Information processing method, information processing device, and program

Abstract

A computer acquires information indicating the machine operation and action of a target user and the presence or absence of other users in an environment in which the target user is present, and determines the presence or absence of other users on the basis of the information. First action information indicating a machine operation and / or an action of a target user in a first environment in which no other user is present, and second action information indicating a machine operation and / or an action of the target user in a second environment in which the other user is present are extracted. The controller estimates, on the basis of the first action information, a first characteristic of a target user present in the first environment, estimates, on the basis of the second action information, a second characteristic of a target user present in the second environment, and outputs information indicating the first characteristic and / or the second characteristic.

Description

Technical Field

[0001] The present disclosure relates to a technology for estimating characteristics of a user. Background Art

[0002] In the past, there is a known technique for estimating the intention and hobby of a user based on the user's search history or browsing history on a network space such as a WEB site. In addition, Patent Document 1 discloses estimating the user's personality tendency for actions associated with material processing based on the use history of a machine for material processing.

[0003] However, in the above-mentioned conventional techniques, it is not considered to individually estimate the characteristics of a user based on whether or not there are other users in the same environment as the user.

[0004] Prior Art Literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent No. 6294825 Summary of the invention

[0007] The present disclosure has been made to solve the above-mentioned problems, and an object of the present disclosure is to provide an information processing method, an information processing device, and a program capable of individually estimating characteristics of a user based on whether or not other users exist in the same environment as the user.

[0008] An information processing method involved in one aspect of the present disclosure is an information processing method for estimating characteristics of a user in a computer. In the information processing method, first information indicating machine operations and actions of an estimated target user is obtained, and second information indicating whether other users different from the target user exist in an environment where the target user exists is obtained. Based on the first information and the second information, first action information indicating at least one of the machine operations and actions of the target user in a first environment where the other users do not exist and second action information indicating at least one of the machine operations and actions of the target user in a second environment where the other users exist are extracted. Based on the first action information, a first characteristic is estimated as a characteristic of the target user existing in the first environment, and based on the second action information, a second characteristic is estimated as a characteristic of the target user existing in the second environment, and at least one of first characteristic information indicating the first characteristic and second characteristic information indicating the second characteristic is output. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a diagram showing an example of the overall configuration of an information processing system according to an embodiment of the present disclosure.

[0010] Figure 2 This is a block diagram showing an example of the structure of an information processing device.

[0011] Figure 3 : is a flowchart showing an example of the characteristic output process.

[0012] Figure 4 It is a diagram showing an example of operation information.

[0013] Figure 5 : is a flowchart showing an example of the characteristic estimation process.

[0014] Figure 6 It is a diagram showing an example of the first characteristic information.

[0015] Figure 7 It is a diagram showing an example of the second characteristic information.

[0016] Figure 8 This is a diagram showing an example of first rule information that defines a relationship between one or more candidate characteristics and one or more feature groups representing features of an operation of a device or equipment.

[0017] Fig. 9 This is a diagram showing an example of first rule information that defines a relationship between one or more candidate characteristics and one or more feature groups representing characteristics of an action.

[0018] Fig.10 This is a diagram showing an example of the relationship between the characteristics of the target user before updating and the number of executions of characteristic actions corresponding to each element characteristic.

[0019] Fig.11 1 is a diagram showing an example of the relationship between the updated characteristics of the target user and the number of executions of the characteristic actions corresponding to each element characteristic.

[0020] Fig.12 This is a diagram showing an example of changes in characteristic information.

[0021] Fig.13 This is a flowchart showing an example of a service provision process.

[0022] Fig.14 This is a diagram showing an example of the second rule information.

[0023] Fig.15 This is a diagram showing an example of a part of the third rule information.

[0024] Fig.16 This is a diagram showing an example of the remaining part of the third rule information. DETAILED DESCRIPTION

[0025] (The process of realizing this disclosure)

[0026] Conventionally, the following technology is known: based on the user's search history or browsing history on a network space such as a WEB site, the user's intention and preference are estimated, and a service suitable for the estimated intention and preference is provided to the user. However, in this technology, the user's intention and preference are estimated based on information reflecting the user's intention and preference actively input by the user through input of search keywords and clicks on product images. Therefore, in order to adopt this technology, an environment in which the user actively inputs information is required.

[0027] In contrast, Patent Document 1 discloses a technique for estimating a user's personality tendency based on the user's device usage history without the user actively inputting information. However, Patent Document 1 does not consider estimating a user's characteristics individually based on whether there are other users in the same environment as the user.

[0028] Therefore, even if the technology of Patent Document 1 is applied, it is not possible to provide appropriate services to users in consideration of the difference between the characteristics when a user lives alone and the characteristics when the user lives with other people.

[0029] Therefore, the present inventors have conducted intensive studies on a technique for individually estimating characteristics of a user based on whether or not other users exist in the same environment as the user, and have achieved various aspects corresponding to the present disclosure described below.

[0030] (1) An information processing method according to one aspect of the present disclosure is an information processing method for estimating characteristics of a user in a computer. In the information processing method, first information indicating machine operations and actions of an estimated target user is obtained, and second information indicating whether other users different from the target user exist in an environment where the target user exists is obtained. Based on the first information and the second information, first action information indicating at least one of the machine operations and actions of the target user in a first environment where the other user does not exist and second action information indicating at least one of the machine operations and actions of the target user in a second environment where the other user exists are extracted. Based on the first action information, a first characteristic is estimated as a characteristic of the target user in the first environment, and based on the second action information, a second characteristic is estimated as a characteristic of the target user in the second environment, and at least one of first characteristic information indicating the first characteristic and second characteristic information indicating the second characteristic is output.

[0031] According to this configuration, the first characteristic is estimated as the characteristic of the target user existing in the first environment where no other user exists, based on the first action information. In addition, the second characteristic is estimated as the characteristic of the target user existing in the second environment where other users exist, based on the second action information. Therefore, this configuration can estimate the characteristic of the target user individually according to whether or not other users exist in the same environment as the target user.

[0032] (2) In the information processing method described in (1) above, it may also be that, in the inference of the first characteristic and the second characteristic, first rule information that specifies the relationship between one or more candidate characteristics and one or more feature groups representing the characteristics of the machine operation or action of the object user is obtained, and when the first action information contains a machine operation or action that exhibits one or more first feature groups included in the one or more feature groups, one or more first candidate characteristics corresponding to the one or more first feature groups are determined from the one or more candidate characteristics, and the one or more determined first candidate characteristics are inferred as the first characteristic; when the second action information contains a machine operation or action that exhibits one or more second feature groups included in the one or more feature groups, one or more second candidate characteristics corresponding to the one or more second feature groups are determined from the one or more candidate characteristics, and the one or more determined second candidate characteristics are inferred as the second characteristic.

[0033] In this structure, one or more first candidate characteristics corresponding to one or more first feature groups of machine operations or behavioral performances included in the first action information are estimated as the first characteristics using the first rule information. In addition, one or more second candidate characteristics corresponding to one or more second feature groups of machine operations or behavioral performances included in the second action information are estimated as the second characteristics using the first rule information. Therefore, this structure can estimate one or more characteristics of the target user in each of the case where other users exist in the same environment as the target user and the case where other users do not exist.

