162] (Step S1309) The position acquisition means 2343 acquires the terminal position that specifies the position of the terminal device 2 using the set of position information and similarity of 3 or more accumulated in a buffer (not shown). It 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. 14.

[0164] (Step S1401) The intensity acquisition means 2341 performs time-series intensity acquisition processing. An example of the time-series intensity acquisition processing was described using the flowchart of FIG. 4.

[0165] (Step S1402) The location determination means 2342 performs fixed information acquisition processing. An example of the fixed information acquisition processing was described using the flowchart of FIG. 5.

[0166] (Step S1403) The position acquisition means 2343 performs position estimation processing for acquiring the terminal position. An example of the position estimation processing was described using the flowchart of FIG. 13.

[0167] (Step S1404) The position output unit 241 outputs the terminal position acquired in Step S1403. Return to Step S1401.

[0168] Note that in the flowchart of FIG. 14, the process ends due to a power-off or a processing end interrupt.

[0169] Next, a second example of the type determination process in the fixed information acquisition process of Step S1402 in the flowchart of FIG. 14 will be described using the flowchart of FIG. 15. Note that the first example of the type determination process was described using the flowchart of FIG. 6.

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

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

[0172] (Step S1503) The type determination unit 232 sets the type as "fixed terminal". Return to the upper-level process.

[0173] (Step S1504) The type determination unit 232 sets the type as "mobile terminal". Return to the upper-level process.

[0174] Hereinafter, a specific operation example of the terminal device 2 in the present embodiment will be described.

[0175] It is assumed that user B holds his / her terminal device 2 and enters an indoor location identified by the 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, 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 according to the processing from step S1103 to S1106 in FIG. 11 or the processing of the flowchart in FIG. 14.

[0177] That is, the intensity acquisition means 2341 performs the time-series intensity acquisition process described with reference to the flowchart in FIG. 4, acquires the radio wave intensities of three or more communication devices B at the location X where the terminal device 2 is present, and constructs a time-series radio wave intensity management table having the structure shown in FIG. 7.

[0178] Next, the intensity acquisition means 2341 performs the time-series intensity acquisition process, acquires the time-series radio wave intensities of each communication device B that can receive radio waves at the location X, and constructs a time-series radio wave intensity management table having the structure shown in FIG. 7.

[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 with reference to the flowchart in FIG. 4.

[0180] Next, the location determination means 2342 acquires the radio wave intensities of "Device 1", "Device 3", "Device 4", "Device 6", etc. determined to be fixed terminals. Then, the location determination means 2342 acquires the radio wave intensity vector "(radio wave intensity of Device 1, radio wave intensity of Device 3, radio wave intensity of Device 4, radio wave intensity of Device 6,...) = (P1, P3, P4, P6,...)". Here, the radio wave intensity of communication device B acquired by the location determination means 2342 may be a representative value of two or more radio wave intensities, or may be a single radio wave intensity such as the intensity of the latest radio wave of communication device B.

[0181] Next, the position acquisition means 2343 performs the position estimation process described with reference to the flowchart of FIG. 13, and calculates the similarity between the radio wave intensity vector at point X and the radio wave intensity vectors of each record in FIG. 8 (vectors constituted by radio wave intensity information). Next, the position acquisition means 2343 determines radio wave intensity vectors that satisfy the similarity condition "similarity >= threshold value" (for example, the radio wave intensity vector of "ID=1" in FIG. 8 (S 11 , S 12 , S 13 ,...), the radio wave intensity vector of "ID=2" (S 21 , S 22 , S 23 ,...),...). Next, the position acquisition means 2343 acquires pairs of position information and similarity that correspond to the radio wave intensity vectors that satisfy the similarity condition (for example, "(x1, y1), DS1", "(x2, y2), DS2",...). Next, the position acquisition means 2343 acquires the position (x1×DS1 / sum of similarities + x2×DS2 / sum of similarities +...) indoors at point X. The sum of similarities is "DS1 + DS2 +...".

[0182] Next, the position output unit 241 outputs the map of the indoor or outdoor area stored in the storage unit 21, and arranges a symbol at the position indicated by the position information (terminal position) acquired by the position acquisition means 2343 on the map.

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

[0184] Note that the processing in this embodiment may be implemented by software. And this software may be distributed by software download or the like. Also, this software may be recorded on a recording medium such as a CD-ROM and distributed. Note that this also applies to other embodiments in this specification. The software for realizing the terminal device 2 in this embodiment is the following program. That is, this program causes a computer to function as a receiving unit that receives radio waves including device identifiers for identifying the communication devices from three or more communication devices, an intensity acquisition unit that acquires time-series radio wave intensities for each of the three or more communication devices, a type determination unit that determines whether each of the three or more communication devices is a fixed terminal in which the communication device is fixed or a mobile terminal that is moving using the time-series radio wave intensities acquired by the intensity acquisition unit, a position acquisition unit that acquires the terminal position which is the position information of the terminal device using the radio wave intensities of the three or more communication devices determined by the type determination unit to be fixed terminals, and a position output unit that outputs the terminal position acquired by the position acquisition unit.

[0185] (Embodiment 3) In this embodiment, an action acquisition device that acquires the user's action information and time zone using the user's position information corresponding to the time and outputs the action information associated with the time zone will be described. Note that it is preferable that the action acquisition device further uses the user's activity data and vital data to acquire the user's action information and time zone. Also, the position information used when acquiring the action information may be the indoor position information or the like acquired by the terminal device 2 described in Embodiment 2.

[0186] Also, in this embodiment, an action acquisition device that acquires action information using past record information will be described. Note that the past record information may be information based on the input of one or two or more users.

[0187] Also, in this embodiment, a behavior acquisition device that acquires and outputs a user's emotional information will be described. Further, in this embodiment, it is preferable to acquire emotional 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 also be a server. That is, the process of acquiring behavior information or emotional information described later may be performed by the user's terminal or the server.

[0189] Also, in this embodiment, when behavior information cannot be acquired, a behavior acquisition device that acquires and outputs location information using a map will be described.

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

[0191] FIG. 16 is a conceptual diagram of the 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 smartwatch, a so-called personal computer, etc., and its type does not matter.

[0193] The server device 4 is, for example, a set of behavior source information and behavior information of two or more users, and is a device that stores a set for each time zone. The server device 4 stores, for example, learning information described later and provides the learning information to the behavior acquisition device 3. The server device 4 is, for example, a cloud server, an ASP server, but its type does not matter.

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

[0195] The action acquisition device 3 includes a storage unit 31, a reception 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 an action management unit 313. Note that the action management unit 313 may exist in an external device (not shown). The processing unit 33 includes an intensity acquisition unit 231, a type determination unit 232, a time acquisition unit 331, a position acquisition unit 332, an activity acquisition unit 333, a vital sign acquisition unit 334, an action 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 an action output unit 341 and an emotion output unit 342.

[0196] The server device 4 includes a server storage unit 41, a server reception unit 42, a server processing unit 43, and a server transmission unit 44.

[0197] The action acquisition device 3 receives, for example, an output instruction, a confirmation instruction, and information input. The output instruction is an instruction to output output information described later. The output instruction usually has period information. The period information is information that specifies the period for outputting action information and the like. The confirmation instruction is an instruction to confirm the estimated action information or emotion information. The information input is an input of information for changing the action information or the emotion information when the estimated action information or emotion information is different. The information input is the updated action information or the updated emotion information.

[0198] Various types of information are stored in the storage unit 31 that constitutes the action acquisition device 3. The various types of information are, for example, learning information described later, a map described later, action information described later, location information, a calendar template, action information associated with two or more action conditions, and emotion information associated with two or more emotion conditions.

[0199] The calendar template is information indicating the calendar template to be output. The calendar template is, for example, an ICS file, a file described in HTML, or a file described in XML, and its data structure is not limited.

[0200] An action condition is a condition for acquiring action information. The action condition is a condition using two or more pieces of action source information. The action condition is associated with the action information. The action condition is, for example, "location information = office AND 8:00 <= time <= 19:00", and the action information associated with such an action condition is "work". The action condition is, for example, "location information = kitchen AND activity data = standing AND 7:00 <= time <= 8:00", and the action information associated with such an action condition is "cooking". The action condition is, for example, "location information = park AND activity data = standing AND 120 <= heart rate", and the action information associated with such an action condition is "running".

[0201] An emotion condition is a condition for acquiring emotion information. The emotion condition is a condition using two or more pieces of emotion source information. The emotion condition is associated with the emotion information. The emotion condition is, for example, "location information = office AND 8:00 <= time <= 19:00", and the emotion information associated with such an action condition is "positive". The emotion condition is, for example, "location information = kitchen AND activity data = standing AND 7:00 <= time <= 8:00", and the emotion information associated with such an action condition is "positive". The action condition is, for example, "location information = park AND activity data = standing AND 120 <= heart rate", and the emotion information associated with such an action 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 of the learning management unit 311 is, for example, action learning information and emotion learning information. Each of the two or more pieces of learning information of the learning management unit 311 may be associated with different user attribute value conditions. The user attribute value condition is a condition regarding one or two or more user attribute values.

[0203] A user attribute value is an attribute value of a user. The user attribute value is, for example, occupation, family composition, unmarried or married, gender, age, age group, morning type or night type, residential area, etc., without limitation.

[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 response 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 response table.

[0205] The behavior teacher data has, for example, one or two or more behavior source information and behavior information. The emotion teacher data has, for example, one or two or more behavior source information, behavior information, and emotion information.

[0206] The emotion teacher data has, for example, one or two or more behavior source information or behavior information and emotion information. The emotion teacher data has, for example, one or two or more behavior source information, behavior information, and emotion information. In the emotion teacher data, one or more behavior source information, behavior information, or one or more behavior source information and behavior information are explanatory variables, and the emotion information is the target variable.

[0207] The behavior information is information that identifies the user's behavior. The behavior information is, for example, "work", "watching TV", "walking", "running", "going to the gym", "taking a bath", "sleeping".

[0208] The emotion information is information regarding the user's emotion. The emotion information is, for example, positive (e.g., "1") or negative (e.g., "0"). The emotion information is, for example, the degree of positiveness or the degree of negativeness. The emotion information is, for example, joy (e.g., "1"), anger (e.g., "2"), sorrow (e.g., "3"), happiness (e.g., "4").

[0209] Action source information is the information that serves as the basis for acquiring action information. 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 pieces of past action information. Among the past action information, usually, the immediately preceding action information is included. The action source information may include one or more pieces of the user's future schedule information. The schedule information is, for example, information stored in a calendar server (e.g., the server of "google calendar (registered trademark)") not shown in the figure. The action source information may include the elapsed time since arriving at the same location information. The action source information may include one or more user attribute values.

[0210] Location information is the information that identifies the location of the action acquisition device 3. The location information is, for example, (latitude, longitude), (latitude, longitude, altitude), the three-dimensional relative position (x, y, z) indoors, or the two-dimensional relative position (x, y) indoors, or place information. Place information is the information that expresses the meaning of a place. The place information is, for example, indoor place information or outdoor place information. Indoor place information is the information that identifies an indoor place. The indoor place information is, for example, "living room", "kitchen", "study room", "office". Outdoor place information is the information that identifies an outdoor place. The outdoor place information is, for example, "ABC Station", "library", "izakaya", "Location A".

[0211] Body data is the information related to the user's body. The body data is, for example, activity data, vital data.

[0212] Activity data is the information that identifies the user's activity. The activity data is, for example, "standing", "grounded (e.g., sitting)".

[0213] Vital data refers to information that can be obtained from a user's living body. Vital data may also be referred to as biometric information. Vital data includes, for example, the heart rate, heart rate variability, blood pressure (systolic and / or diastolic), respiratory rate per unit time (e.g., 1 minute or 30 seconds), and body temperature. Learning information is, for example, a learning model or a correspondence table. A learning model is information constructed by a learning process of machine learning using two or more pieces of teacher data and is information used for the prediction process of machine learning. A learning model may also be referred to as a learner, a classifier, a classification model, etc. The algorithm of machine learning can be any of deep learning, random forest, decision tree, SVM, etc. Also, for machine learning, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, TinySVM, and various existing libraries can be used. Also, when the learning information is a learning model, one or more pieces of action source information of the teacher data are explanatory variables, and the action information is the target variable.

[0214] The learning model here is, for example, a behavior learning model or an emotion learning model. A behavior learning model is a learning model for obtaining behavior information and is information obtained by a learning process of machine learning using behavior teacher data. Behavior teacher data has one or more pieces of action source information and action information.

[0215] An emotion learning model is a learning model for obtaining emotion information and is information obtained by a learning process of machine learning using emotion teacher data. Emotion teacher data has one or more pieces of 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 pieces of action correspondence information. The action correspondence information is information indicating the correspondence between one or more pieces of action source information and action information. Note that one or more pieces of action source information have, for example, a vector structure. Such a vector is called an action source vector. The action source vector is a vector having one or more pieces of each action source information as elements. The emotion correspondence table has two or more pieces of emotion correspondence information. The emotion correspondence information is information indicating the correspondence between one or more pieces of emotion source information and emotion information. One or more pieces of emotion source information have, for example, a vector structure. Such a vector is called an emotion source vector. The emotion source vector is a vector having one or more pieces of each emotion source information as elements.

[0217] The map management unit 312 stores a map. The map has location information corresponding to one or more pieces of each location information. The map is, for example, in the KIWI format, but its structure does not matter.

[0218] The action management unit 313 stores action information associated with two or more time zones. The action information here is, for example, the information acquired by the action estimation unit 335. The action information is the information acquired by the action estimation unit 335 and modified by the user.

[0219] It is preferable that it is possible to determine whether the action information here is finalized action information. The action information is, for example, associated with a finalized flag. The finalized flag is a flag indicating that the action information is finalized. The ability to determine whether the action information is finalized means, for example, that the storage areas for finalized action information and non-finalized action information are different. Other methods for making it possible to determine whether the action information is finalized are not limited.