[0034] (3) In the information processing method described in (2) above, it may be that the first characteristic includes one or more first element characteristics, and the second characteristic includes one or more second element characteristics. Furthermore, for each of the one or more first element characteristics, based on the first action information, the number of executions of the first characteristic action as a machine operation or action that expresses a feature group corresponding to each first element characteristic is calculated, and the calculated number of executions of the first characteristic action is set as the strength of each first element characteristic. Furthermore, for each of the one or more second element characteristics, based on the second action information, the number of executions of the second characteristic action as a machine operation or action that expresses a feature group corresponding to each second element characteristic is calculated, and the calculated number of executions of the second characteristic action is set as the strength of each second element characteristic. The first characteristic information includes the strength of each first element characteristic, and the second characteristic information includes the strength of each second element characteristic.

[0035] In this structure, the strength of each first element characteristic included in the first characteristic is set to the number of executions of the first characteristic action calculated based on the first action information, and is included in the first characteristic information. In addition, the strength of each second element characteristic included in the second characteristic is set to the number of executions of the second characteristic action calculated based on the second action information, and is included in the second characteristic information.

[0036] Therefore, based on the first characteristic information of the present structure, not only one or more characteristics of the target user when there are no other users in the same environment as the target user, but also the strength of each of the one or more characteristics can be grasped. In addition, based on the second characteristic information of the present structure, not only one or more characteristics of the target user when there are other users in the same environment as the target user, but also the strength of each of the one or more characteristics can be grasped.

[0037] (4) In the information processing method described in (3) above, it may also be that, in the setting of the strength of each first element characteristic and each second element characteristic, a third action information representing the machine operation and action of one or more other users different from the target user is further obtained, and based on the third action information, an average value of the number of executions of the first characteristic action at each given time based on each of the one or more other users, that is, a first average value, is calculated, based on the first action information, a first execution count as the number of executions of the first characteristic action at each given time is calculated, and a result obtained by dividing the first average value by the first execution count is set as the strength of each first element characteristic, and based on the third action information, an average value of the number of executions of the second characteristic action at each given time based on each of the one or more other users, that is, a second average value, is calculated, based on the second action information, a second execution count as the number of executions of the second characteristic action at each given time is calculated, and a result obtained by dividing the second average value by the second execution count is set as the strength of each second element characteristic.

[0038] In the present structure, the result obtained by dividing the average number of executions of the first characteristic action at each given time based on one or more other users different from the target user, that is, the first average value, by the number of executions of the first characteristic action at each given time based on the target user, that is, the first number of executions, is set as the intensity of each first element characteristic.

[0039] Therefore, the present structure can appropriately set the strength of each first element characteristic based on the first average value. Similarly, the present structure can appropriately set the strength of each second element characteristic based on the second average value, which is the average value of the number of executions of the second characteristic action of each of more than one other users at each given time.

[0040] (5) In the information processing method described in (3) above, it may also be that, in the estimation of the first characteristic and the second characteristic, when at least one of the first action information and the second action information includes a machine operation or action that exhibits a feature group corresponding to a common candidate characteristic predetermined from among the one or more candidate characteristics, that is, a common feature action, the common candidate characteristic is estimated to be a common element characteristic included in both the first characteristic and the second characteristic, and in the setting of the strength of each first element characteristic and each second element characteristic, based on the first action information, a first execution number is calculated as the execution number of the common feature action, and based on the second action information, a second execution number is calculated as the execution number of the common feature action, and the sum of the first execution number and the second execution number is set as the strength of the common element characteristic.

[0041] According to this structure, even if the common characteristic action corresponding to the common candidate characteristic is not included in either the first action information or the second action information, the common candidate characteristic can be inferred as the common element characteristic included in both the first characteristic and the second characteristic if at least one of the first action information and the second action information includes the common characteristic action.

[0042] In addition, not only can the number of executions of the common characteristic action calculated based on either the first action information or the second action information be set as the intensity of the common element characteristic, but the sum of the number of executions of the common characteristic action calculated based on each of the first action information and the second action information can also be set as the intensity of the common element characteristic.

[0043] (6) In the information processing method described in (3) above, it may be that, in the estimation of the first characteristic and the second characteristic, when at least one of the first action information and the second action information includes a machine operation or action that exhibits a feature group corresponding to a common candidate characteristic predetermined from among the one or more candidate characteristics, that is, a common feature action, the common candidate characteristic is estimated to be a common element characteristic included in both the first characteristic and the second characteristic, and in the setting of the strength of each first element characteristic and each second element characteristic, the first time that the target user stays in the first environment is calculated based on the first action information, and the time that the target user stays in the first environment is calculated based on the second action information. The second time remaining in the second environment, based on the first action information, calculates the first execution number as the execution number of the common feature action, based on the second action information, calculates the second execution number as the execution number of the common feature action, and sets the result obtained by dividing the sum of the first time and the second time by the product of the sum of the first execution number and the second execution number and the first time as the intensity of the common element characteristic contained in the first characteristic, and sets the result obtained by dividing the sum of the first time and the second time by the product of the sum of the first execution number and the second execution number and the second time as the intensity of the common element characteristic contained in the second characteristic.

[0044] According to this structure, even if neither the first action information nor the second action information includes a common characteristic action corresponding to the common candidate characteristic, when at least one of the first action information and the second action information includes the common characteristic action, the common candidate characteristic can be inferred as a common element characteristic included in both the first characteristic and the second characteristic.

[0045] In addition, the sum of the number of executions of the common characteristic actions calculated based on each of the first action information and the second action information can be proportionally allocated according to the first time that the target user stays in the first environment and the second time that the target user stays in the second environment, and the intensity of the common element characteristics contained in each of the first characteristic and the second characteristic can be appropriately set.

[0046] (7) In the information processing method described in (5) above, further, when the first characteristic and the second characteristic have the same element characteristic with similar strength, the same element characteristic may be estimated as the common element characteristic.

[0047] According to this structure, when there is a same element characteristic with similar strength between the first characteristic and the second characteristic, the same element characteristic is estimated as a common element characteristic. Therefore, the sum of the number of executions of machine operations or actions showing a feature group corresponding to the same element characteristic calculated based on each of the first action information and the second action information can be set as the strength of the same element characteristic.

[0048] (8) In the information processing method described in (5) above, further, when the first information contains a machine operation or action that exhibits any one of the one or more feature groups, and the machine operation or action has a similar number of executions per unit time in each of the first environment and the second environment, i.e., similar feature actions, the candidate characteristic corresponding to the feature group exhibited by the similar feature action is inferred as the common element characteristic.

[0049] According to this structure, when the similar characteristic action is included in the first information, the candidate characteristic corresponding to the characteristic group represented by the similar characteristic action is inferred as the common element characteristic. Therefore, the sum of the number of executions of the similar characteristic action calculated based on each of the first action information and the second action information can be set as the strength of the common element characteristic.

[0050] (9) In the information processing method described in any one of (3) to (8) above, it may also be that, further, for each of the one or more first element characteristics, when the machine operation or action showing the feature group corresponding to each first element characteristic has not been performed for more than a first given time, the intensity of each first element characteristic is reduced at a first reduction rate, and further, for each of the one or more second element characteristics, when the machine operation or action showing the feature group corresponding to each second element characteristic has not been performed for more than a second given time, the intensity of each second element characteristic is reduced at a second reduction rate.

[0051] According to this structure, the intensity of the first element characteristic that a machine operation or action of a corresponding feature group has not been performed for more than a first given time can be reduced at a first reduction rate among one or more first element characteristics included in the first characteristic. Similarly, according to this structure, the intensity of the second element characteristic that a machine operation or action of a corresponding feature group has not been performed for more than a second given time can be reduced at a second reduction rate among one or more second element characteristics included in the second characteristic.