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

[0221] The processing unit 33 performs various processes. The various processes are, for example, processes 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 time. The time acquisition unit 331 acquires time from, for example, a clock (not shown). The time acquisition unit 331 receives time from, for example, the server device 4 or a device (not shown). The time may be in hours, minutes, and seconds, or in hours and minutes. The time may include information on one or two or more of "year", "month", and "day". The time acquisition unit 331 may acquire the day of the week. The day of the week may be considered as 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 action acquisition device 3 is present indoors or the like and a GPS signal cannot be received, it is preferable that the position acquisition unit 332 perform 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, a GPS receiver. Such position information is absolute position information. The position information acquired by the position acquisition unit 332 may be position information outdoors or indoors.

[0224] It is preferable that the position acquisition unit 332 acquires indoor position information using the radio wave intensity of the communication device B determined by the type determination unit 232 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 also be the radio wave intensity of one or two or more communication devices B.

[0225] The activity acquisition unit 333 acquires the user's activity data associated with the 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 acquisition unit 334 acquires one or more types of vital data of the user associated with the time. The vital acquisition unit 334 usually acquires one or more types of vital data associated with the time acquired by the time acquisition unit 331. The process of acquiring the vital data is a known technique.

[0227] The action estimation unit 335 acquires action information for specifying the user's action in the time zone specified by the time included in two or more pieces of action source information including position information associated with the time, using the two or more pieces of action source information. It is preferable that the action source information also includes activity data. Further, it is preferable that the action source information also includes one or more types of vital data.

[0228] The action estimation unit 335, for example, detects an action condition in which two or more pieces of action source information including position information associated with the time match, and acquires the action information paired with the action condition from the storage unit 31.

[0229] The action estimation unit 335, for example, acquires the action information "working" when sitting at the position of the desk at home indoors at 13:15. The action estimation unit 335, for example, acquires the action information "watching TV" when sitting in the living room at home indoors at 20:17. The action estimation unit 335, for example, acquires the action information "drinking party" when acquiring the position information "izakaya" at 20:17.

[0230] The action estimation unit 335 may acquire the action information in the time zone using the action learning information of the learning management unit 311 and two or more pieces of action source information including position information associated with the time. Hereinafter, the processing of the action estimation unit 335 when the learning information is a learning model and when it is a correspondence table will be described.

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

[0232] The action estimation unit 335 acquires the learning model of the learning management unit 311. Also, the action estimation unit 335 acquires the time and one or more pieces of action source information associated with the time. Next, the action estimation unit 335 provides the time, the action source information, and the learning model to a module that performs prediction processing of machine learning, executes the module, and acquires action information.

[0233] Note that when the score output by the module is less than or equal to the threshold value or less than the threshold value, the action estimation unit 335 may not acquire the action information. (2) When the behavior learning information is a behavior correspondence table

[0234] The action estimation unit 335 acquires the time and the action source information associated with the time. Next, the action estimation unit 335 acquires an action source vector having the time and the action source information as elements. Next, the action estimation unit 335 calculates the similarity between the action source vector and the action source vectors of each of the two or more pieces of action correspondence information included in the action correspondence table. Next, the action estimation unit 335 acquires from the action correspondence table the action information that pairs with the action source vector having the maximum similarity. Note that even if the similarity is the maximum, when the similarity is less than or equal to the threshold value or less than the threshold value, the action estimation unit 335 may not acquire the action information.

[0235] The emotion estimation unit 336 acquires emotion information regarding the user's emotion in a time zone using the action information or the action source information.

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

[0237] For example, when sitting at the position of a desk at home indoors at 13:15, the emotion estimation unit 336 obtains the emotion information "positive". For example, when sitting in the living room of a home indoors at 20:17, the emotion estimation unit 336 obtains the emotion information "positive". For example, when the location information "izakaya" is obtained at 20:17, the emotion estimation unit 336 obtains the emotion information "positive".

[0238] The emotion estimation unit 336 uses the emotion learning information of the learning management unit 311 and the action information obtained by the action estimation unit 335 or one or more action source information from which the action information is obtained to obtain emotion information in a time period. Here, the action information or one or more pieces of action source information are used for obtaining emotion information, so they are called emotion source information.

[0239] The emotion estimation unit 336 obtains one or more user attribute values, obtains from the learning management unit 311 the emotion learning information that pairs with the user attribute value conditions that the one or more user attribute values match, and may obtain emotion information using the emotion learning information.

[0240] The emotion estimation unit 336 will explain the processing of the action estimation unit 335 when the emotion learning information is an emotion learning model and when it is an emotion correspondence table as follows. (1) When the emotion learning information is an emotion learning model

[0241] The emotion estimation unit 336 obtains the emotion learning model of the learning management unit 311. Also, the emotion estimation unit 336 obtains one or more pieces of action source information or action information associated with the time. Next, the emotion estimation unit 336 provides the obtained one or more pieces of action source information or action information and the emotion learning model to a module that performs prediction processing of machine learning, executes the module, and obtains emotion information.

[0242] Note that when the score output by the module is below or less than the threshold value, the emotion estimation unit 336 does not have to obtain emotion information. (2) When the emotion learning information is an emotion correspondence table

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

[0244] The location acquisition unit 337 refers to the map of 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 such a case is usually absolute location information (for example, (latitude, longitude)).

[0245] It is preferable that the location acquisition unit 337 acquires the location information only for the time period when the action estimation unit 335 does not acquire the action information.

[0246] The storage unit 338 associates the action information acquired by the action estimation unit 335 with the time period and stores it in the action management unit 313.

[0247] The storage unit 338 may associate the emotion information acquired by the emotion estimation unit 336 with the time period and store it in the action management unit 313.

[0248] The composition unit 339 composes the information to be output using the action information of the action management unit 313. The composition unit 339 composes the information to be output using, for example, the emotion information of the action management unit 313.

[0249] The component 339 constitutes output information having action information paired with each of two or more time zones on the calendar, for example, in the regions specified by the time zones. In the output information, it is preferable that two or more pieces of action information are arranged so that the confirmed action information and the unconfirmed action information can be visually distinguished. The component 339 preferably constitutes output information that visually explicates the emotion information corresponding to two or more time zones. The component 339 preferably constitutes output information in which, for example, a time zone in which the emotion information is "positive" and a time zone in which the emotion information is "negative" are visually distinguishable. The component 339 preferably constitutes output information such that, for example, the background colors of a time zone in which the emotion information is "positive" and a time zone in which the emotion information is "negative" are different.

[0250] The output unit 34 outputs various types of information. The various types of information are, for example, action information, emotion information, and location information.

[0251] Here, output generally refers to display on a display, but may also be a concept including projection using a projector, printing by a printer, transmission to an external device, storage in a recording medium, delivery of a processing result to another processing device or another program, etc.

[0252] The action output unit 341 outputs the action information in each of one or more time zones.

[0253] When the action estimation unit 335 cannot acquire the action information, it is preferable that the action output unit 341 outputs the location acquired by the location acquisition unit 337.

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

[0255] The emotion output unit 342 outputs emotion information. The emotion output unit 342 outputs, for example, the emotion information acquired by the emotion estimation unit 336. It is preferable that the emotion output unit 342 outputs the emotion information in each of one or more time zones.

[0256] In the server storage unit 41 that constitutes the server device 4, various types of information are stored. The various types of information are, for example, the learning information described above, the action source information and time associated with two or more user identifiers, two or more action teacher data, and two or more emotion teacher data.

[0257] The server reception unit 42 receives various instructions and information. The various instructions and information are, for example, an instruction to transmit information. The information here is, for example, learning information and action source information.

[0258] The server processing unit 43 performs various processes. The various processes are, for example, a learning process. The learning process is, for example, an action learning process and an emotion learning process.

[0259] The action learning process is a process of obtaining an action learning model using two or more action teacher data. For example, the server processing unit 43 provides two or more action teacher data to a learning processing module of machine learning, executes the module, obtains an action learning model, and accumulates it in the server storage unit 41. For example, for each candidate of two or more pieces of action information, the server processing unit 43 provides two or more positive examples that are teacher data including the action information and two or more negative examples that are teacher data not including the action information to the learning processing module of machine learning, executes the module, obtains an action learning model for each candidate of the action information, and accumulates it in the server storage unit 41 in association with the action information. For example, the server processing unit 43 obtains an action correspondence table that is a table having two or more pieces of each action teacher data as records, and accumulates it in the server storage unit 41.

[0260] Sentiment learning processing is a process of obtaining a sentiment learning model using two or more sentiment teacher data. The server processing unit 43, for example, provides two or more sentiment teacher data to a learning processing module of machine learning, executes the module, obtains a sentiment learning model, and stores it in the server storage unit 41. The server processing unit 43, for example, for each candidate of two or more pieces of each sentiment information, provides two or more positive examples, which are teacher data including the sentiment information, and two or more negative examples, which are teacher data not including the sentiment information, to a learning processing module of machine learning, executes the module, obtains a sentiment learning model for each candidate of the sentiment information, and stores it in the server storage unit 41 in association with the sentiment information. The server processing unit 43, for example, obtains a sentiment correspondence table, which is a table with two or more pieces of each sentiment teacher data as records, and stores it in the server storage unit 41.

[0261] The server transmission unit 44 transmits various types of information. The various types of information are, for example, an action learning model, a sentiment learning model, an action correspondence table, and a sentiment correspondence table.

[0262] The storage unit 31, the learning management unit 311, the map management unit 312, the action management unit 313, and the server storage unit 41 are preferably non-volatile recording media, but can also be realized with volatile recording media.

[0263] The process of storing information in the storage unit 31 or the like is not limited. For example, information may be stored in the storage unit 31 or the like via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 31 or the like, or information input via an input device may be stored in the storage unit 31 or the like.

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

[0265] The processing unit 33, time acquisition unit 331, position acquisition unit 332, activity acquisition unit 333, vital acquisition unit 334, action estimation unit 335, emotion estimation unit 336, location acquisition unit 337, storage unit 338, configuration unit 339, and server processing unit 43 can usually be realized from a processor, memory, etc. The processing procedures of the processing unit 33, etc. are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may also be realized by hardware (a dedicated circuit). Note that the processor may be a CPU, MPU, GPU, etc., and its type does not matter.

[0266] The output unit 34, action output unit 341, and emotion output unit 342 may or may not be considered to include output devices such as a display and a speaker. The output unit 34 can be realized by the driver software of the output device or the driver software of the output device and the output device, etc.

[0267] Next, an operation example of the action acquisition device 3 that constitutes the information system D will be described using the flowchart of FIG. 19.

[0268] (Step S1901) The processing unit 33 determines whether to acquire information. If it is determined to acquire information, it proceeds to step S1902, and if it is determined not to acquire information, it proceeds to step S1916. Note that the processing unit 33 may always determine to acquire information, or may determine to acquire information when a flag indicating that information is to be acquired is stored in the storage unit 31, etc. The conditions for such determination do not matter.

[0269] (Step S1902) 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.

[0270] (Step S1903) The processing unit 33 acquires one or two or more pieces of action source information. An example of such action source acquisition processing will be described using the flowchart of FIG. 20.

[0271] (Step S1904) The action estimation unit 335 estimates action information for identifying the user's action using the one or more action source information acquired in step S1903. An example of such action estimation processing will be described using the flowcharts of FIGS. 22 to 24.

[0272] (Step S1905) The location acquisition unit 337 determines whether action information could be acquired in step S1904. If action information could be acquired, it proceeds to step S1907, and if action information could not be acquired, it proceeds to step S1906.

[0273] (Step S1906) The location acquisition unit 337 refers to the map of the map management unit 312 and acquires location information corresponding to the position information acquired in step S1903. Note that it may be the case that location information cannot be acquired here.

[0274] (Step S1907) The storage unit 338 determines whether the action information stored in a buffer (not shown), which is the action information accumulated immediately before, matches the action information acquired in step S1904. If they match, it proceeds to step S1908, and if they do not match, it proceeds to step S1912.

[0275] (Step S1908) The storage unit 338 associates the acquired action information, etc. with the time acquired in step S1902 and stores it in a buffer (not shown).

[0276] (Step S1909) The emotion estimation unit 336 performs processing for estimating emotion information. An example of such emotion estimation processing will be described using the flowcharts of FIGS. 25 to 27.

[0277] (Step S1910) The storage unit 338 determines whether emotion information could be acquired in step S1909. If emotion information could be acquired, it proceeds to step S1911, and if emotion information could not be acquired, it returns to step S1901.

[0278] (Step S1911) The storage unit 338 stores the emotion information acquired in step S1909 in a buffer (not shown) in association with the time acquired in step S1902. Return to step S1901.

[0279] (Step S1912) The storage unit 338 stores the time acquired in step S1902 and the acquired action information, etc. in a buffer (not shown) in association with each other.

[0280] (Step S1913) The storage unit 338 acquires the previous action information, etc.

[0281] (Step S1914) The storage unit 338 acquires the time zone specified by two or more times associated with the previous action information, etc.

[0282] (Step S1915) The storage unit 338 associates the time zone acquired in step S1914 with the previous action information, etc. acquired in step S1913 and stores them in the action management unit 313. Here, the storage unit 338 may also associate the user identifier and store the time zone and the previous action information, etc. in association with each other. In such a case, it is preferable that the storage destination is the server device 4.

[0283] (Step S1916) The action acquisition device 3 determines whether an output instruction has been received. If an output instruction has been received, go to step S1917; if not, go to step S1919.

[0284] (Step S1917) The configuration unit 339 configures output information using the action information, etc. of the action management unit 313. An example of such output configuration processing will be described with reference to the flowchart of FIG. 28.

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

[0286] (Step S1919) The action acquisition device 3 determines whether it has received an input of information with respect to the output information that is being output. If it has received an input of information, it proceeds to step S1920, and if it has not received an input, it proceeds to step S1923.

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

[0288] (Step S1921) The storage unit 338 performs processing for confirming the action information or emotion information corresponding to the confirmation instruction. It returns to step S1901. Note that such processing is, for example, processing for associating a confirmation flag with the action 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. It returns to step S1901. Note that the input information is, for example, correct action information or correct emotion information. And the storage unit 338 updates the estimated action information or emotion information corresponding to the input information to the input action information or emotion information. Also, the storage unit 338 performs processing for confirming such action information or emotion information.

[0290] (Step S1923) The action acquisition device 3 determines whether it has received a learning instruction. If it has received a learning instruction, it proceeds to step S1924, and if it has not received a learning instruction, it returns to step S1901.