[0052] (10) In the information processing method described in any one of (3) to (9) above, it may also be that, further, for each of the one or more first element characteristics, the ratio of the intensity of each first element characteristic to the sum of the intensity of the one or more first element characteristics is calculated, and the calculated ratio is set as the intensity of each first element characteristic, and further, for each of the one or more second element characteristics, the ratio of the intensity of each second element characteristic to the sum of the intensity of the one or more second element characteristics is calculated, and the calculated ratio is set as the intensity of each second element characteristic.

[0053] According to this structure, the ratio of the intensity of each first element characteristic to the sum of the intensities of one or more first element characteristics is set to the intensity of each first element characteristic included in the first characteristic. Therefore, the intensities of one or more first element characteristics included in the first characteristic can be normalized. Similarly, according to this structure, since the ratio of the intensity of each second element characteristic to the sum of the intensities of one or more second element characteristics is set to the intensity of each second element characteristic included in the second characteristic, the intensities of one or more second element characteristics included in the second characteristic can be normalized.

[0054] (11) In the information processing method described in (2) above, it may also be that, further, for each of the one or more first element characteristics, when the machine operation or action showing the feature group corresponding to each first element characteristic has not been performed for more than a first given time, each first element characteristic is excluded from the first characteristic; further, for each of the one or more second element characteristics, when the machine operation or action showing the feature group corresponding to each second element characteristic has not been performed for more than a second given time, each second element characteristic is excluded from the second characteristic.

[0055] According to this structure, among one or more first element characteristics included in the first characteristic, the first element characteristic showing that the machine operation or action of the corresponding feature group has not been performed for more than a first given time can be excluded from the first characteristic. Similarly, according to this structure, among one or more second element characteristics included in the second characteristic, the second element characteristic showing that the machine operation or action of the corresponding feature group has not been performed for more than a second given time can be excluded from the second characteristic.

[0056] (12) In the information processing method described in (1) above, it may also be that, further, information representing one or more attributes of the other users is obtained, and further, for each of the one or more attributes, fourth action information representing the machine operation and behavior of the target user in the third environment of the other users with each attribute is extracted from the second action information, and based on the fourth action information, a third characteristic as a characteristic of the target user existing in the third environment is inferred, and third characteristic information related to the third characteristic is output.

[0057] According to this structure, for each of more than one attribute, a third characteristic is estimated as a characteristic of the target user existing in a third environment where other users with the respective attributes exist. Therefore, this structure can estimate the characteristic of the target user individually based on the attributes of other users existing in the same environment as the target user.

[0058] (13) In the information processing method described in (1) above, it may be that, in acquiring the first information, information indicating the machine operation and actions of the target user within a first given period is acquired as the first information, and in acquiring the second information, information indicating the history of the presence or absence of the other users in the environment where the target user exists within the first given period is acquired as the second information.

[0059] According to this structure, information indicating the machine operation and action of the target user in the first given period is acquired as the first information, and information indicating the history of the presence or absence of other users in the environment where the target user exists in the first given period is acquired as the second information. Therefore, the first characteristic and the second characteristic of the target user in the first given period can be appropriately estimated based on the first action information and the second action information extracted from the first information based on the second information.

[0060] (14) In the information processing method described in (1) above, it may also be that, further, third information representing the action of the target user in the environment in which the target user currently exists is obtained, and further, based on the third information, it is determined whether the fourth environment in which the target user currently exists is the first environment or the second environment. If the fourth environment is the first environment, the first characteristic information is obtained, and based on the first characteristic information, a first service to be provided to the target user is determined, and the first service is executed. If the fourth environment is the second environment, the second characteristic information is obtained, and based on the second characteristic information, a second service to be provided to the target user is determined, and the second service is executed.

[0061] According to this structure, it is determined whether the fourth environment in which the target user currently exists is the first environment or the second environment. If the fourth environment is the first environment, the first characteristic information is acquired, and if the fourth environment is the second environment, the second characteristic information is acquired. Furthermore, if the fourth environment is the first environment, the first service determined based on the first characteristic information is executed, and if the fourth environment is the second environment, the second service determined based on the second characteristic information is executed. Therefore, this structure can execute a service suitable for the characteristics of the target user in the environment in which the target user currently exists.

[0062] (15) In the information processing method described in (14) above, it may also be that the first characteristic includes one or more first element characteristics, and the second characteristic includes one or more second element characteristics. In determining the first service and the second service, second rule information that specifies the relationship between one or more element characteristic groups representing one or more element characteristics and one or more service offerings is obtained. When the first characteristic includes one or more first element characteristic groups included in the one or more element characteristic groups, one or more first service offerings corresponding to the one or more first element characteristic groups are determined, and the determined one or more first service offerings are determined as the first service. When the second characteristic includes one or more second element characteristic groups included in the one or more element characteristic groups, one or more second service offerings corresponding to the one or more second element characteristic groups are determined, and the determined one or more second service offerings are determined as the second service.

[0063] In this configuration, one or more first service offers corresponding to one or more first element characteristic groups included in the first characteristic are determined to be the first service to be executed when the fourth environment is the first environment, using the second rule information. In addition, one or more second service offers corresponding to one or more second element characteristic groups included in the second characteristic are determined to be the second service to be executed when the fourth environment is the second environment, using the second rule information. Therefore, this configuration can execute one or more service offers suitable for the characteristics of the target user in the environment where the target user currently exists.

[0064] (16) In the information processing method described in (3) above, it may also be that, further, the third information representing the action of the target user in the environment in which the target user currently exists is obtained, and further, based on the third information, it is determined whether the fourth environment in which the target user currently exists is the first environment or the second environment. If the fourth environment is the first environment, the first characteristic information is obtained, and based on the first characteristic information, the first service provided to the target user is determined and the first service is executed. If the fourth environment is the second environment, the second characteristic information is obtained, and based on the second characteristic information, the second service provided to the target user is determined and the second service is executed.

[0065] According to this structure, it is determined whether the fourth environment in which the target user currently exists is the first environment or the second environment. If the fourth environment is the first environment, the first characteristic information is acquired, and if the fourth environment is the second environment, the second characteristic information is acquired. Furthermore, if the fourth environment is the first environment, the first service determined based on the first characteristic information is executed, and if the fourth environment is the second environment, the second service determined based on the second characteristic information is executed. Therefore, this structure can execute a service suitable for the characteristics of the target user in the environment in which the target user currently exists.

[0066] (17) In the information processing method described in (16) above, it may also be that, in determining the first service and the second service, second rule information that specifies the relationship between one or more element characteristic groups representing one or more element characteristics and one or more service offerings is obtained; when the first characteristic includes one or more first element characteristic groups included in the one or more element characteristic groups, one or more first service offerings corresponding to the one or more first element characteristic groups are determined; when the second characteristic includes one or more second element characteristic groups included in the one or more element characteristic groups, one or more second service offerings corresponding to the one or more second element characteristic groups are determined; third rule information that establishes a correspondence between the one or more service offerings and the coefficients assigned to each service offering and the service field to which each service offering belongs is obtained; and for each of the one or more first service offerings, a first product is calculated, wherein the first product is a coefficient of one or more element characteristics indicated by the element characteristic groups included in the first characteristic and corresponding to each first service offering. The product of the sum of the respective strengths and the coefficient assigned to each first service offering is calculated, for each of the one or more first service areas to which the one or more first service offerings belong among the one or more service areas, when the sum of the first products calculated for each of at least one first service offering belonging to each of the first service areas is greater than a first given value, the at least one first service offering is determined to be the first service. For each of the one or more second service offerings, a second product is calculated, the second product being the product of the sum of the respective strengths of one or more element characteristics indicated by the element characteristic group corresponding to each second service offering included in the second characteristic and the coefficient assigned to each second service offering. For each of the one or more second service areas to which the one or more second service offerings belong among the one or more service areas, when the sum of the second products calculated for each of at least one second service offering belonging to each of the second service areas is greater than the first given value, the at least one second service offering is determined to be the second service.