[0291] (Step S1924) A learning unit (not shown) or a learning device (not shown) of the action acquisition device 3 constructs action learning information using two or more action teacher data including action information and the like, and stores it in the learning management unit 311. An example of such action learning processing will be described using 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 constructs emotion learning information using two or more emotion teacher data including emotion information and stores it in the learning management unit 311. Return to step S1901. An example of such emotion learning processing will be described using the flowcharts of FIGS. 31 and 32.

[0293] Note that in the flowchart of FIG. 19, the process ends due to a power-off or a processing end interrupt.

[0294] Next, an example of the action source acquisition process in step S1903 will be described using the flowchart of FIG. 20.

[0295] (Step S2001) The processing unit 33 determines whether the reception unit 32 can acquire a GPS signal. If the GPS signal can be acquired, go to step S2002; if the GPS signal cannot be acquired, go to step S2003.

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

[0297] (Step S2003) The position acquisition unit 332 acquires position information. An example of such position estimation processing will be described using the flowchart of FIG. 21.

[0298] (Step S2004) The activity acquisition unit 333 acquires the user's activity data.

[0299] (Step S2005) The vital acquisition unit 334 acquires one or two or more types of the user's vital data.

[0300] (Step S2006) The action estimation unit 335 acquires one or more pieces of past action information. Note that the one or more pieces of past action information include the immediately preceding action information in terms of time.

[0301] (Step S2007) The action estimation unit 335 determines whether to use the emotion information for the action estimation process. If using the emotion information, it proceeds to step S2008; if not using the emotion information, it proceeds to step S2009. Note that whether to use the emotion information for the action estimation process is usually determined in advance.

[0302] (Step S2008) The emotion estimation unit 336 acquires the emotion information. An example of such emotion estimation processing will be described using the flowcharts in FIGS. 25 to 27.

[0303] (Step S2009) The action estimation unit 335 acquires the elapsed time since the start of the new action specified by the new action information.

[0304] (Step S2010) The action estimation unit 335 constructs the information to be used in the action estimation process using two or more types of action source information including time and position information. Then it returns to the upper-level process. Here, the information constructed is usually a set of action source information, for example, an action source vector with two or more pieces of action source information as elements. The two or more types of action source information are, for example, two or more types of information among time, day of the week, position information, activity data, vital data, past action information, emotion information, and elapsed time.

[0305] Note that in the flowchart of FIG. 20, even when the GPS signal can be acquired, the position acquisition unit 332 may acquire the position information by the position estimation process described using the flowchart of FIG. 21.

[0306] Also, in the flowchart of FIG. 20, the action estimation unit 335 may acquire one or two or more user attribute values and acquire action source information including the one or more user attribute values. Note that the user attribute value may be information stored in the storage unit 31 or information input by the user or the like. Next, an example of the position estimation process in step S2003 will be described using the flowchart of FIG. 21. In the flowchart of FIG. 21, description of the same steps as in FIG. 13 will be omitted. Also, the position estimation process in step S2003 may be the same process as the flowchart of FIG. 13.

[0307] (Step S2101) The position acquisition unit 332 acquires the similarity between two radio field intensity vectors, associates the similarity with the i-th point information, and temporarily stores it in a buffer (not shown). Proceed to step S1308.

[0308] (Step S2102) The position acquisition unit 332 acquires the position information included in the point information paired with the maximum similarity. Return to the upper-level process. Note that the acquired position information is the terminal position.

[0309] Next, an example of the first action estimation process in step S1904 will be described using the flowchart of FIG. 22. The flowchart of FIG. 22 is a process of estimating action information by a prediction process of machine learning using one action learning model. That is, the flowchart of FIG. 22 is a process of estimating action information by a prediction process of multi-class classification of machine learning.

[0310] (Step S2201) The action estimation unit 335 acquires two or more types of action source information (for example, action source vectors) acquired in step S1903.

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

[0312] (Step S2203) The action estimation unit 335 provides the two or more types of action source information acquired in Step S2201 and the action learning model to the prediction processing module of machine learning, and executes the module.

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

[0314] (Step S2205) The action estimation unit 335 determines whether the score acquired in Step S2204 is equal to or greater than the threshold value. If it is equal to or greater than the threshold value, it proceeds to Step S2206; if it is less than the threshold value, it proceeds to Step S2207.

[0315] (Step S2206) The action estimation unit 335 acquires the action information acquired in Step S2204 as the output action information. Return to the upper-level process.

[0316] (Step S2207) The action estimation unit 335 acquires "empty" action information. Return to the upper-level process.

[0317] Note that in the flowchart of FIG. 22, the processes from Step S2205 to Step S2207 may not be performed.

[0318] Next, an example of the second action estimation process in Step S1904 will be described using the flowchart of FIG. 23. The flowchart of FIG. 23 is a process of estimating action information by prediction processing of machine learning using an action learning model for each candidate of two or more pieces of action information. That is, the flowchart of FIG. 22 is a process of estimating action information by prediction processing of binary classification of machine learning.

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

[0320] (Step S2302) The action estimation unit 335 substitutes 1 for the counter i.

[0321] (Step S2303) The action estimation unit 335 refers to the learning management unit 311 to determine whether there is a candidate for the i-th action information. If there is a candidate for the i-th action information, it proceeds to step S2304; if not, it proceeds to step S2309.

[0322] (Step S2304) The action estimation unit 335 acquires the i-th action learning model corresponding to the candidate for the i-th action information from the learning management unit 311.

[0323] (Step S2305) The action estimation unit 335 provides the two or more types of action source information acquired in step S2301 and the i-th action learning model acquired in step S2304 to a prediction processing module that performs binary classification of machine learning, and executes the module.

[0324] (Step S2306) The action estimation unit 335 determines whether the execution result in step S2305 is "true". If it is "true", it proceeds to step S2307; if it is "false", it proceeds to step S2308.

[0325] (Step S2307) The action estimation unit 335 temporarily stores in a buffer (not shown) a score that is part of the execution result in step S2305, associated with the candidate for the i-th action information.

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

[0327] (Step S2309) The action estimation unit 335 acquires the maximum score and determines whether the maximum score is greater than or equal to a threshold value. If the maximum score is greater than or equal to the threshold value, it proceeds to step S2310; if it is less than the threshold value, it proceeds to step S2311.

[0328] (Step S2310) The action estimation unit 335 commends the action information corresponding to the maximum score. It returns to the upper-level process.

[0329] (Step S2311) The action estimation unit 335 acquires the action information of "empty" and returns to the upper-level process.

[0330] Note that in the flowchart of FIG. 23, the processes from step S2308 to step S2311 may not be performed.

[0331] Next, an example of the third action estimation process in step S1904 will be described using the flowchart of FIG. 24. The flowchart of FIG. 24 is a process for estimating action information using an action correspondence table.

[0332] (Step S2401) The action estimation unit 335 acquires two or more types of action source information acquired in step S1903. The two or more types of action source information here are action source vectors.

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

[0334] (Step S2403) The action estimation unit 335 determines whether the i-th action correspondence information exists in the action correspondence table of the learning management unit 311. If the i-th action correspondence information exists, it proceeds to step S2404; if it does not exist, it proceeds to step S2407.

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

[0336] (Step S2405) The action estimation unit 335 acquires the similarity between the action source vector acquired in step S2403 and the action source vector acquired in step S2404, and associates it with the i-th action correspondence information.

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

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

[0339] (Step S2408) The action estimation unit 335 determines whether the maximum similarity acquired in Step S2407 is equal to or greater than a threshold value. If it is equal to or greater than the threshold value, it proceeds to Step S2409; if it is less than the threshold value, it proceeds to Step S2410.

[0340] (Step S2409) The action estimation unit 335 acquires the action information associated with the i-th action correspondence information paired with the maximum similarity. It returns to the upper-level process.

[0341] (Step S2410) The action estimation unit 335 acquires the action information of "empty". It returns to the upper-level process.

[0342] Note that 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 using the flowchart of FIG. 25. The flowchart of FIG. 25 is a process of estimating emotion information by prediction processing of machine learning using one emotion learning model. That is, the flowchart of FIG. 25 is a process of estimating emotion information by prediction processing of multi-class classification of machine learning.

[0344] (Step S2501) The emotion estimation unit 336 acquires two or more types of action source information acquired in Step S1903. Each of the two or more types of action source information here is emotion source information. Also, the two or more types of emotion source information are, for example, emotion source vectors.

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

[0346] (Step S2503) The emotion estimation unit 336 provides two or more types of emotion source information acquired in Step S2501 and the emotion learning model to the prediction processing module of machine learning and executes the module.

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

[0348] (Step S2505) The emotion estimation unit 336 determines whether the score acquired in Step S2504 is greater than or equal to the threshold value. If it is greater than or equal to the threshold value, it proceeds to Step S2506; if it is less than the threshold value, it proceeds to Step S2507.

[0349] (Step S2506) The emotion estimation unit 336 acquires the emotion information acquired in Step S2504 as the output emotion information. It returns to the upper-level process.

[0350] (Step S2507) The emotion estimation unit 336 acquires "empty" emotion information. It returns to the upper-level process.

[0351] Note that in the flowchart of FIG. 25, the processes from Step S2505 to Step S2507 may not be performed.

[0352] Next, an example of the second emotion estimation process in Step S1909 will be described with reference to the flowchart of FIG. 26. The flowchart of FIG. 26 is a process of estimating emotion information by using the emotion learning model for each candidate of two or more types of emotion information through the prediction process of machine learning. That is, the flowchart of FIG. 26 is a process of estimating emotion information by the prediction process of binary classification of machine learning.

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

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

[0355] (Step S2603) The emotion estimation unit 336 refers to the learning management unit 311 to determine whether there is a candidate for the i-th emotion information. If there is a candidate for the i-th emotion information, it proceeds to step S2604; if not, it proceeds to step S2609.

[0356] (Step S2604) The emotion estimation unit 336 obtains the i-th emotion learning model corresponding to the candidate for the i-th emotion information from the learning management unit 311.

[0357] (Step S2605) The emotion estimation unit 336 provides the two or more types of emotion source information obtained in step S2601 and the i-th emotion learning model obtained in step S2604 to a prediction processing module that performs binary classification of machine learning, and executes the module.

[0358] (Step S2606) The emotion estimation unit 336 determines whether the execution result in step S2605 is "true". If it is "true", it proceeds to step S2607; if it is "false", it proceeds to step S2608.

[0359] (Step S2607) The emotion estimation unit 336 temporarily stores in a buffer (not shown) the score, which is part of the execution result in step S2605, in association with the candidate for the i-th emotion information.

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

[0361] (Step S2609) The emotion estimation unit 336 obtains the maximum score and determines whether the maximum score is greater than or equal to the threshold. If the maximum score is greater than or equal to the threshold, it proceeds to step S2610; if it is less than the threshold, it proceeds to step S2611.

[0362] (Step S2610) The emotion estimation unit 336 commends the emotion information corresponding to the maximum score. It returns to the upper-level process.

[0363] (Step S2611) The emotion estimation unit 336 acquires emotion information of "empty". Return to the upper-level process.

[0364] Note that in the flowchart of FIG. 26, the processes from step S2608 to step S2611 may not be performed.

[0365] Next, an example of the third emotion estimation process in step S1909 will be described using the flowchart of FIG. 27. The flowchart of FIG. 27 is a process of 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 emotion estimation unit 336 substitutes 1 for the counter i.

[0368] (Step S2703) The emotion estimation unit 336 determines whether the i-th emotion correspondence information exists in the emotion correspondence table of the learning management unit 311. If the i-th emotion correspondence information exists, go to step S2704; if it does not exist, go 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 the 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 emotion estimation unit 336 increments the counter i by 1. Return to step S2703.

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

[0373] (Step S2708) The emotion estimation unit 336 determines whether the maximum similarity acquired in Step S2707 is greater than or equal to a threshold value. If it is greater than or equal to the threshold value, it proceeds to Step S2709; if it is less than the threshold value, it proceeds to Step S2710.

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

[0375] (Step S2710) The emotion estimation unit 336 acquires "empty" emotion information. Return to the upper-level process.

[0376] Note that in the flowchart of FIG. 27, the processes from Step S2708 to Step S2711 do not necessarily need to be performed.

[0377] Next, an example of the output configuration process in Step S1917 will be described using the flowchart of FIG. 28.

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

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

[0380] (Step S2803) The configuration unit 339 determines whether the i-th time zone stored in the action management unit 313 exists. If the i-th time zone exists, it proceeds to Step S2804; if it does not exist, it returns to the upper-level process. Note that in the action management unit 313, action information and emotion information are stored in association with each time zone of 1 or more.

[0381] (Step S2804) Component 339 determines whether the i-th time period stored in the action management unit 313 is included in the period covered by the calendar template acquired in Step S2801. If it is included, it proceeds to Step S2805; if not, it proceeds to Step S2809.

[0382] (Step S2805) Component 339 acquires the action information corresponding to the i-th time period from the action management unit 313.

[0383] (Step S2806) Component 339 acquires the emotion information corresponding to the i-th time period from the action management unit 313. Note that it is not necessary to acquire the emotion information here.

[0384] (Step S2807) Component 339 constructs time period information that is the information to be arranged in the i-th time period of the calendar, can identify the action information acquired in Step S2805, and can identify the emotion information acquired in Step S2806.

[0385] (Step S2808) Component 339 arranges the time period information constructed in Step S2807 at the position in the calendar specified by the i-th time period.

[0386] (Step S2809) Component 339 increments the counter i by 1 and returns to Step S2803.

[0387] Next, an example of the first action learning process in Step S1924 will be described using the flowchart of FIG. 29. Note that the action learning process is assumed to be performed by a learning unit (not shown), for example. The learning unit may be a learning device different from the action acquisition device 3. The first action learning process is a process of acquiring an action learning model for multi-class classification.

[0388] (Step S2901) The learning unit substitutes 1 for the counter i.

[0389] (Step S2902) The learning unit determines whether the i-th behavior information and the like exist in the behavior management unit 313. If the i-th behavior information and the like exist, it proceeds to step S2903; if not, it proceeds to step S2905.