[0067] In the present structure, the second rule information and the third rule information are used, and when the sum of the first products calculated for each of at least one first provided service belonging to each first service area is greater than the first given value, the at least one first provided service is determined as the first service to be executed when the fourth environment is the first environment.

[0068] Therefore, in the present configuration, when the target user exists in the first environment, it is possible to determine, for each first service area, whether to determine at least one first provided service belonging to each first service area as the first service.

[0069] Similarly, when the target user exists in the second environment, this configuration can determine, for each second service area, whether to determine at least one second offering service belonging to each second service area as the second service.

[0070] (18) In the information processing method described in any one of (14) to (17) above, it may be that, when there are multiple users in the same environment, the second service is determined by taking each of the multiple users as the target user, and when the determined multiple second services include multiple services for automatically controlling machines in the environment where the user exists based on the characteristics of the user, in the execution of the multiple services, the parameters used in the automatic control of the machines based on the multiple services are averaged and executed.

[0071] According to this configuration, when a plurality of second services are determined when a plurality of users exist in the same environment, when a plurality of services for automatically controlling a machine in the environment in which the user exists are included according to the characteristics of the user, the parameters used for automatically controlling the machine based on the plurality of services are averaged and executed during the execution of the plurality of services. Therefore, the configuration can avoid a situation in which the parameters used for automatically controlling the machine based on each service conflict due to the execution of the plurality of services.

[0072] (19) In the information processing method described in any one of (14) to (17) above, it may be that, when there are multiple users in the same environment, the second service is determined with each of the multiple users as the target user, and when the determined multiple second services include multiple services that automatically control machines in the environment where the user exists according to the characteristics of the user, a priority pre-assigned to each of the multiple users is obtained, and in the execution of the multiple services, the service determined as the second service to be provided to the user with the highest priority among the multiple users is executed.

[0073] According to this structure, when there are multiple users in the same environment, among the multiple second services determined, when there are multiple services that automatically control the machine existing in the user's environment according to the characteristics of the user, the service determined as the second service provided to the user with the highest priority among the multiple users among the multiple services is executed. Therefore, this structure can avoid the situation where the automatic control of the machine based on each service conflicts due to the execution of the multiple services.

[0074] (20) In the information processing method described in any one of (14) to (17) above, it may be that, when there are multiple users in the same environment, the second service is determined by taking each of the multiple users as the target user, and when the determined multiple second services include multiple services that automatically control machines existing in the environment where the user exists according to the characteristics of the user, in the execution of the multiple services, the service with the largest number among the multiple services is executed.

[0075] According to this structure, when there are multiple users in the same environment, among the multiple second services determined, when there are multiple services that automatically control the machines in the environment where the users exist according to the characteristics of the users, the largest number of services among the multiple services are executed. Therefore, this structure can avoid the situation where the automatic control of the machines based on each service conflicts due to the execution of the multiple services.

[0076] (21) In the information processing method described in (15) or (17) above, it may be that, when there are multiple users in the same environment, the second service is determined by taking each of the multiple users as the target user, and when the determined multiple second services include multiple services for automatically controlling a machine existing in the environment where the user exists according to the characteristics of the user, a predetermined priority order is obtained for each of the one or more element characteristic groups, and in the execution of the multiple services, the service corresponding to the element characteristic group with the lowest priority order among the multiple services is executed.

[0077] According to this structure, when a plurality of second services determined when a plurality of users exist in the same environment include a plurality of services for automatically controlling a machine in the environment in which the user exists according to the characteristics of the user, the service corresponding to the element characteristic group with the lowest priority among the plurality of services is executed. Therefore, the present structure can avoid a situation in which a conflict occurs in the automatic control of the machine based on each service due to the execution of the plurality of services.

[0078] (22) Another aspect of the present disclosure relates to an information processing device for estimating characteristics of a user, the information processing device comprising: a first acquisition unit for acquiring first information indicating machine operations and actions of an estimated target user; a second acquisition unit for acquiring second information indicating whether there is a user other than the target user in an environment where the target user exists; an extraction unit for extracting, based on the first information and the second information, first action information indicating at least one of the machine operations and actions of the target user in a first environment where the other user does not exist, and second action information indicating at least one of the machine operations and actions of the target user in a second environment where the other user exists; an estimation unit for estimating a first characteristic as a characteristic of the target user in the first environment based on the first action information, and estimating a second characteristic as a characteristic of the target user in the second environment based on the second action information; and an output unit for outputting at least one of the first characteristic information indicating the first characteristic and the second characteristic information indicating the second characteristic.

[0079] According to this structure, the same effect as the above-mentioned information processing method can be obtained.

[0080] (23) Another aspect of the present disclosure relates to a program that causes a computer to function so as to estimate characteristics of a user, wherein the program causes the computer to function as: a first acquisition unit that acquires first information indicating machine operations and actions of an estimated target user; a second acquisition unit that acquires second information indicating whether there is a user other than the target user in an environment where the target user exists; an extraction unit that extracts, based on the first information and the second information, first action information indicating at least one of the machine operations and actions of the target user in a first environment where the other user does not exist, and second action information indicating at least one of the machine operations and actions of the target user in a second environment where the other user exists; an estimation unit that estimates a first characteristic as a characteristic of the target user existing in the first environment based on the first action information, and estimates a second characteristic as a characteristic of the target user existing in the second environment based on the second action information; and an output unit that outputs at least one of first characteristic information indicating the first characteristic and second characteristic information indicating the second characteristic.

[0081] According to this structure, the same effect as the above-mentioned information processing method can be obtained.

[0082] The present disclosure can also be realized as a system operated by such a program. In addition, such a computer program can of course be distributed via a computer-readable non-transitory recording medium such as a CD-ROM or a communication network such as the Internet.

[0083] In addition, the embodiments described below all represent a specific example of the present disclosure. The numerical values, shapes, structural elements, steps, the order of steps, etc. shown in the following embodiments are examples and are not intended to limit the present disclosure. In addition, among the structural elements in the following embodiments, the structural elements that are not recorded in the independent claims representing the highest concept are described as arbitrary structural elements. In addition, in all embodiments, the various contents can also be combined.

[0084] (First embodiment)

[0085] Hereinafter, a first embodiment of the present disclosure will be described with reference to the drawings. Figure 1 1 is a diagram showing an example of the overall configuration of an information processing system 100 according to an embodiment of the present disclosure. The information processing system 100 includes a plurality of devices 3 , equipment 5 (devices), sensors 7 , output devices 6 , and an information processing device 1 installed in a facility 4 (environment).

[0086] The plurality of machines 3, devices 5, and sensors 7 are connected to the information processing device 1 via a network 9 so as to be able to communicate with each other. The network 9 is, for example, a public communication line such as the Internet. In addition, the network 9 may also be a local area network. The plurality of machines 3, devices 5, and sensors 7 may also be connected to each other so as to be able to communicate via a local network within the facility 4.

[0087] The facility 4 is divided into a plurality of spaces 40 (environments), and a plurality of machines 3 , equipment 5 , and sensors 7 are arranged in any of the plurality of spaces 40 . Figure 1 The example shown is that the facility 4 is divided into a first space 40a and a second space 40b, the first space 40a includes a machine 3a, a device 5a, and a sensor 7a, and the second space 40b includes a machine 3b, a device 5b, and a sensor 7b.

[0088] The facility 4 is, for example, a residence. The residence may be a collective residence or an independent residence. When the facility 4 is a residence, the space 40 may be, for example, a living room, a dining room, a kitchen, an LDK (living room / dining room / kitchen), a Western-style room, a Japanese-style room, a corridor, a toilet, a porch, and a bathroom. In addition, the so-called LDK refers to a space that combines a living room, a dining room, and a kitchen. In addition, the space 40 may be, for example, the first floor and the second floor. In addition, the entire residence may be regarded as a space 40.

[0089] Alternatively, the facility 4 may be an office. When the facility 4 is an office, the space 40 may be, for example, an office, a conference room, a hot water supply room, a reception room, a hall, a corridor, and a toilet. In addition, the space 40 may be, for example, the first floor and the second floor. In addition, the entire office may be regarded as one space 40.

[0090] Machine 3 is an electronic device such as a rice cooker, a washing machine, a refrigerator, a microwave oven, and a cleaning robot that can be freely arranged in facility 4. Machine 3 is operated by a switch and a remote controller inherent to machine 3. Device 5 is an electronic device such as an electronic lock, an air conditioner, and a solar power generation device that is installed at a given location in facility 4. Device 5 is operated by a switch and a remote controller inherent to device 5.

[0091] In addition, information processing devices such as computers, smartphones, and tablet terminals are not included in the machine 3 and the device 5. Operations of the machine 3 and the device 5 do not include operations in which the user actively inputs information reflecting his or her intentions and preferences, such as inputting search keywords and clicking on product images.

[0092] When the machine 3 and the device 5 are operated by the user, they transmit information related to the operation (hereinafter referred to as operation information) to the information processing apparatus 1 via the network 9 .

[0093] The operation information includes the date and time when the machine 3 and the device 5 are operated (hereinafter, the operation date and time), the identification information of the user who operates the machine 3 and the device 5 (hereinafter, the user ID), the identification information of the machine 3 and the device 5 (hereinafter, the machine ID), and the information indicating the content of the operation of the machine 3 and the device 5 (hereinafter, the operation content information). In addition, the operation information sent by the machine 3 and the device 5 does not need to include the user ID of the user who operates the machine 3 and the device 5.

[0094] The operation content information includes information indicating the status of the machine 3 and the device 5 during operation (hereinafter referred to as status information), information set through the operation of the machine 3 and the device 5 (hereinafter referred to as setting information), information indicating the function performed through the operation of the machine 3 and the device 5 (hereinafter referred to as function information), etc.

[0095] The sensor 7 periodically detects information related to the space 40 in which the sensor 7 is installed. The sensor 7 transmits information (hereinafter, sensor information) including the detected information (hereinafter, detection information), the date and time when the detection information is detected (hereinafter, detection date and time), and identification information (hereinafter, sensor ID) of the sensor 7 to the information processing device 1 via the network 9. The sensor 7 includes a camera, a microphone, an electric wave sensor, a human body sensing sensor, and the like.

[0096] The camera captures an image of the space 40 and transmits sensor information including image data representing the captured image as detection information. The microphone picks up sounds generated in the space 40 and transmits sensor information including voice data representing the picked up sounds as detection information. The radio wave sensor detects the position and shape of a person existing in the space 40 by the intensity of radio waves, and transmits sensor information including information representing the position and shape of the detected person as detection information. The human body sensing sensor is, for example, an infrared sensor and a beacon sensor, which detects whether a person exists in the space 40. When the human body sensing sensor detects the presence of a person in the space 40, it transmits sensor information including information representing the position of the person as detection information.

[0097] The output device 6 is connected to the information processing device 1 via the network 9 so as to be able to communicate with each other. The output device 6 outputs information instructed from the information processing device 1 via the network 9. The output device 6 includes a display, a speaker, a controller, and the like.

[0098] The display is, for example, a display device provided in a television or a personal computer disposed in the facility 4. The display is not limited thereto, and may be a display provided in a portable terminal such as a smartphone or a tablet terminal that can be moved outside the facility 4, or may be a display provided in the machine 3 or the device 5. The display displays a still image or a moving image instructed by the information processing device 1.

[0099] The speaker is, for example, a smart speaker disposed in the facility 4. The speaker is not limited thereto, and may be a speaker included in a portable terminal such as a smartphone or a tablet terminal that can be moved outside the facility 4, or a speaker included in the machine 3 and the device 5. The speaker outputs a voice indicating information instructed by the information processing device 1.

[0100] The controller is, for example, a home controller or an edge server configured in the facility 4. The controller is not limited thereto, and may also be a portable terminal such as a smart phone or a tablet terminal that can be moved outside the facility 4. Furthermore, the controller can be wirelessly connected to the machine 3, the device 5, and the sensor 7 via the network 9 or not via the network 9. The controller outputs information related to the control of the machine 3, the device 5, and the sensor 7 (hereinafter, control information) input by the user's operation or instructed from the information processing device 1 to the machine 3, the device 5, and the sensor 7. The machine 3, the device 5, and the sensor 7 perform various functions according to the control information. Thus, the controller remotely controls the machine 3, the device 5, and the sensor 7.

[0101] The information processing device 1 is constituted by a cloud server, a personal computer, etc. Alternatively, the information processing device 1 may be an edge server disposed in the facility 4. The information processing device 1 is connected to an external service server 8 via a network 9 so as to be able to communicate with each other.

[0102] The service server 8 is composed of a cloud server and a personal computer, etc. The service server 8 executes the service requested from the information processing device 1 via the network 9. The services executed by the service server 8 include a service of sending information instructed from the information processing device 1 to a database not shown and / or an external service server, a service of obtaining information instructed from the information processing device 1 from a database not shown and / or an external service server and replying, etc.

[0103] Hereinafter, the configuration of the information processing device 1 will be described in detail. Figure 2 1 is a block diagram showing an example of the configuration of the information processing device 1. The information processing device 1 includes a communication circuit 11, a processor 12, and a memory 13.

[0104] The communication circuit 11 is a communication interface circuit corresponding to a communication method using the network 9 such as Ethernet (registered trademark). The communication circuit 11 connects the information processing device 1 to the network 9. The communication circuit 11 outputs various information received via the network 9 to the processor 12. In addition, the communication circuit 11 transmits various information to an external device via the network 9 under the control of the processor 12.

[0105] The processor 12 (computer) is constituted by, for example, a CPU. The processor 12 stores the operation information received by the communication circuit 11 from the device 3 and the equipment 5 via the network 9 in an operation information storage unit 133 described later. In addition, the processor 12 stores the sensor information received by the communication circuit 11 from the sensor 7 via the network 9 in a sensor information storage unit 134 described later.

[0106] In addition, the processor 12 functions as a first acquisition unit 121, a second acquisition unit 122, an estimation unit 124, an output unit 125, a determination unit 126, and an execution unit 127. The first acquisition unit 121 to the execution unit 127 may be realized by the processor 12 executing a given program stored in the memory 13, or may be configured by a dedicated hardware circuit. In the first embodiment, the processor 12 functions as the first acquisition unit 121, the second acquisition unit 122, the estimation unit 124, and the output unit 125. The details of the first acquisition unit 121 to the output unit 125 will be described later.

[0107] The memory 13 is constituted by a storage device such as a hard disk drive and a solid state drive. The memory 13 includes a machine information storage unit 131, a user information storage unit 132, an operation information storage unit 133, a sensor information storage unit 134, and a rule information storage unit 135. In addition, the machine information storage unit 131 to the rule information storage unit 135 are not limited to the memory 13, and may also be included in an external storage device that the processor 12 can access via the network 9 using the communication circuit 11.