[0390] (Step S2903) The learning unit uses the i-th behavior information and the like to construct behavior teacher data and append it to a buffer (not shown). Note that the behavior teacher data usually uses two or more types of behavior source information as explanatory variables and the behavior information as the target variable.

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

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

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

[0394] Note that in the flowchart of FIG. 29, the learning unit may store in the learning management unit 311 a behavior correspondence table in which each of the two or more pieces of behavior teacher data in the buffer (not shown) is a record (behavior correspondence information) without performing the learning processes of steps S2905 and S2906.

[0395] Next, an example of the second behavior learning process in step S1924 will be described using the flowchart of FIG. 30. The second behavior learning process is a process of obtaining a binary classification behavior learning model for each of two or more behavior information candidates.

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

[0397] (Step S3002) The learning unit determines whether there is action information of the i-th type. If there is action information of the i-th type, it proceeds to step S3003; if not, it returns to the upper-level process.

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

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

[0400] (Step S3005) The learning unit acquires two or more negative examples that are teacher data in the action management unit 313 and are action teacher data not including the action information of the i-th type.

[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 the learning processing module of machine learning, executes the module, and acquires an action learning model.

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

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

[0404] Next, an example of the first emotion learning process in step S1925 will be described using the flowchart in FIG. 31. It is assumed that the action learning process is performed by a learning unit (not shown), for example. The learning unit may be a learning device different from the action acquisition device 3. The first emotion learning process is a process of acquiring a multi-class emotion learning model.

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

[0406] (Step S3102) The learning unit determines whether the i-th emotion information or the like exists in the behavior management unit 313. If the i-th emotion information or the like exists, it proceeds to step S3103; if it does not exist, it proceeds to step S3105.

[0407] (Step S3103) The learning unit uses the i-th emotion information or the like to construct emotion teacher data and append 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 the target variable.

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

[0409] (Step S3105) The learning unit provides the two or more emotion teacher data stored in a buffer (not shown) to the learning processing module of machine learning, executes the module, and obtains an emotion learning model.

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

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

[0412] Next, an example of the second emotion learning process in step S1925 will be described with reference to the flowchart of FIG. 32. The second emotion learning process is a process of obtaining a binary classification emotion learning model for each candidate of two or more types of emotion information.

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

[0414] (Step S3202) The learning unit determines whether the i-th type of emotion information exists. If the i-th type of emotion information exists, it proceeds to step S3203; if not, it returns to the upper-level process.

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

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

[0417] (Step S3205) The learning unit acquires two or more negative examples that are emotion teacher data in the behavior management unit 313 and do not include the i-th type of emotion information.

[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 the learning processing module of machine learning, executes the module, and acquires an emotion learning model.

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

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

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

[0422] Also, in the storage unit 31 of the behavior acquisition device 3, which is a terminal (e.g., a smartwatch) held by the user "U1", a large number of behavior source information at two or more consecutive times acquired by the processing unit 33 is stored in the behavior source management table shown in FIG. 33 through the above-described processing.

[0423] The behavior source management table (FIG. 33) is a record having "ID", "time information", "physical data", "location information", and "elapsed time". "ID" is information for identifying the record. "Time information" is information for specifying the time, and here it has "month / day", "time", and "day of the week". "Physical data" has "activity data" and "vital data". "Vital data" has "heart rate", "systolic blood pressure", "diastolic blood pressure", and "body temperature". "Heart rate" is the heart rate per unit time (here, "1 minute"). "Systolic blood pressure" is the maximum blood pressure, and "diastolic blood pressure" is the minimum blood pressure. "Location information" is, for example, the location information acquired by the processing described in Embodiment 2. "Elapsed time" is the time elapsed since the start of the same behavior.

[0424] Then, it is assumed that the user "U1" inputs an output instruction to the behavior acquisition device 3. Then, the behavior acquisition device 3 receives the output instruction.

[0425] Next, the behavior estimation unit 335 accesses, for example, the server device 4, receives the behavior learning model and the emotion learning model from the server device 4, and stores the behavior learning model and the emotion learning model in the learning management unit 311.

[0426] Next, the action estimation unit 335 constructs an action source vector having time information, body data, position information, elapsed time, etc. for each record in FIG. 33 by the process described using the flowchart of FIG. 22, for example. Next, the action estimation unit 335 acquires an action learning model from the learning management unit 311. Next, the action estimation unit 335 gives the action source vector and the action learning model to the prediction processing module of machine learning and executes the module. Then, it is assumed that the action estimation unit 335 acquires action information "A1" for the action source vectors from the record of "ID=1" to the record of "ID=289", and acquires action information "A2" for the action source vectors from the record of "ID=290" to the record of "ID=N". Then, the storage unit 338 stores the action information and the action confirmation flag "0" in the action management unit 313 for each record. Note that "0" of the action confirmation flag and the emotion confirmation flag indicates uncertainty, and "1" indicates certainty.

[0427] Also, the emotion estimation unit 336 constructs an emotion source vector having time information, body data, position information, elapsed time, action information, etc. for each record in FIG. 33 by the process described using the flowchart of FIG. 25, for example. Next, the emotion estimation unit 336 acquires an emotion learning model from the learning management unit 311. Next, the emotion estimation unit 336 gives the emotion source vector and the emotion learning model to the prediction processing module of machine learning and executes the module. Then, it is assumed that the emotion estimation unit 336 acquires emotion information "E1" for the action source vectors from the record of "ID=1" to the record of "ID=289", and acquires emotion information "E2" for the action source vectors from the record of "ID=290" to the record of "ID=N". Then, the storage unit 338 stores the emotion information and the emotion confirmation flag "0" in the action management unit 313 for each record.

[0428] As a result of the above processing, the action-emotion management table shown in FIG. 34 is stored in the action management unit 313. The action-emotion management table has two or more records having "ID", "action information", "action confirmation flag", "emotion information", and "emotion confirmation flag".

[0429] Next, for each record in FIGS. 33 and 34, when the accumulation unit 338 pairs with the same action information (for example, "A1"), the time (for example, T 001 , ···, T 002 , T 289 ) of the time zone (for example, "T 001 to T 289 ", namely "TZ1") is obtained, and the time zone and the action information are associated and accumulated.

[0430] Also, for each record in FIGS. 33 and 34, when the accumulation unit 338 pairs with the same emotion information (for example, "E1"), the time (for example, T 001 , ···, T 002 , T 289 ) of the time zone (for example, "T 001 to T 289 ", namely "TZ1") is obtained, and the time zone and the emotion information are associated and accumulated. At this stage, both the action determination flag and the emotion determination flag corresponding to each time zone are "0". And an example of the accumulated information is shown in FIG. 35. FIG. 35 is a time zone information management table. The time zone information management table has one or more records having "ID", "time zone", "action information", "action determination flag", "emotion information", and "emotion determination flag".

[0431] Then, the component 339 uses one or more sets of the time zone, the action information, and the emotion information accumulated by the accumulation unit 338 to perform the process described with reference to the flowchart of FIG. 28, constructs the time zone information for each action information, arranges it in the calendar template, and constructs the output information.

[0432] Next, the output unit 34 outputs the output information. Such an output example is shown in FIG. 36. In FIG. 36, the estimated action information for each time zone of each day in the calendar is displayed.

[0433] Then, if the action information for each time period is correct, user "U1" inputs a "confirmation instruction" for the displayed action information. If the estimated action information is incorrect, user "U1" inputs the correct action information. Also, if the emotion information for each time period is correct, user "U1" inputs a "confirmation instruction" for the output emotion information. If the estimated emotion information is incorrect, user "U1" inputs the correct emotion information. With the user's input, the "action information", "action confirmation flag", "emotion information", and "emotion confirmation flag" in FIG. 35 will be changed.

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

[0435] Also, according to this embodiment, the user's actions can be estimated using the location information corresponding to the time and the user's activity data corresponding to the time.

[0436] Also, according to this embodiment, the user's actions can be estimated with higher accuracy using the 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] Also, according to this embodiment, the emotion of the user during the action can be estimated.

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

[0439] Also, according to this embodiment, the user's actions can be estimated with higher accuracy using the past records of two or more users.

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

[0441] Furthermore, according to this embodiment, using the location information such as indoors obtained by the location information acquisition method described in Embodiment 2, the user's actions can be estimated with high accuracy even in a place where a GPS signal cannot be received.

[0442] Note that the processing in this embodiment may be implemented by software. And this software may be distributed by software download or the like. Also, this software may be recorded on a recording medium such as a CD-ROM and distributed. Note that this also applies to other embodiments in this specification. The software that realizes the information system D in this embodiment is a program as follows. That is, this program causes a computer to function as a time acquisition unit that acquires a time, a position acquisition unit that acquires position information associated with the time, and an action estimation unit that acquires action information for specifying the user's action in a time zone specified by the time included in two or more pieces of action source information including the position information associated with the time, using the two or more pieces of action source information, and an action output unit that outputs the action information in the time zone.

[0443] (Embodiment 4) The difference between this embodiment and Embodiment 3 is as follows. That is, in this embodiment, the action acquisition device is a server, and the action acquisition device estimates the user's action information and emotion information using the position information and the like received from the user's terminal device.

[0444] FIG. 37 is a conceptual diagram of the information system E in this embodiment. The information system E includes an action acquisition device 5, one or two or more terminal devices 6, and one or two or more communication devices B.

[0445] The action acquisition device 5 is a server, for example, a cloud server, an ASP server, and its type is not limited. The action acquisition device 5 is a device that receives action source information such as position information from the user's terminal device 6, estimates the user's action information using the action source information, and transmits the action information to the terminal device 6. The action 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 action information to the terminal device 6.

[0446] The terminal device 6 is a terminal used by the user. The terminal device 6 is, for example, a smartphone, a tablet terminal, a smartwatch, a so-called personal computer, etc., and its type is not limited. The terminal device 6 is a terminal that transmits behavior source information and emotion source information including position information, etc. to the behavior acquisition device 5, receives behavior information and emotion information from the behavior acquisition device 5, and outputs it. Note that the device that transmits the behavior source information and emotion source information to the behavior acquisition device 5 and the device that receives and outputs the behavior information and emotion information from the behavior acquisition device 5 may be different devices.

[0447] FIG. 38 is a block diagram of the information system E in the present embodiment. FIG. 39 is a block diagram of the behavior acquisition device 5.

[0448] The behavior acquisition device 5 includes a storage unit 51, a reception unit 52, a processing unit 53, and a transmission unit 54. The storage unit 51 includes a learning management unit 311 and a behavior management unit 313. The reception unit 52 includes a position acquisition unit 521, an activity acquisition unit 522, and a vital 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 transmission 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 terminal processing unit 64, a terminal transmission unit 65, and a terminal output unit 66. The terminal storage unit 61 includes a map management unit 312. The terminal processing unit 64 includes an intensity acquisition unit 231, a type determination unit 232, a position acquisition unit 332, an activity acquisition unit 333, a vital acquisition unit 334, and a location acquisition unit 337.

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

[0451] The reception 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 position information, activity data, vital data, etc. from the terminal device 6 at once. It is preferable that the receiving unit 52 receives position information, etc. associated with a user identifier at once. The user identifier is information for identifying the user who uses the terminal device 6. The user identifier is, for example, a user ID, a telephone number, an email address, or an identifier of the terminal device 6. The identifier of the terminal device 6 is, for example, an IP address.

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

[0455] The vital acquisition unit 523 receives one or two or more types of vital data from the terminal device 6. Such vital data is associated with time. When the vital acquisition unit 523 receives the vital data, it is preferable to acquire the time from a clock (not shown) and associate the time with the vital data. It is preferable that such vital data is associated with a user identifier.

[0456] The processing unit 53 performs various processes. The various processes are, for example, processes performed by the time acquisition unit 331, the action estimation unit 335, the emotion estimation unit 336, and the storage 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, the estimated action information, the estimated emotion information, and the output information configured by the component 339.

[0458] The action output unit 341 is the action information acquired by the action estimation unit 335, and transmits the action information associated with the time zone to the terminal device 6.

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

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

[0461] The terminal reception unit 62 receives various types of instructions and information. The various types of instructions and information are, for example, an output instruction, a confirmation instruction, the action information modified by the user for the estimated action information, and the emotion information modified by the user for the estimated emotion information.

[0462] The input means for the various types of instructions and information can be anything, such as a touch panel, a keyboard, a mouse, or a menu screen.

[0463] The terminal reception unit 63 receives various types of information from the action acquisition device 5. The various types of information are, for example, output information, action information, and emotion information.

[0464] The terminal processing unit 64 performs various types of processing. The various types of processing are, for example, processing for changing the instructions and information received by the terminal reception unit 62 into the instructions and information of a transmission structure, and processing for changing the information received by the terminal reception unit 63 into an output structure.

[0465] The terminal transmission unit 65 transmits various types of instructions and information. The various types of instructions and information are, for example, an output instruction, a confirmation instruction, the action information to be changed, and the emotion information to be changed.

[0466] The terminal output unit 66 outputs various types of information. The various types of information are, for example, output information, action information, and emotion information.

[0467] The storage unit 51 and the terminal storage unit 61 are preferably non-volatile recording media, but can also be realized with volatile recording media.

[0468] The process by which information is stored in the storage unit 51 or the like is not limited. For example, information may be stored in the storage unit 51 or the like via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 51 or the like, or information input via an input device may be stored in the storage unit 51 or the like.

[0469] The receiving unit 52, the position acquisition unit 521, the activity acquisition unit 522, the vital acquisition unit 523, the transmitting unit 54, the action output unit 341, the emotion output unit 342, the terminal receiving unit 63, and the terminal transmitting unit 65 are realized by, for example, wireless or wired communication means.

[0470] The processing unit 53 and the terminal processing unit 64 can usually be realized from a processor, a memory, etc. The processing procedures of the processing unit 53 or the like are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may also be realized by hardware (a dedicated circuit). Note that the processor may be a CPU, an MPU, a GPU, etc., and its type is not limited.

[0471] The terminal reception unit 62 can be realized by a device driver of an input means such as a touch panel or a keyboard, control software for a menu screen, etc.

[0472] The terminal output unit 66 may or may not be considered to include output devices such as a display and a speaker. The terminal output unit 66 can be realized by driver software for the output device or driver software for the output device and the output device, etc.