[0108] The machine information storage unit 131 stores information related to the machine 3, the device 5, and the sensor 7 (hereinafter referred to as machine information). Specifically, the machine information related to the machine 3 and the device 5 includes identification information (hereinafter referred to as space ID) of the space 40 where the machine 3 and the device 5 are arranged, the machine ID of the machine 3 and the device 5, an address indicating a destination when control information is sent to the machine 3 and the device 5, functions of the machine 3 and the device 5, the start time of use of consumables used by the machine 3 and the device 5, and the status (normal, abnormal) of the machine 3 and the device 5. The machine information related to the sensor 7 includes the space ID of the space 40 where the sensor 7 is arranged and the sensor ID of the sensor 7.

[0109] The user information storage unit 132 stores information (hereinafter, user information) related to each of the plurality of users of the information processing system 100. Specifically, the user information includes a user ID of the user, information related to the attributes of the user (hereinafter, attribute information), and information indicating the characteristics of the user (hereinafter, characteristic information).

[0110] The attribute information includes, for example, the user's address, age, gender, group, role in the group, etc. The group includes, for example, family, company department, etc. The role includes, for example, father, mother, son, daughter, manager, section chief, etc.

[0111] The characteristic information includes the characteristics of the user and their strengths. The characteristics of the user are composed of one or more element characteristics. The element characteristics include, for example, laziness, rigor, wastefulness, thriftiness, regularity, irregularity, cleanliness, slovenliness, neuroticism, solitude, and communication. In addition, the characteristic information is divided into information indicating the characteristics of the user when there are no other users in the space 40 where the user exists, that is, when the user exists alone in the space 40 (hereinafter, the first characteristic information), and information indicating the characteristics of the user when there are other users in the space 40 where the user exists (hereinafter, the second characteristic information).

[0112] The user information storage unit 132 further stores comparison data. The comparison data is used to compare with the detection information included in the sensor information for the purpose of identifying the user existing in the space 40 where the sensor 7 is arranged, and identifying the user who has operated the machine 3 and the device 5 arranged in the same space 40 as the sensor 7. Specifically, the comparison data includes various data representing the characteristics of the user, such as image data representing an image obtained by photographing the face or whole body of the user, voice data representing the voice of the user, shape data representing the shape of the user, and the user ID of the user.

[0113] The user information storage unit 132 stores user-specific information such as the user's TODO list, schedule, life data, subscription services under contract, preferred external services, identification information of the output device 6 used by the user, and the IP address of the output device 6.

[0114] The operation information storage unit 133 stores the operation information received by the communication circuit 11 from the equipment 3 and the device 5 via the network 9 .

[0115] The sensor information storage unit 134 stores sensor information received by the communication circuit 11 from the sensor 7 via the network 9 .

[0116] The rule information storage unit 135 stores information (hereinafter referred to as rule information) indicating various rules used in various processes by the processor 12. Details of the rule information stored in the rule information storage unit 135 will be described later.

[0117] (Flow of feature output processing)

[0118] Next, the flow of the characteristic output process performed in the information processing device 1 will be described. The characteristic output process is a process of estimating the characteristics of the user of the information processing system 100 based on the operation information and the sensor information and outputting the characteristic information of the user. Figure 3 1 is a flowchart showing an example of a characteristic output process. The characteristic output process is executed at a given period such as every day, every week, or every month. However, this is not limited to this, and the characteristic output process may be executed every time the processor 12 acquires operation information.

[0119] First, in step S100 , after the last characteristic output process is completed, the first acquisition unit 121 acquires the operation information stored in the operation information storage unit 133 and the sensor information stored in the sensor information storage unit 134 .

[0120] Figure 4is a diagram showing an example of operation information. For example, Figure 4 The operation information obtained in step S100 of the characteristic output process started at 6:00 on September 1, 2021 after the previous characteristic output process started at 6:00 on August 31, 2021 is completed. The operation information indicates, for example, that at 7:05:30 on August 31, 2021, the user with user ID "User A" performed an operation to turn on the machine 3 with machine ID "Lighting 1".

[0121] In addition, as described above, the operation information stored in the operation information storage unit 133 sometimes does not include the user ID of the user who has operated the machine 3 and the device 5. In this case, the first acquisition unit 121 refers to the sensor information including the detection date and time that is consistent with the operation date and time included in the operation information. The so-called consistency means consistency within a given allowable range, and the same applies to the subsequent description. The first acquisition unit 121 determines the user who has operated the machine 3 or the device 5 in the space 40 indicated by the detection information by comparing the detection information included in the sensor information with the comparison data stored in the user information storage unit 132. The first acquisition unit 121 obtains the user ID of the determined user as the user ID included in the operation information.

[0122] Next, in step S200 , the processor 12 targets users of one or more user IDs included in the operation information acquired in step S100 for estimation, and executes a process (hereinafter, a characteristic estimation process) of estimating characteristics of the target users (hereinafter, target users).

[0123] In the characteristic estimation process, the characteristic when the target user is present alone in the space 40 is estimated, and the first characteristic information indicating the characteristic is updated. In addition, in the characteristic estimation process, the characteristic when another user (other user) different from the target user exists in the space 40 where the target user exists is estimated, and the second characteristic information indicating the characteristic is updated. The characteristics estimation process will be described in detail later.

[0124] Next, in step S300 , the output unit 125 outputs at least one of the first characteristic information and the second characteristic information of each target user.

[0125] Specifically, in step S300, the output unit 125 transmits (outputs) at least one of the first characteristic information and the second characteristic information of each target user updated in step S200 to the output device 6 ( Figure 1) or a given external device. Not limited to this, in step S300, the output unit 125 may also use the communication circuit 11 to send (output) at least one of the first characteristic information and the second characteristic information of each target user stored in the user information storage unit 132 to the output device 6 ( Figure 1 ) and other given external devices.

[0126] (Flow of characteristic estimation processing)

[0127] Next, the characteristics estimation process in step S200 will be described in detail. Figure 5 is a flowchart showing an example of characteristic estimation processing. For example, assuming that Figure 4 In this case, the processor 12 regards the user with the user ID "user A" included in the operation information as the target user and executes the characteristic estimation process for estimating the characteristics of the target user. In addition, the processor 12 regards the user with the user ID "user B" included in the operation information as the target user and executes the characteristic estimation process for estimating the characteristics of the target user.

[0128] First, in step S201 , the first acquisition unit 121 acquires operation information (first information) indicating the target user's operation (machine operation) of the machine 3 and the device 5 after the previous characteristic output process is completed and sensor information (first information) indicating the target user's behavior.

[0129] Specifically, in step S201, the first acquisition unit 121 acquires operation information including the user ID of the target user from the operation information acquired in step S100. Thereafter, the operation information representing the operation of the target user's machine 3 or device 5 acquired in step S201 is recorded as operation history information.

[0130] The first acquisition unit 121 refers to the machine information stored in the machine information storage unit 131, and acquires the sensor ID of the sensor 7 that is arranged in the same space 40 as the machine 3 or the device 5 of the machine ID included in the operation history information. The first acquisition unit 121 acquires the sensor information that includes the sensor ID and the operation date and time included in the operation history information coincide with the detection date and time from the sensor information acquired in step S100. Thereafter, the sensor information representing the action of the target user acquired in step S201 is recorded as the action history information.

[0131] Next, in step S202, the second acquisition unit 122 acquires information (second information) indicating whether other users exist in the space 40 where the target user exists after the previous characteristic output process is completed. Hereinafter, the information indicating whether other users exist in the space 40 where the target user exists acquired in step S202 is recorded as existence history information.