[0473] Next, an operation example of the action acquisition device 5 will be described using the flowchart of FIG. 40. In the flowchart of FIG. 40, the description of the same steps as those in FIG. 19 will be omitted.

[0474] (Step S4001) The receiving unit 52 determines whether it has received position information or the like paired with the user identifier from the terminal device 6. If it has received the position information or the like, it proceeds to step S4002; if not, it proceeds to step S4004. Note that the position information or the like is, for example, the user identifier and the position information. The position information or the like is, for example, one or more types of information among the user identifier and the position information, and the activity data and the 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. Also, the time usually includes the hour and minute. The time may include any one or more of the year, month, and day.

[0476] (Step S4003) The storage unit 338 stores the position information or the like received in step S4001 and the time in the action management unit 313 in association with the user identifier. It returns to step S4001.

[0477] (Step S4004) The receiving unit 52 determines whether it has received an output instruction from the terminal device 6. If it has received the output instruction, it proceeds to step S4005; if not, it proceeds to step S4009. Note that the received output instruction is usually associated with the user identifier. Also, the output instruction usually includes period information (e.g., "from December 17, 2023 to December 23, 2023") for specifying the period for acquiring the action information.

[0478] (Step S4005) The action estimation unit 335 substitutes 1 for the counter i.

[0479] (Step S4006) The action estimation unit 335 determines whether the i-th action source information paired with the user identifier associated with the output instruction received in step S4004 exists in the action management unit 313. If the i-th action source information exists, it proceeds to step S4007; if it does not exist, it proceeds to step S1917.

[0480] (Step S4007) The action estimation unit 335 determines whether there is action information associated with the i-th action source information. If the action information exists, it proceeds to step S4008; if it does not exist, it proceeds to step S1904. Note that when there is action information associated with the action source information, usually, action information estimation has already been performed using the action source information.

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

[0482] (Step S4009) The reception unit 52 determines whether it has received information etc. from the terminal device 6. If it has received the information etc., it proceeds to step S1920; if it has not received it, it proceeds to step S4010. Note that the information etc. is, for example, a confirmation instruction, action information to be corrected, or emotion information to be corrected.

[0483] (Step S4010) The reception unit 52 determines whether it has received a learning instruction from the terminal device 6. If it has received the learning instruction, it proceeds to step S1924; if it has not received it, it returns to step S4001.

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

[0485] Note that in the flowchart of FIG. 40, the process ends due to a power-off or a process end interrupt.

[0486] Next, an operation example of the terminal device 6 will be described using the flowchart of FIG. 41. In the flowchart of FIG. 41, the description of the same steps as those in FIG. 19 will be omitted.

[0487] (Step S4101) The terminal transmission unit 65 acquires the user identifier in the terminal storage unit 61, and transmits the position information and the like acquired in step S1903 to the behavior acquisition device 5 in association with the user identifier.

[0488] (Step S4102) The terminal transmission unit 65 transmits the output instruction received in step S1916 to the behavior acquisition device 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 reception unit 63 determines whether or not output information has been received from the behavior acquisition device 5. If the output information has been received, the process proceeds to step S4104; if not, the process returns to step S4103.

[0490] (Step S4104) The terminal processing unit 64 configures the output information to be output using the received output information. The terminal output unit 66 outputs the output information. The process returns to step S1901.

[0491] (Step S4105) The terminal transmission unit 65 transmits the information and the like acquired from the information input received in step S1919 to the behavior acquisition device 5 in association with the user identifier in the terminal storage unit 61. The process returns to step S1901.

[0492] Note that the information and the like are, for example, a confirmation instruction, changed behavior information, and changed emotion information. Note that the confirmation instruction includes information specifying the behavior information or emotion information to be confirmed. The changed behavior information is associated with the information specifying the behavior information to be corrected. The changed emotion information is associated with the information specifying the emotion information to be corrected.

[0493] Note that in the flowchart of FIG. 41, the process ends due to a power-off or a processing end interrupt.

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

[0495] Also, according to this embodiment, the user's behavior can be estimated using the position information corresponding to the time and the activity data of the user corresponding to the time.

[0496] Also, according to this embodiment, the user's behavior can be estimated with higher accuracy using the position information corresponding to the time, the activity data of the user corresponding to the time, and the vital data of the user corresponding to the time.

[0497] Also, according to this embodiment, the emotion of the user during the behavior can be estimated.

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

[0499] Also, according to this embodiment, the user's behavior can be estimated with higher accuracy using the past records of two or more users.

[0500] Also, 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 that realizes the behavior acquisition device 5 in this embodiment is a program as follows. That is, this program causes a computer to function as a time acquisition unit that acquires time, a position acquisition unit that acquires position information associated with the time, and an action estimation unit that acquires action information that specifies the user's behavior in the time zone specified by the time included in two or more pieces of action source information including the position information associated with the time, and an action output unit that outputs the action information in the time zone.

[0502] (Embodiment 5) In this embodiment, a behavior analysis apparatus that acquires and outputs a disturbance score for specifying the degree of disturbance of a user's behavior will be described. In particular, in this embodiment, a behavior analysis apparatus that acquires and outputs a disturbance score using the time-series behavior information of a user and reference information will be described. The reference information is, for example, one or two or more types of information among information based on the user's past time-series information, a learning model based on time-series behavior information, recommended behavior information, and other-reference information. The reference information may include, for example, environmental information.

[0503] In this embodiment, a behavior analysis apparatus that acquires and outputs the factors causing the disturbance of behavior will be described.

[0504] In this embodiment, a behavior analysis apparatus that makes recommendations for improving the disturbance of behavior will be described.

[0505] In this embodiment, a behavior analysis apparatus that acquires and outputs the improvement degree of the disturbance of behavior will be described. In this embodiment, a behavior analysis apparatus that acquires and outputs the improvement degree when the improvement degree output condition is satisfied will be described.

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

[0507] In this embodiment, a behavior analysis apparatus that acquires and outputs a long-term disturbance score will be described.

[0508] FIG. 42 is a conceptual diagram of the information system F in this embodiment. The information system F includes a behavior analysis apparatus 7, one or two or more terminal apparatuses 8, and two or three or more communication apparatuses B.

[0509] The behavior analysis device 7 is a device that acquires one or more pieces of information among the disturbance score, disturbance factors, recommendation information, improvement degree, recovery period, and long-term disturbance score described later, and transmits the information to the terminal device 8. Further, the behavior analysis device 7 is, for example, a device that receives behavior source information such as position 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 its 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 behavior acquisition device 3. In such a case, the behavior analysis device 7 is, for example, a smartphone, a tablet terminal, a smartwatch, a so-called personal computer, etc., and its type is not limited.

[0511] The terminal device 8 is a terminal used by the user. The terminal device 8 is, for example, a smartphone, a tablet terminal, a smartwatch, a so-called personal computer, etc., and its type is not limited. The terminal device 8 transmits behavior source information and emotion source information including, for example, position information to the behavior analysis device 7, and receives and outputs the disturbance score, disturbance factors, recommendation information, improvement degree, recovery period, long-term disturbance score, behavior information, or emotion information described later from the behavior analysis device 7. Note that the device that transmits the behavior source information and emotion source information to the behavior analysis device 7 and the device that receives and outputs the disturbance score and disturbance factors from the behavior analysis device 7 may be different devices.

[0512] FIG. 43 is a block diagram of the information system F in the present embodiment. FIG. 44 is a block diagram of the behavior analysis device 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 part of the components of the behavior acquisition device 5. When the behavior analysis device 7 is stand-alone, the behavior analysis device 7 may include all or part of the components of 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 reference 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 sign 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 determination 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 terminal 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 terminal processing unit 64 includes an intensity acquisition unit 231, a type determination unit 232, a position acquisition unit 332, an activity acquisition unit 333, a vital sign acquisition unit 334, and a location acquisition unit 337.

[0516] Various types of information are stored in the storage unit 71 that constitutes the behavior analysis device 7. The various types of information are, for example, the learning information described above, the behavior information described above, the reference information described later, the recommendation information described later, the learning model described later, and various conditions described later. Needless to say, the various conditions may be embedded in the program.

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

[0518] The reference information is information that serves as a reference when obtaining a disturbance score, which is a measure of the degree of disturbance in a user's behavior. Note that the disturbance in behavior may also be referred to as a disturbance in life. The reference information is the underlying information for obtaining the degree of disturbance in a user's behavior corresponding to time-series information. The reference information is, for example, one or more types of information among self-reference information, a learning model, recommended behavior information, and other-reference information. The reference information may also include, for example, environmental reference information.

[0519] The disturbance score is information that identifies the degree of disturbance in behavior. The disturbance in behavior may also be referred to as a disturbance in life. The disturbance score may be, for example, on a scale of 0 to 100, in five levels, in ten levels, etc. The disturbance score may also be in two levels (whether or not there is a disturbance). The disturbance score may also be a measure of the quality of life (the degree of non-disturbance).

[0520] Note that the disturbance score may be a measure of the degree of non-disturbance rather than the degree of disturbance. It can be said that the degree of non-disturbance is a measure of the quality of life.

[0521] Hereinafter, self-reference information, a learning model, recommended behavior information, other-reference information, and environmental reference information, which are examples of reference information, will be described in detail. (1) Self-reference information

[0522] Self-reference information is information based on the past time-series information of a single user. The reference information is, for example, a set of the average (in the case of normal behavior) behavior information and time information of a single user for a predetermined period (e.g., one day, one week). The reference information is, for example, a set of behavior information and time information when there is no disturbance for a single user for a predetermined period. The behavior information included in the reference information may be associated with the time (length) when the behavior was performed. The behavior information included in the reference information has, for example, a start time and an end time of the behavior. The reference information is, for example, a vector. The vector is, for example, (the time of behavior information 1, the start time of behavior information 1, the end time of behavior information 1, the time of behavior information 2, the start time of behavior information 2, the end time of behavior information 2, ···, the time of behavior information n, the start time of behavior information n, the end time of behavior information n). The vector is, for example, (the time-related information of behavior information 1, the time-related information of behavior information 2, ···, the time-related information of behavior information n). The time-related information is one or more pieces of information among time, start time, or end time. "Behavior information 1", "behavior information 2", "behavior information 3", ···, "behavior information n" are, for example, "sleep", "breakfast", "movement", "work", "shopping", "dinner", "drinking party", "game". (2) Learning model,

[0523] A learning model is a model obtained by performing learning processing of machine learning using two or more pieces of teacher data. The teacher data here has an explanatory variable based on the past time-series information of one or two or more users and an objective variable which is disturbance information regarding the degree of disturbance of the behavior of the user. Two or more pieces of teacher data are data for creating a learning model used for a single user, and may be information based on the time-series information of the single user. Also, two or more pieces of 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 two or more users including others. A learning model is usually a model created by the learning unit 731.

[0524] The disorder information is information related to the disorder of the user's behavior. The disorder information is, for example, a disorder score, information indicating whether it is disordered, information indicating the level of physical condition, information indicating whether the physical condition is good, information indicating the level of mental condition, information indicating whether the mental condition is good, information indicating the level of the overall physical and mental condition, and information indicating whether the overall physical and mental condition is good. The disorder information is, for example, information input by the user. The disorder information is, for example, a score obtained from one or two or more types of vital data of the user (for example, heart rate, heart rate variability, blood pressure (up or / and down), respiratory rate per unit time, body temperature) or information indicating whether the physical condition is good. Note that the technology for obtaining information indicating whether a score or physical condition is good from vital data is a known technology. The disorder information may be information input by the user in association with time-series information.

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

[0526] Also, the learning model based on the time-series information of one user may be considered as an example of self-reference information. The learning model based on the time-series information of one or two or more users including others may be considered as an example of other-reference information. (3) Recommended action information

[0527] The recommended action information is information that identifies the recommended actions. The recommended action information is, for example, information that identifies common sense that is less likely to cause disruptions in actions, and information that identifies common sense that is more likely to cause disruptions in actions. The recommended action information that identifies the recommended actions is, for example, "number of meals = 3 times / day", "6 hours <= sleep time <= 10 hours", "20:00 <= bedtime <= 24:00", "5:00 <= wake-up time <= 9:00". The recommended action information that identifies the non-recommended actions is, for example, "number of drinking parties >= 3 times / week", "drinking parties for 3 consecutive days", "game time >= 3 hours / day". Note that the recommended action information may be information that identifies the non-recommended actions. However, in this specification, usually, the recommended action information is described as information that identifies the recommended actions. (4) Other-person reference information

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

[0529] The environmental reference information is reference environmental information. The environmental information is information that identifies the environment of the place of action. The environmental information is, for example, information that identifies the weather, temperature, humidity, and amount of ultraviolet rays. The environmental reference information is, for example, "sunny" which is the appropriate weather, "15 degrees <= temperature <= 25 degrees" which is the appropriate temperature range, "40% <= humidity <= 60%" which is the appropriate humidity range, and the appropriate amount of ultraviolet rays.

[0530] In the recommendation management unit 712, recommendation source information is stored in association with each one or more factor conditions. The factor condition is a condition related to the disturbance factor. The factor condition may be the same as the disturbance condition. The disturbance factor is a factor of the disturbance of behavior. The disturbance factors are, for example, "sleep" (e.g., short sleep time, late bedtime, late wake-up time), "breakfast" (e.g., late breakfast time, skipping breakfast), "drinking party" (large number of drinking parties).

[0531] The recommendation source information is the information that is the source of the recommendation information. The recommendation source information is the information for constructing the recommendation information. The recommendation source information has, for example, one or more variables. For the variables, for example, the disturbance factor and the element information associated with the disturbance factor are substituted. The recommendation source information may be the recommendation information. The recommendation information is the information recommended to the user for improving the disturbance of the user's behavior. The recommendation information is, for example, a character string, voice, still image, moving image, and its data type is not limited.

[0532] The reception unit 72 receives one or more time series information. The method by which the reception unit 72 receives the time series information is not limited. The reception unit 72 only needs to acquire the time series information.

[0533] The time series information is the time series behavior information of the user. The time series information includes two or more pieces of behavior information in time series. The behavior information is the information for specifying the behavior of the user. The behavior information is, for example, associated with the time information. The time information is the information for specifying the time when the user acts. The time information is, for example, the start time and the end time. The time information is, for example, time.

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

[0535] Here, reception generally refers to the reception of information transmitted via a wired or wireless communication line. However, it may also include the reception of information input from input devices such as keyboards, mice, and touch panels, and the reception of information read from recording media such as optical disks, magnetic disks, and semiconductor memories.

[0536] The processing unit 73 performs various processes. The various processes are, for example, the processes performed by the time acquisition unit 331, the behavior estimation unit 335, the emotion estimation unit 336, the storage unit 338, the configuration unit 339, the learning unit 731, the score acquisition unit 732, the long-term score acquisition unit 733, the determination 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 stores it in the reference management unit 711. The learning unit 731 creates a learning model by performing learning processing of machine learning using two or more pieces of teacher data. The teacher data here has an explanatory variable based on the past time-series information of one or two or more users and an objective variable which is disturbance information regarding the degree of disturbance of the behavior of the user.

[0538] The learning unit 731, for example, for each one or two or more users, provides two or more pieces of teacher data having an explanatory variable based on the past time-series information of the user and an objective variable which is disturbance information regarding the degree of disturbance of the behavior of the user to a module that performs learning processing of machine learning, executes the module, and acquires a learning model. As described above, the algorithm of machine learning is not limited.

[0539] The score acquisition unit 732 acquires a disturbance score which is the degree of disturbance of the user's behavior using the time-series information received by the reception unit 72 and the reference information of the reference management unit 711. The score acquisition unit 732 usually acquires a disturbance score which is a score regarding the difference between the time-series information received by the reception unit 72 and the reference information of the reference management unit 711.

[0540] The score acquisition unit 732 may acquire a first disturbance score and a second disturbance score, which are disturbance scores at two respective times. The score acquisition unit 732 may acquire disturbance scores for two or more pieces of time-series information.

[0541] Hereinafter, an example of the process of the score acquisition unit 732 will be described for each case where the reference information is self-reference information, a learning model, recommended action information, and other-reference information. (1) Self-reference information

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

[0543] The score acquisition unit 732 acquires a vector from the time-series information of a predetermined period (for example, one day, one week) received by the reception unit 72. Such a vector is referred to as an inspection vector. Next, the score acquisition unit 732 acquires a larger disturbance score as the difference between the inspection vector and the self-reference vector is larger.

[0544] For example, the score acquisition unit 732 acquires the distance between the inspection vector and the self-reference vector. Next, the score acquisition unit 732 acquires a disturbance score by using the distance or an increasing function having 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, when the difference between the elements is equal to or greater than a threshold value, the disturbance score is counted up to obtain a final disturbance score.

[0546] Also, for example, the score acquisition 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 the self-reference information is a set of pairs of action information and time information

[0547] The score acquisition unit 732 determines whether or not the action information included in the time-series information for the predetermined period received by the reception unit 72 exists in the self-reference information.

[0548] For example, if there is action information that does not exist in the self-reference information, the score acquisition unit 732 increments the disorder score. Action information that does not exist in the self-reference information is, for example, information on actions that are not normally performed and can be a factor in action disorder.

[0549] In addition, the score acquisition unit 732 acquires the difference (for example, one or more pieces of information among the difference in time length, the difference in start time, and the difference in end time) between the time information paired with the action information included in the time-series information for the predetermined period received by the reception unit 72 and the time information paired with the action information existing in the self-reference information. The greater such a difference, the greater the disorder score acquired by the score acquisition unit 732.

[0550] In addition, the score acquisition unit 732 acquires the frequency of the action information (for example, "meal", "drinking party") included in the time-series information received by the reception unit 72. The score acquisition unit 732 acquires the frequency of the action information in the self-reference information. Next, when the difference between the two frequencies is equal to or greater than a threshold value, the score acquisition unit 732 acquires a large disorder score. (2) Learning model

[0551] The score acquisition unit 732 performs a prediction process of machine learning using the time-series information received by the reception unit 72 and the learning model, and acquires a disorder score.

[0552] For example, the score acquisition unit 732 constructs an inspection vector from the time series information received by the reception unit 72. The inspection vector here is, for example, (time of action information 1, start time of action information 1, end time of action information 1, time of action information 2, start time of action information 2, end time of action information 2, ···, time of action information n, start time of action information n, end time of action information n), or (time-related information of action information 1, time-related information of action information 2, ···, time-related information of action information n), etc., but the structure does not matter.

[0553] Next, the score acquisition unit 732 provides the inspection vector and the learning model to a module that performs prediction processing of machine learning, executes the module, and acquires a perturbation score. Note that the score acquisition unit 732 provides the vector and the learning model to a module that performs prediction processing of machine learning, executes the module, acquires information on whether it is perturbed, and when acquiring a target variable of "perturbed", acquires the score returned by the module that performs prediction processing, and may acquire a perturbation score based on the score. Such a perturbation score is a score returned by the module or a score obtained by an increasing function using the score returned by the module as a parameter. The score acquisition unit 732 acquires a perturbation score of "0", for example, when acquiring a target variable of "not perturbed".

[0554] Note that the module that performs prediction processing of machine learning may be a module that returns a perturbation score or a module that returns a predicted value that is the basis of the perturbation score.

[0555] Also, the algorithm of the prediction processing of machine learning may be deep learning, random forest, decision tree, SVM, etc. However, it is preferable that the algorithm of the prediction processing of machine learning is a random forest. This is because in a random forest, the influence degree of each explanatory variable given to the output target variable can be acquired. The action information corresponding to the explanatory variable with a large influence degree constitutes a perturbation factor described later. The explanatory variable with a large influence degree is, for example, an explanatory variable with the first rank of influence degree, an explanatory variable with the Nth rank or higher of influence degree, an explanatory variable with an influence degree 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 acquisition unit 732 acquires an inspection vector from the time series information of a predetermined period (for example, one day, one week) received by the reception unit 72. Next, the score acquisition unit 732 acquires a larger disturbance score as the difference between the inspection vector and the recommended action vector is larger.

[0557] For example, the score acquisition unit 732 acquires the distance between the inspection vector and the recommended action vector. Next, the score acquisition unit 732 acquires a disturbance score by using the distance or an increasing function using the distance as a parameter.

[0558] Also, for example, the score acquisition unit 732 acquires the difference between each element of the inspection vector and each element of the recommended action vector for each element, and for each element, when the difference between the elements is equal to or greater than a threshold value or greater than the threshold value, the disturbance score is counted up to obtain a final disturbance score.

[0559] Also, for example, the score acquisition unit 732 acquires the difference between each element of the inspection vector and each element of the recommended action vector for each element, and obtains 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 acquisition unit 732 determines whether the action information included in the time series information of a predetermined period received by the reception unit 72 exists in the recommended action information. When the recommended action information is information for specifying an action to be recommended, if the action information does not exist in the recommended action information, the score acquisition unit 732 acquires a larger disturbance score than when it exists in the recommended action information. When the recommended action information is information for specifying an action not to be recommended, if the action information exists in the recommended action information, the score acquisition unit 732 acquires a larger disturbance score than when it does not exist in the recommended action information.

[0561] For example, the score acquisition unit 732 acquires time information (e.g., time, start time, end time) that pairs with the action information included in the time-series information for a predetermined period received by the reception unit 72. For each piece of action information, the score acquisition unit 732 determines whether the time information that pairs with the action information satisfies the condition of the time information that pairs with the action information included in the recommended action information (e.g., the suitable range of sleep time). When the condition of the time information that pairs with the action information included in the recommended action information is not satisfied, the score acquisition unit 732 acquires a larger disorder score than when it is satisfied. (4) Other reference information (4-1) When the other reference information is a vector (other reference vector)

[0562] The score acquisition unit 732 acquires an inspection vector from the time-series information for a predetermined period (e.g., one day, one week) received by the reception unit 72. Next, the score acquisition unit 732 acquires a larger disorder score as the difference between the inspection vector and the other reference vector is larger.

[0563] For example, the score acquisition unit 732 acquires the distance between the inspection vector and the other reference vector. Next, the score acquisition unit 732 acquires a disorder score based on the distance or an increasing function using the distance as a parameter.

[0564] Also, for example, the score acquisition unit 732 acquires the difference between each element of the inspection vector and each element of the other reference vector for each element, and for each element, when the difference between the elements is equal to or greater than a threshold value, the disorder score is incremented to obtain a final disorder score.

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

[0566] The score acquisition unit 732 determines whether or not the behavior information included in the time series information for the predetermined period received by the reception unit 72 exists in the other person's reference information for each piece of behavior information.

[0567] If there is behavior information that does not exist in the other person's reference information, the score acquisition unit 732 increments the disorder score. Behavior information that does not exist in the other person's reference information is information on behaviors that two or more people usually do not perform.

[0568] In addition, the score acquisition unit 732 obtains the difference (for example, one or more pieces of information among the difference in time length, the difference in start time, and the difference in end time) between the time information paired with the behavior information included in the time series information for the predetermined period received by the reception unit 72 and the time information paired with the behavior information existing in the other person's reference information. The greater such a difference, the greater the disorder score obtained by the score acquisition unit 732.

[0569] In addition, the score acquisition unit 732 obtains the frequency of the behavior information (for example, "meal", "drinking party") included in the time series information for the predetermined period received by the reception unit 72. The score acquisition unit 732 obtains the frequency of the behavior information in the other person's reference information. Next, when the difference between the two frequencies is equal to or greater than a threshold value, the score acquisition unit 732 obtains a large disorder score. In addition, the score acquisition unit 732 may obtain, for example, the comparison result of the scores for two or more periods and obtain a score based on the comparison result. The score acquisition unit 732 obtains, for example, the change (for example, difference, ratio) in the scores for the same month of the previous year (June 2023 and June 2024) or one week of the previous month, and obtains a score by a function using such a 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 obtained.

[0570] The long-term score acquisition unit 733 obtains a long-term disorder score that specifies the degree of disorder in long-term behavior using two or more disorder scores obtained by the score acquisition unit 732. Here, the long term is, for example, three months (per 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 acquisition unit 733 acquires the number of times the determination unit 734 determines that the disturbance condition is satisfied, and uses the number of times to acquire a long-term disturbance score. The long-term score acquisition unit 733 acquires a larger long-term disturbance score as the number of times the determination unit 734 determines that the disturbance condition is satisfied increases.

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

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

[0574] The determination unit 734 determines whether the first disturbance score and the second disturbance score satisfy the recovery condition. The recovery condition is a condition for acquiring a recovery period. The recovery condition is, for example, that the disturbance condition is no longer satisfied, that the disturbance condition is no longer satisfied after satisfying the disturbance condition, that the improvement degree is greater than or equal to a threshold value or greater than the threshold value, that the improvement degree is greater than or equal to a threshold value or greater than the threshold value and the second disturbance score is less than or equal to the threshold value or less than the threshold value, or that the second disturbance score is less than or equal to the threshold value or less than the threshold value.

[0575] The determination unit 734 determines whether each of 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 acquisition unit 735 acquires, from the behavior information included in the time series information corresponding to the disturbance score and received by the reception unit 72, a disturbance factor related to the behavior information that is a cause of the disturbance. It is preferable for the factor acquisition unit 735 to acquire the disturbance factor when the determination unit 734 determines that the disturbance condition is satisfied.

[0577] The factor acquisition unit 735 acquires, for example, the behavior information corresponding to the explanatory variable with a large influence degree obtained as a result of the prediction process by the random forest, and acquires a disturbance factor having the behavior information and the time information paired with the behavior information.

[0578] The factor acquisition unit 735 acquires, for example, the behavior information that has caused the disturbance score acquired by the score acquisition unit 732 to increase as it satisfies the adoption condition. The factor acquisition unit 735 acquires a disturbance factor having the behavior information and the time information paired with the behavior information. The adoption condition is a condition for the behavior information to be adopted as a disturbance factor. The adoption condition is, for example, that the increase number of the disturbance score is equal to or greater than a threshold value. The adoption condition is, for example, that the number of appearances of specific behavior information for a predetermined period to be adopted as a disturbance factor is equal to or greater than a threshold value (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, from the recommendation management unit 712, recommendation source information associated with a factor condition that matches the disturbance factor acquired by the factor acquisition unit 735. The recommendation acquisition unit 736 substitutes the disturbance factor into the variable part of the recommendation information, which is the recommendation source information, or the recommendation source information, and acquires the recommendation information.

[0580] The improvement degree acquisition unit 737 acquires the improvement degree 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 acquisition unit 732 using time series information at different times.

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

[0582] It is preferable that the improvement degree acquisition unit 737 acquires the improvement degree when the first disturbance score and the second disturbance score satisfy the improvement degree output condition. 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 the recovery period, which is the difference between the first time information associated with the first disturbance score and the second time information associated with the second disturbance score. It is preferable that the recovery period acquisition unit 738 acquires the recovery period when the determination unit 734 determines that the recovery condition is satisfied. The recovery condition is a condition for acquiring the recovery period. The recovery condition is, for example, that the second disturbance score does not satisfy the disturbance condition.

[0584] The output unit 74 outputs various types of information. The various types of information are, for example, disturbance scores, long-term disturbance scores, behavior information, emotion information, or location information.

[0585] Here, the output usually refers to the output to the terminal device 8, but it may also be a concept including display on a display, projection using a projector, printing by a printer, sound output, transmission to an external device, storage on a recording medium, delivery of the processing result to other processing devices or other programs, etc.

[0586] The score output unit 741 outputs the disturbance score acquired by the score acquisition unit 732. The output mode of the disturbance score is not limited. It is preferable that the score output unit 741 outputs the disturbance score in association with the user identifier. It is preferable that the score output unit 741 outputs the disturbance score for each predetermined period (for example, one week, one day).

[0587] The long-term score output unit 742 outputs the long-term disturbance score acquired by the long-term score acquisition unit 733. The output mode of the long-term disturbance score is not limited. It is preferable that the long-term score output unit 742 outputs the long-term disturbance 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 improvement degree 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 reception unit 83 that configures the terminal device 8 receives various types of information. The terminal reception unit 83 receives various types of information from the behavior analysis device 7. The various types of information are, for example, behavior information, emotion information, disturbance score, long-term disturbance score, disturbance factor, recommendation information, improvement degree, or recovery period.