[0132] Specifically, in step S202, the second acquisition unit 122 refers to the machine information stored in the machine information storage unit 131, and acquires the space ID of the space 40 where the sensor 7 including the sensor ID included in the action history information is arranged as the space ID of the space 40 where the target user exists. The second acquisition unit 122 compares the detection information included in the action history information with the comparison data stored in the user information storage unit 132, and thereby identifies one or more persons existing in the space 40 where the target user exists.

[0133] When it is determined that only the target user exists, the second acquisition unit 122 acquires the detection date and time contained in the action history information, the space ID of the space 40 where the target user exists, and the information indicating that there are no other users (hereinafter, a separate mark), and establishes corresponding information as the existence history information.

[0134] On the other hand, in a case where it is determined that there is an object user and a user whose user ID is different from that of the object user, the second acquisition unit 122 acquires the detection date and time contained in the action history information, the space ID of the space 40 where the object user exists, the user ID of the above-mentioned different user and information indicating the existence of other users (hereinafter, multiple flags), and establishes corresponding information as existence history information.

[0135] In addition, when the existence target user and the person who cannot be identified using the comparison data are determined, the second acquisition unit 122 obtains information that establishes a correspondence between the detection date and time contained in the action history information, the space ID of the space 40 where the existence target user exists, and multiple tags as existence history information.

[0136] Next, in step S203 , the estimation unit 124 acquires the current characteristic information of the target user. Specifically, the estimation unit 124 acquires the first characteristic information and the second characteristic information of the target user stored in the user information storage unit 132 .

[0137] Figure 6 1 is a diagram showing an example of the first characteristic information. The first characteristic information is information indicating a characteristic (hereinafter referred to as the first characteristic) when the target user is alone in the space 40. The first characteristic information is information in which one or more element characteristics included in the first characteristic are associated with the strength of each element characteristic. Figure 6The first characteristic information shown shows the following: when the target user is alone in space 40, five element characteristics "lazy", "wasteful", "cleanliness-loving", "neurotic", and "fear of heat" are shown, and the strengths of these element characteristics are "0.3", "0.2", "0.3", "0.1", and "0.1", respectively.

[0138] In this embodiment, the larger the value of the intensity, the more strongly the target user exhibits the element characteristic. This also applies to the following description. In other words, Figure 6 The first characteristic information shown indicates that when the target user is alone in the space 40 , the characteristics of “laziness” and “cleanliness preference” are most strongly expressed.

[0139] Figure 7 FIG. 1 is a diagram showing an example of the second characteristic information. Figure 7 As shown, the second characteristic information is information indicating characteristics (hereinafter, second characteristics) when other users exist in the space 40 where the target user exists. The second characteristic information is information that establishes a correspondence between one or more element characteristics included in the second characteristic and the strength of each element characteristic, similarly to the first characteristic information. Figure 7 The second characteristic information shown shows the following: when there are other users in the space 40 where the target user exists, five element characteristics are shown: "lazy", "thrifty", "cleanliness-loving", "neurotic", and "communicative", and the strengths of these element characteristics are "0.1", "0.1", "0.5", "0.1", and "0.2". In other words, Figure 7 The second characteristic information shown indicates that the characteristic of “liking cleanliness” is most strongly expressed when other users exist in the space 40 where the target user exists.

[0140] Next, in step S204, the extraction unit 123 extracts information representing the operations and actions of the target user on the machine 3 or the device 5 in a space 40 (hereinafter, the first environment) where no other users exist and in a space 40 (hereinafter, the second environment) where other users exist, based on the existence history information obtained in step S202, from the operation history information and the action history information obtained in step S201.

[0141] Specifically, in step S204, the extraction unit 123 refers to the machine information stored in the machine information storage unit 131 to determine whether the existence history information includes a single flag or multiple flags, and the existence history information includes a detection date and time consistent with the operation date and time included in the operation history information, and a space ID of a space 40 of a machine 3 or device 5 configured with the machine ID included in the operation history information.

[0142] When the extraction unit 123 determines that a single flag is included, the operation history information is extracted as information indicating the operation of the target user on the machine 3 or device 5 in the first environment (hereinafter, first operation history information). On the other hand, when the extraction unit 123 determines that a plurality of flags are included, the operation history information is extracted as information indicating the operation of the target user on the machine 3 or device 5 in the second environment (hereinafter, second operation history information).

[0143] In addition, the extraction unit 123 refers to the machine information stored in the machine information storage unit 131 to determine whether the existence history information includes a single flag or multiple flags, and the existence history information includes a detection date and time consistent with the detection date and time included in the action history information, and the space ID of the space 40 where the sensor 7 with the sensor ID included in the action history information is configured.

[0144] When the extraction unit 123 determines that a single flag is included, the action history information is extracted as information indicating an action of the target user in the first environment (hereinafter, first action history information). On the other hand, when the extraction unit 123 determines that a plurality of flags are included, the action history information is extracted as information indicating an operation of the target user on the machine 3 or the device 5 in the second environment (hereinafter, second action history information).

[0145] That is, the first operation history information and the first action history information extracted in step S204 represent an example of the first action information of the present disclosure, and the second operation history information and the second action history information represent an example of the second action information of the present disclosure.

[0146] Next, in step S205 , the estimating unit 124 extracts candidate characteristics estimated as element characteristics of the first characteristic of the target user existing in the first environment and the second characteristic of the target user existing in the second environment based on the information extracted in step S204 .

[0147] Specifically, in step S205 , the estimation unit 124 obtains first rule information from the rule information storage unit 135 , which specifies the relationship between one or more candidate characteristics that can become element characteristics and one or more feature groups representing characteristics of the operation or behavior of the machine 3 or the device 5 .

[0148] Figure 8 1 is a diagram showing an example of first rule information that specifies a relationship between one or more candidate characteristics and one or more feature groups representing characteristics of the operation of the device 3 or the equipment 5. Figure 8In the first rule information shown, eight candidate characteristics are associated with a feature group indicating two characteristics of the user's operation on the device 3 or the equipment 5 with each candidate characteristic as an element characteristic.

[0149] For example, in Figure 8 In the first rule information shown, the candidate characteristic "laziness" is corresponded to a feature group representing the characteristics of the user's lighting operation based on the candidate characteristic "laziness" as an element characteristic, namely "the frequency of lighting operation is low", and the characteristics of the refrigerator operation, namely "the refrigerator door is opened for a long time".

[0150] Fig. 9 1 is a diagram showing an example of first rule information that defines a relationship between one or more candidate characteristics and one or more feature groups representing characteristics of an action. Fig. 9 In the first rule information shown, eight candidate characteristics are associated with a feature group indicating two characteristics based on the user's behavior with each candidate characteristic as an element characteristic.

[0151] For example, in Fig. 9 In the first rule information shown, the candidate characteristic "laziness" is associated with a feature group representing a feature "the desk is messy" based on the user's action with the candidate characteristic "laziness" as an element characteristic and a feature of the action "the frequency of folding laundry is low".

[0152] In addition, in the first rule information, the number of features included in the feature group is not limited to 2, and may be 1 or 3 or more. However, in the first rule information, the number of features included in the feature group is preferably 2 or more. This is because the user's intention and preference may not be clearly reflected in one feature based on the operation or action of the user's machine 3 or device 5.

[0153] Then, the estimating unit 124 refers to the first rule information, and extracts the element characteristics of the first characteristic of the target user and the estimated candidate characteristics based on the first operation history information and the first action history information extracted in step S204 .

[0154] Specifically, the estimating unit 124 refers to Figure 8 The first rule information shown determines whether the operations of the machine 3 or device 5 of the target user in the first environment shown in the first operation history information include operations of the machine 3 or device 5 that exhibit one or more feature groups (first feature group) included in the first rule information.