[0593] The terminal output unit 86 outputs various types of information. The various types of information are, for example, behavior 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 reference management unit 711, and the recommendation management unit 712 preferably use a non-volatile recording medium, but can also be realized using a volatile recording medium.

[0595] The process by which information is stored in the storage unit 71 or the like is not limited. For example, information may be stored in the storage unit 71 or the like via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 71 or the like, or information input via an input device may be stored in the storage unit 71 or the like.

[0596] The reception unit 72 is preferably realized by wireless or wired communication means, but may also be realized by 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, the learning unit 731, the score acquisition unit 732, the long-term score acquisition unit 733, the determination 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 can usually be realized from a processor, memory, etc. The processing procedures of the processing unit 73 or the like are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may also be realized by hardware (a dedicated circuit). Note that the processor may be a CPU, MPU, GPU, etc., and its type is not limited.

[0598] The output unit 74, the action output unit 341, the emotion output unit 342, the score output unit 741, the long-term score output unit 742, the factor output unit 743, the recommendation output unit 744, the improvement degree output unit 745, and the recovery period output unit 746 are 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 or the like.

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

[0600] The terminal output unit 86 may or may not include output devices such as a display and a speaker. The terminal output unit 86 may be realized by driver software for the output device or by the driver software for the output device and the output device or the like.

[0601] Next, an operation example of the behavior analysis device 7 will be described using the flowchart of FIG. 45. In the flowchart of FIG. 45, the same processing as that of the behavior acquisition device 5 is not included. The same processing as that of the behavior acquisition device 5 is the processing of the flowchart of FIG. 40.

[0602] (Step S4501) The processing unit 73 determines 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] Note that the score acquisition condition is a condition for acquiring a disturbance score. The score acquisition condition is, for example, that a predetermined time (for example, 24:00 every day) has arrived, the reception unit 72 has received a score acquisition instruction from the user, or the reception unit 72 has received time series information. Note that the score acquisition instruction received by the reception unit 72 includes a user identifier. Further, the time series information received by the reception unit 72 is associated with the user identifier.

[0604] (Step S4502) The processing unit 73 substitutes 1 for the counter i.

[0605] (Step S4503) The processing unit 73 determines whether or not the i-th user for which the disturbance score is to be acquired exists. If the i-th user exists, the process proceeds to step S4504, and if not, the process returns to step S4501.

[0606] (Step S4504) The score acquisition unit 732 acquires the disturbance score of the i-th user. An example of such score acquisition processing will be described using the flowcharts of FIGS. 46, 47, and 48.

[0607] (Step S4505) The score output unit 741 stores the noise score obtained in step S4504 in the behavior management unit 313 in association with the i-th user identifier and the time series information used when obtaining the noise score.

[0608] (Step S4506) The determination unit 734 determines whether the noise score obtained in step S4504 meets the noise condition. If it meets the noise condition, it proceeds to step S4507; if it does not meet the noise condition, it proceeds to step S4512.

[0609] (Step S4507) The factor acquisition unit 735 acquires the factor causing the noise in the user's behavior. An example of such factor acquisition processing will be described using the flowchart of FIG. 49.

[0610] (Step S4508) The recommendation acquisition unit 736 acquires recommendation information. An example of such recommendation acquisition processing will be described using the flowchart of FIG. 50.

[0611] (Step S4509) The processing unit 73 or a component not shown configures the output information. The output information is the information to be output. The output information includes a noise score. Here, the output information includes, for example, one or more noise 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 and returns to step S4503.

[0614] (Step S4512) The improvement degree acquisition unit 737 acquires the past noise score of the i-th user. The past noise score is preferably the most recent noise score.

[0615] (Step S4513) The improvement degree acquisition unit 737 determines whether the disturbance score acquired in Step S4504 and the disturbance score acquired in Step S4512 satisfy the improvement degree output condition. If the improvement degree output condition is satisfied, it proceeds to Step S4514; if not, it proceeds to Step S4515.

[0616] (Step S4514) The improvement degree acquisition unit 737 acquires the improvement degree using the disturbance score acquired in Step S4504 and the disturbance 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 when acquiring the disturbance score.

[0617] (Step S4515) The determination unit 734 determines whether the disturbance score acquired in Step S4504 and the disturbance score acquired in Step S4512 satisfy the recovery condition. If the recovery condition is satisfied, it proceeds to Step S4516; if not, it proceeds to Step S4517.

[0618] (Step S4516) The recovery period acquisition unit 738 acquires the recovery period, which is the difference between the time information associated with the disturbance score acquired in Step S4504 and the time information associated with the disturbance 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 when acquiring the disturbance score.

[0619] (Step S4517) The processing unit 73 or a component not shown in the figure constitutes the output information. The output information includes the disturbance score. Here, the output information includes, for example, the improvement degree and the recovery period.

[0620] (Step S4518) The output unit 74 outputs the output information configured 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 determines whether the long-term score acquisition condition is satisfied. If the long-term score acquisition condition is satisfied, it proceeds to step S4520; if not, it proceeds to step S4525. Note that the long-term score acquisition condition is, for example, that the disturbance score for a predetermined period has been accumulated and an instruction from the user has been received.

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

[0623] (Step S4521) The processing unit 73 determines whether the i-th user who has the long-term disturbance score exists. If the i-th user exists, it proceeds to step S4522; if not, it returns to step S4501.

[0624] (Step S4522) The long-term score acquisition unit 733 acquires the long-term disturbance score of the i-th user. An example of such long-term score acquisition processing will be described using the flowchart of FIG. 51.

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

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

[0627] (Step S4525) The processing unit 73 determines whether the learning condition is satisfied. If the learning condition is satisfied, it proceeds to step S4526; if not, it returns to step S4501. Note that the learning condition is a condition for creating a learning model. The learning condition is, for example, that the number of time-series information of the user has reached a threshold or an instruction from the user has been received.

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

[0629] (Step S4527) The processing unit 73 determines whether there is an i-th user who creates a learning model. If there is an i-th user, it proceeds to step S4528; if not, it 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 obtain a learning model. An example of such learning processing will be described using the flowchart of FIG. 52.

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

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

[0633] Note that in the flowchart of FIG. 45, the processing ends due to a power-off or a processing end interrupt.

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

[0635] (Step S4601) The score acquisition unit 732 acquires the time series information of the target user. Such time series information is, for example, time series information paired with the user identifier of the target user and time series information received by the reception unit 72.

[0636] (Step S4602) The score acquisition unit 732 determines whether to use environmental information to acquire a disturbance score. If it uses environmental information, it proceeds to step S4603; if it does not use environmental information, it proceeds to step S4604.

[0637] (Step S4603) The score acquisition unit 732 acquires the time-series information of the target user and the corresponding environmental information.

[0638] (Step S4604) The score acquisition unit 732 constructs a vector using the time-series information of the target user, or the time-series information of the target user and the environmental information. Such a vector is referred to as an inspection vector.

[0639] The inspection vector is, for example, (time of action information 1, start time of action information 1, end time of action information 1, time of action information 2, start time of action information 2, end time of action information 2, ···, time of action information n, start time of action information n, end time of action 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 of 1 or more.

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

[0641] (Step S4606) The score acquisition unit 732 calculates the distance (for example, cosΘ) between the inspection vector and the reference vector.

[0642] (Step S4607) The score acquisition unit 732 acquires a disturbance score using an increasing function with the distance obtained in Step S4606 as a parameter. Return to the upper-level 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 the case of machine learning.

[0644] (Step S4701) The score acquisition unit 732 acquires a learning model from the reference management unit 711. Here, it is preferable for the score acquisition unit 732 to acquire the learning model corresponding to the user identifier of the target user.

[0645] (Step S4702) The score acquisition unit 732 provides the inspection vector and the learning model obtained in step S4701 to a module that performs the prediction process of machine learning, executes the module, and acquires the prediction result. Note that the prediction result is, for example, a noise score. The prediction result is, for example, whether it is noisy or not and the score returned by the module.

[0646] (Step S4703) The score acquisition unit 732 acquires the noise score based on the prediction result. Return to the upper-level process.

[0647] For example, when the prediction result is "not noisy (for example, "0"), the score acquisition unit 732 acquires the noise score "0", and when the prediction result is "noisy (for example, "1"), the score acquisition unit 732 acquires the noise score calculated by an increasing function using 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 of FIG. 48. In the flowchart of FIG. 48, the description of the same steps as in the flowchart of FIG. 46 will be omitted. Note that the third example is a case where the difference from the element information of the reference information is determined for each element information included in the time series information.

[0649] (Step S4801) The score acquisition unit 732 acquires reference information including time series information from the reference management unit 711. The reference information may include one or more pieces of environment information. The one or more pieces of environment information are, for example, "<weather> sunny <temperature> 15 degrees to 25 degrees <humidity> 40% to 60%".

[0650] (Step S4802) The score acquisition unit 732 substitutes 1 for the counter i.

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

[0652] (Step S4804) The score acquisition unit 732 acquires the i-th element information from the time series information of the target user or the like. Note that the element information is action information, time information, or environmental information.

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

[0654] (Step S4806) The score acquisition unit 732 acquires the difference between the time information corresponding to the action information of the i-th element information and the time information corresponding to the action information in the reference information, or the difference between the environmental information (for example, temperature) of the i-th element information and the environmental information (for example, temperature) in the reference information. The score acquisition unit 732 temporarily accumulates the difference in association with the i-th element information.

[0655] Note that the difference is, for example, one or more differences among the difference in time, the difference in start time, and the difference in end time. The difference is, for example, the difference in environmental information or whether the environmental information is within the range of the conditions of the environmental information possessed by the standard information.

[0656] (Step S4807) The score acquisition unit 732 determines whether the difference acquired in step S4806 satisfies the addition condition. If it satisfies the addition condition, it proceeds to step S4808, and if it does not satisfy the addition condition, it proceeds to step S4809.

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

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

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

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

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

[0662] (Step S4812) The score acquisition unit 732 determines whether the j-th overall condition exists. If the j-th overall condition exists, it proceeds to step S4813; if not, it returns to the upper-level process. Note that the overall condition is a condition regarding one or more element information in the entire time-series information. The overall condition is, for example, "in the time-series information including the set of one-week behavior information, the behavior information 'drinking party' is included four or more times", "in the time-series information including the set of one-week behavior information, the number of times the sleep time of the behavior information'sleep' is less than 6 hours is included three or more times".

[0663] (Step S4813) The score acquisition unit 732 determines whether the time series information satisfies the j-th overall condition. If it satisfies the j-th overall condition, it proceeds to step S4814; if it does not, it proceeds to step S4815.

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

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

[0666] Next, an example of the factor acquisition process in step S4507 will be described using the flowchart of FIG. 49.

[0667] (Step S4901) The factor acquisition unit 735 assigns 1 to the counter i.

[0668] (Step S4902) The factor acquisition unit 735 determines whether the i-th element information exists in the time series information. If the i-th element information exists, it proceeds to step S4903; if it does not, it returns to the upper-level process.

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

[0670] (Step S4904) The factor acquisition unit 735 determines whether the difference acquired in step S4903 satisfies the adoption condition. If it satisfies the adoption condition, it proceeds to step S4905; if it does not, it proceeds to step S4906.

[0671] (Step S4905) The factor acquisition unit 735 temporarily stores the i-th element information in a buffer (not shown). The i-th element information is information that constitutes the noise factor.

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

[0673] Next, an example of the recommendation acquisition process in step S4508 will be described using the flowchart in FIG. 50.

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

[0675] (Step S5002) The recommendation acquisition unit 736 determines whether the i-th disturbance factor exists in a buffer (not shown) in which the disturbance factors temporarily stored in step S4905 are stored. If the i-th disturbance factor exists, go to step S5004; if not, return to the upper-level process.

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

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

[0678] (Step S5005) The recommendation acquisition unit 736 determines whether the j-th factor condition exists in the recommendation management unit 712. If the j-th factor condition exists, go to step S5006; if not, go to step S5010.

[0679] (Step S5006) The recommendation acquisition unit 736 determines whether the element information acquired in step S5003 satisfies the j-th factor condition. If it satisfies the j-th factor condition, go to step S5007; if not, go to step S5009.

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

[0681] (Step S5008) The recommendation acquisition unit 736 uses the recommendation source information and the element information acquired in Step S5003 to configure recommendation information, and temporarily stores the recommendation information in a buffer (not shown).

[0682] (Step S5009) The recommendation acquisition unit 736 increments the counter j by 1. Return to Step S5005.

[0683] (Step S5010) The recommendation acquisition unit 736 increments the counter i by 1. Return to Step S5002.

[0684] Next, an example of the long-term score acquisition process in Step S4522 will be described using the flowchart of FIG. 51.

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

[0686] (Step S5102) The long-term score acquisition unit 733 detects the jitter scores that match the jitter condition among the jitter scores acquired in Step S5101, and acquires the number of such jitter scores. The jitter score is the number of times the user's behavior has been jittered during a predetermined period.

[0687] (Step S5103) The long-term score acquisition unit 733 uses all the jitter scores and the number of jitter scores that match the jitter condition to acquire a long-term jitter score. Return to the upper-level process. Note that the long-term score acquisition unit 733 acquires a larger long-term jitter score as the jitter score is larger and the number of jitter scores that match the jitter condition is larger.

[0688] Next, an example of the learning process in Step S4528 will be described using the flowchart of FIG. 52.

[0689] (Step S5201) The learning unit 731 substitutes 1 for the counter i.

[0690] (Step S5202) The learning unit 731 determines whether the i-th time series information to be learned exists in the action management unit 313. If the i-th time series information exists, it proceeds to step S5203; if it does not exist, it proceeds to step S5208.

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

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

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

[0694] (Step S5206) The learning unit 731 acquires teacher data having the explanatory variable vector and the noise 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. It returns to step S5202.

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

[0697] Hereinafter, a specific operation example of the information system F in the present embodiment will be described.

[0698] Now, assume that the behavior management unit 313 of the behavior analysis device 7 stores the time zone information management table shown in FIG. 53. The time zone information management table is a table that manages the behavior information of each time zone for one or two or more users. The time zone information management table has one or more records having "ID", "date", "time zone", "behavior information", and "disorder score" here. FIG. 53 is a table for managing the behavior information of one user (user U) for convenience. Also, here, it is assumed that the disorder score is acquired in units of time zones from waking up to before the next waking up (in units of one day).

[0699] Note that each record in FIG. 53 is assumed to be information accumulated in the behavior management unit 313 by the behavior acquisition device 5. Also, each record in FIG. 53 may be information input by the user U.

[0700] Also, assume that the reference management unit 711 of the behavior analysis device 7 stores a reference vector. The reference vector may be a self-reference vector, an other-reference vector, or a recommended behavior vector.

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

[0702] In such a situation, two specific examples will be described below. Specific example 1 is the case of outputting a disorder score and recommendation information. Specific example 2 is the case of outputting an improvement degree and a recovery period.

[0703] (Specific example 1) The reception unit 72 of the behavior analysis device 7 receives the time series information including the combination of the "ID = 538 to 548" date, time zone, and behavior information in FIG. 53, which is information from waking up to sleeping (before the next waking up), in pairs with the user identifier of the user U. Then, the processing unit 73 determines that the score acquisition condition (receiving the time series information of the time zone from waking up to before the next waking up) is satisfied.

[0704] Next, it is assumed that the score acquisition unit 732 has acquired the disturbance score "6" of the user U by the above-described score acquisition process (see FIGS. 46, 47, or 48) using the received time-series information of the user U. Note that the disturbance score can take any rank from "0" to "10" here. Also, here, it is assumed that the disturbance condition is "disturbance score >= 5". Next, the score output unit 741 accumulates the disturbance score "6" as an attribute value of "disturbance score" in FIG. 53 in association with the action information and the like.

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

[0706] Next, the factor acquisition unit 735 acquires the reference information corresponding to "sleep" in the time zone "0:30 - 5:00" and the action information "sleep" and the reference information of the reference management unit 711 (for example, the total of the time difference "1.5 hours" and the start time difference "1.5 hours" for "<time> 6 hours to 10 hours <start time> until 23:00" is "3 hours"). Then, the factor output unit 743 temporarily accumulates the time zone "0:30 - 5:00", the action information "sleep", and the difference "3 hours" in a buffer (not shown). That is, the factor acquisition unit 735 acquires the disturbance factor "sleep".

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

[0708] Next, the processing unit 73 configures output information having the disturbance score "6" and the recommendation information.

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

[0710] Then, the terminal device 8 of the user U outputs, for example, a disturbance score and recommendation information as shown in FIG. 55.

[0711] (Specific Example 2) Next, it is assumed that one week has passed after the disturbance score of "6". And on each day of that one week, it is assumed that the score acquisition unit 732 acquired the disturbance scores of "5", "4", "3", "3", "2", "2", and "0" each day using the accumulated time-series information and the like. And it is assumed that the score output unit 741 accumulated the disturbance scores of "5", "4", "3", "3", "2", "2", and "0" respectively after the disturbance score of "6" in association with the user U.

[0712] Then, the determination unit 734 determines that the improvement degree output condition "the disturbance score became 0 after satisfying the disturbance condition" is satisfied.

[0713] Next, the improvement degree acquisition unit 737 acquires the disturbance score of "6" that matches the disturbance condition and the disturbance score of "0" when the improvement degree output condition is satisfied from a situation that does not match the disturbance condition, and acquires the improvement degree of "6 - 0 = 6".

[0714] Also, the determination unit 734 determines that the disturbance score of "6" acquired when the disturbance condition is satisfied and the most recent disturbance score of "0" satisfy the recovery condition (the disturbance score became 0 after satisfying the disturbance condition).

[0715] Next, the recovery period acquisition unit 738 acquires a recovery period of "7 days", which is the difference between the date "10 / 20" included in the time information associated with the disturbance score of "6" and the date "10 / 27" included in the time information associated with the disturbance score.

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

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

[0718] Then, the terminal device 8 of the user U outputs a disorder score, an improvement degree, and a recovery period as shown in FIG. 56, for example.

[0719] As described above, according to the present embodiment, a disorder score that is the degree of disorder of the user's behavior can be acquired. Note that according to the present embodiment, for example, the disorder score can be acquired using self-reference information. Further, according to the present embodiment, for example, the disorder score can be acquired by prediction processing of machine learning. Further, according to the present embodiment, for example, the disorder score can be acquired using one or more of recommended behavior information and other-reference information. Further, according to the present embodiment, for example, environmental information can also be used to acquire the disorder score.

[0720] Further, according to the present embodiment, the factors causing disorder of the user's behavior can be acquired.

[0721] Further, according to the present embodiment, recommendations for improving the disorder of the user's behavior can be made.

[0722] Furthermore, according to the present embodiment, the improvement degree of the disorder of the user's behavior can be acquired. Note that according to the present embodiment, the improvement degree of the disorder of the user's behavior can be output at an appropriate timing that satisfies the improvement degree output condition.

[0723] Note that, as described above, the behavior analysis device 7 in this embodiment may also be a terminal. The block diagram when the behavior analysis device 7 is a terminal is shown in FIG. 57. This behavior analysis device becomes the behavior analysis device 9. That is, the behavior analysis device 9 has the function of the behavior acquisition device 3.

[0724] Also, the software that realizes the behavior analysis device 7 in this embodiment is the following program. That is, this program is information that can access a computer where reference information serving as a basis for obtaining the degree of disturbance of the user's behavior is stored in a reference management unit, information associated with time information for specifying the time when the user acts, and time series information including two or more pieces of behavior information that are information for specifying the behavior of the user. It is a program for causing a reception unit that receives the time series information, a score acquisition unit that acquires a disturbance score that is the degree of disturbance of 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 disturbance score acquired by the score acquisition unit to function.

[0725] Also, FIG. 58 is a block diagram of a computer system 300 that executes the program described in this specification and realizes the behavior analysis device 7 and the like of the various embodiments described above.

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

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

[0728] A program for causing the computer system 300 to execute functions such as the behavior analysis device 7 of the above-described embodiment may be stored in a CD-ROM 3101, inserted into the CD-ROM drive 3012, and further transferred to the hard disk 3017. Alternatively, the program may be transmitted to the computer 301 via a network (not shown) and stored in the hard disk 3017. The program is loaded into the RAM 3016 when executed. The program may be loaded directly from the CD-ROM 3101 or the network.

[0729] The program does not necessarily include an operating system (OS) or a third-party program that causes the computer 301 to execute functions such as the behavior analysis device 7 of the above-described embodiment. The program only needs to include a portion of instructions that call appropriate functions (modules) in a controlled manner so as to obtain a desired result. How the computer system 300 operates is well known, and a detailed description is omitted.

[0730] In the above program, in steps such as a step of transmitting information and a step of receiving information, processing performed by hardware, for example, processing performed by a modem or an interface card in the transmission step (processing that can only be performed by hardware) is not included.

[0731] Also, the computer that executes the above program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

[0732] Also, in each of the above embodiments, it goes without saying that two or more communication means existing in one device may be physically realized by one medium.

[0733] Also, in each of the above embodiments, each process may be realized by being centrally processed by a single device, or may be realized by being distributedly processed by a plurality of devices.

[0734] It goes without saying that the present invention is not limited to the above embodiments, and various modifications are possible and are also included within the scope of the present invention.

Industrial Applicability

[0735] 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 of a user's behavior, and is useful as a behavior analysis device or the like.

Explanation of Signs

[0736] F Information system 7, 9 Behavior analysis device 71 Storage unit 72 Reception unit 73 Processing unit 74 Output unit 54 Transmission unit 711 Reference management unit 712 Recommendation management unit 731 Learning unit 732 Score acquisition unit 733 Long-term score acquisition unit 734 Judgment unit 735 Factor acquisition unit 736 Recommendation acquisition unit 737 Improvement degree acquisition unit 738 Recovery period acquisition unit 741 Score Output Unit 742 Long-Term Score Output Unit 743 Factor Output Unit 744 Recommendation Output Unit 745 Improvement Degree Output Unit 746 Recovery Period Output Unit

Claims

1. A reception unit that receives time-series information including two or more pieces of behavior information in time series, which is information associated with time information for specifying when a user has taken an action and is information for specifying the action of the user; A reference management unit that stores reference information serving as a basis for obtaining the degree of disruption of the user's actions 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 of the reference management unit to obtain a disruption score that is the degree of disruption of the user's actions; An action analysis device comprising a score output unit that outputs the disruption score acquired by the score acquisition unit.

2. The action analysis device according to claim 1, wherein the reference information includes self-reference information that is information based on the user's past time-series information.

3. The reference information is a learning model obtained by performing learning processing of machine learning using two or more pieces of teacher data having an explanatory variable based on the user's past time-series information and a target variable that is disruption information regarding the degree of disruption of the user's actions, The score acquisition unit, The action analysis device according to claim 2, wherein prediction processing of machine learning is performed using the time-series information received by the reception unit and the learning model to obtain the disruption score.

4. The action analysis device according to any one of claims 1 to 3, wherein the reference information includes one or more types of information among recommended action information for specifying recommended actions or non-recommended actions and other-reference information based on the action information of one or more other persons other than the user.

5. The action analysis device according to any one of claims 1 to 4, wherein the time-series action information of the user and the reference information include environment information for specifying the environment of the action site.

6. A determination unit that determines whether or not the disruption score satisfies a disruption condition; When the determination unit determines that the disruption condition is satisfied, a factor acquisition unit that acquires disruption factors related to the action information that is the cause of the disruption from the action information included in the time-series information received by the reception unit corresponding to the disruption score; The action analysis device according to any one of claims 1 to 5, further comprising a factor output unit that outputs the disruption factors acquired by the factor acquisition unit.

7. With respect to each of two or more factor conditions that are conditions related to the cause of disturbance, referring to a recommendation management unit that stores recommendation source information serving as a source of information for improving the disturbance, and using the recommendation source information associated with the factor condition that matches the disturbance factor acquired by the factor acquisition unit, a recommendation acquisition unit that acquires recommendation information; The behavior analysis apparatus according to claim 6, further comprising a recommendation output unit that outputs the recommendation information acquired by the recommendation acquisition unit.

8. The score acquisition unit acquires a first disturbance score and a second disturbance score that are disturbance scores at two respective times, an improvement degree acquisition unit that acquires an improvement degree, which is information regarding the difference between the first disturbance score and the second disturbance score and is information for specifying the degree of improvement of the disturbance; The behavior analysis apparatus according to any one of claims 1 to 7, further comprising an improvement degree output unit that outputs the improvement degree acquired by the improvement degree acquisition unit.

9. The improvement degree acquisition unit acquires the improvement degree when the first disturbance score and the second disturbance score satisfy an improvement degree output condition. The behavior analysis apparatus according to claim 8.

10. The score acquisition unit acquires a first disturbance score and a second disturbance score that are disturbance scores at two respective times, a determination unit that determines whether or not the first disturbance score and the second disturbance score satisfy a recovery condition, a recovery period acquisition unit that, when the determination unit determines that the recovery condition is satisfied, acquires a recovery period that is the difference between first time information associated with the first disturbance score and second time information associated with the second disturbance score; The behavior analysis apparatus according to any one of claims 1 to 9, further comprising a recovery period output unit that outputs the recovery period acquired by the recovery period acquisition unit.

11. The reception unit receives two or more pieces of time-series information, The score acquisition unit acquires disturbance scores for the two or more pieces of time-series information, a long-term score acquisition unit that acquires a long-term disturbance score for specifying the degree of disturbance of long-term behavior, using the two or more disturbance scores acquired by the score acquisition unit; The behavior analysis apparatus according to any one of claims 1 to 10, further comprising a long-term score output unit that outputs the long-term disturbance score acquired by the long-term score acquisition unit.

12. further comprising a determination unit that determines whether or not each of the two or more disturbance scores satisfies a disturbance condition, The long-term score acquisition unit The action analysis device according to claim 11, wherein the number of times the determination unit determines that the disturbance condition is satisfied is acquired, and the long-term disturbance score is acquired using the number of times.

13. A time acquisition unit that acquires time; A position acquisition unit that acquires position information associated with the time; An action estimation unit that acquires action information for specifying the user's action in a time zone specified by the time included in the two or more pieces of action source information including the position information associated with the time, using the two or more pieces of action source information; and The reception unit The action analysis device according to any one of claims 1 to 12, wherein the reception unit receives the action information in each of the two or more time zones acquired by the action estimation unit.

14. A reception unit that receives radio waves including device identifiers for identifying the communication devices from three or more communication devices; An intensity acquisition unit that acquires time-series radio wave intensities for each of the three or more communication devices; A type determination unit that determines whether each of the three or more communication devices is a fixed terminal in which the communication device is fixed or a mobile terminal that is moving, using the time-series radio wave intensities acquired by the intensity acquisition unit; and The position acquisition unit The action analysis device according to claim 13, wherein the position acquisition unit acquires a terminal position that is the position information of the terminal device, using the radio wave intensities of the three or more communication devices determined by the type determination unit to be fixed terminals.

15. An action analysis method realized by a reference management unit that stores reference information serving as a basis for acquiring the degree of disturbance of a user's action, a reception unit, a score acquisition unit, and a score output unit, A reception step in which the reception unit receives time-series information including two or more pieces of action information in time series, which is information associated with time information for specifying the time when the user performed an action and is information for specifying the user's action; A score acquisition step in which the score acquisition unit acquires a disturbance score that is the degree of disturbance of the user's action, using the time-series information received by the reception unit and the reference information of the reference management unit; An action analysis method comprising: a score output step in which the score output unit outputs the disturbance score acquired by the score acquisition unit.

16. A computer accessible to a reference management unit that stores reference information serving as a basis for acquiring the degree of disturbance of a user's action A reception unit that receives time-series information including two or more pieces of action information in a time series, which is information associated with time information for specifying when a user has taken an action and is information for specifying the action of the user; A score acquisition unit that acquires a disturbance score, which is the degree of disturbance of the user's action, using the time-series information received by the reception unit and the reference information of the reference management unit; A program for causing the score output unit to function as a score output unit that outputs the disturbance score acquired by the score acquisition unit.

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