[0155] The operation of the machine 3 or device 5 that exhibits the feature group means that the number of executions of the operation of the machine 3 or device 5 that exhibits each of the one or more features included in the feature group is all one or more. Information indicating the operation of the machine 3 or device 5 that exhibits each feature is stored in the rule information storage unit 135. The estimation unit 124 makes the above determination with reference to this information.

[0156] When determining that the operation of the machine 3 or the device 5 includes one or more feature groups, the inference unit 124 determines one or more candidate characteristics (first candidate characteristics) associated with the one or more feature groups in the first rule information. The inference unit 124 infers the determined one or more candidate characteristics as element characteristics of the first characteristic of the target user, and extracts the one or more candidate characteristics.

[0157] For example, suppose that the operation of the machine 3 or device 5 of the target user in the first environment shown in the first operation history information includes one or more operations that are similar to Figure 8 The candidate characteristic "laziness" included in the first rule information shown in the figure establishes an operation of a lighting having the characteristic "less frequency of lighting operation" included in the corresponding characteristic group, and includes an operation of a refrigerator having the characteristic "the refrigerator door is opened for a long time" included in the characteristic group exhibited more than once. In this case, the inference unit 124 infers the candidate characteristic "laziness" corresponding to the characteristic group in the first rule information as an element characteristic of the first characteristic of the target user, and extracts the candidate characteristic "laziness".

[0158] In addition, the lighting operation that shows the characteristic of "the lighting operation frequency is low" means, for example, an operation in which the number of times the lighting is turned on or off per day is less than a given number of times (for example, 2 times). However, this is not limited to this, and the operation may be, for example, an operation in which the ratio of the number of times the user turns the lighting on or off to the number of times the user is not in the space 40 where the lighting is arranged is less than a given value (for example, 0.7). The number of times the user is not in the space 40 where the lighting is arranged may be obtained by referring to the machine information stored in the machine information storage unit 131 and the first action history information including the detection date and time that coincides with the operation date and time included in the first operation history information.

[0159] On the other hand, the operation of the refrigerator showing the characteristic of "the refrigerator door is opened for a long time" is, for example, an operation indicating that the average time the refrigerator door is opened every day is more than a given time. However, the operation may also be, for example, an operation in which the ratio of the number of times a warning indicating that the door has been opened for more than a given time is output after the user opens the refrigerator door to the number of times the user opens the refrigerator door is more than a given value (for example, 0.3).

[0160] Similarly, the estimation unit 124 refers to Fig. 9 The first rule information shown determines whether the actions of the target user in the first environment shown in the first action history information include actions that exhibit one or more feature groups (first feature group) included in the first rule information.

[0161] The number of times the action that expresses the feature group is performed means that the number of times each action that expresses one or more features included in the feature group is performed is more than one time. Information indicating the action that expresses each feature is stored in the rule information storage unit 135. The estimation unit 124 refers to this information to make the above determination.

[0162] When determining that an action showing one or more characteristic groups is included, the inference unit 124 determines one or more candidate characteristics (first candidate characteristics) associated with the one or more characteristic groups in the first rule information. The inference unit 124 infers the determined one or more candidate characteristics as element characteristics of the first characteristic of the target user, and extracts the one or more candidate characteristics.

[0163] For example, suppose that the actions of the target user in the first environment indicated by the first action history information include one or more actions that are similar to Fig. 9 The candidate characteristic "laziness" included in the first rule information shown has established an action of the characteristic "the table is messy" included in the corresponding characteristic group, and includes an action that exhibits the characteristic "the frequency of folding laundry is low" included in the characteristic group more than once. In this case, the inference unit 124 infers the candidate characteristic "laziness" that is associated with the characteristic group in the first rule information as an element characteristic of the first characteristic of the target user, and extracts the candidate characteristic "laziness".

[0164] In addition, the action showing the characteristic "the table is messy" is, for example, an action indicating that a given number of items or more are on the table for a given period of time or more. The action showing the characteristic "the frequency of folding laundry is low" is, for example, an action indicating that the ratio of the number of times clothes are worn in an unfolded state to the number of times clothes are worn is greater than a given value.

[0165] As described above, the estimation unit 124 refers to Figure 8The first rule information shown in the figure determines whether the operations of the machine 3 or device 5 of the target user in the second environment shown in the second operation history information include operations of the machine 3 or device 5 that exhibit one or more feature groups (second feature groups) included in the first rule information. When it is determined that the operations of the machine 3 or device 5 that exhibit one or more feature groups are included, the inference unit 124 determines one or more candidate characteristics (second candidate characteristics) associated with the one or more feature groups in the first rule information. The inference unit 124 infers the determined one or more candidate characteristics as element characteristics of the second characteristic of the target user, and extracts the one or more candidate characteristics.

[0166] In addition, the estimation unit 124 refers to Fig. 9 The first rule information shown in the second rule information is used to determine whether the actions of the target user in the second environment shown in the second action history information include actions that exhibit one or more feature groups (second feature groups) included in the first rule information. When it is determined that the actions exhibit one or more feature groups are included, the inference unit 124 determines one or more candidate characteristics (second candidate characteristics) associated with the one or more feature groups in the first rule information, infers the element characteristics of the second characteristic of the target user, and extracts the one or more candidate characteristics.

[0167] In addition, in step S205, the inference unit 124 may extract the candidate characteristic based on either the first operation history information or the first action history information extracted in step S204, or the inference unit 124 may extract the candidate characteristic based on either the second operation history information or the second ...

[0168] If the candidate characteristic is not extracted in step S205 (No in step S205 ), the first characteristic information and the second characteristic information acquired in step S203 are not updated, and the characteristic estimation process ends.

[0169] On the other hand, it is assumed that at least one candidate characteristic that is estimated to be an element characteristic of the characteristic of the target user existing in at least one of the first environment and the second environment is extracted in step S205 (Yes in step S205). In this case, in step S206, the estimation unit 124 updates the characteristic information indicating the characteristic of the target user existing in the first characteristic information and the second characteristic information acquired in step S203, and ends the characteristic estimation process.

[0170] Specifically, it is assumed that a candidate characteristic estimated as an element characteristic of the first characteristic is extracted in step S205. In this case, in step S206, if the candidate characteristic is not included in the first characteristic indicated by the first characteristic information, the estimation unit 124 adds the candidate characteristic as an element characteristic of the first characteristic. Furthermore, the estimation unit 124 updates (sets) the strength of each element characteristic included in the first characteristic, and updates the first characteristic information stored in the user information storage unit 132 using the first characteristic information indicating the updated first characteristic.

[0171] On the other hand, it is assumed that a candidate characteristic estimated as an element characteristic of the second characteristic is extracted in step S205. In this case, in step S206, if the candidate characteristic is not included in the second characteristic indicated by the second characteristic information, the estimation unit 124 adds the candidate characteristic as an element characteristic of the second characteristic. Furthermore, the estimation unit 124 updates (sets) the strength of each element characteristic included in the second characteristic, and updates the second characteristic information stored in the user information storage unit 132 using the second characteristic information indicating the updated second characteristic.

[0172] The following is a detailed description of step S206. In addition, step S206 performed when a candidate characteristic of an element characteristic estimated as the first characteristic (first element characteristic) is extracted in step S205 and step S206 performed when a candidate characteristic of an element characteristic estimated as the second characteristic (second element characteristic) is extracted in step S205 are the same processing contents.

[0173] Therefore, the following describes in detail step S206, which is performed when the candidate characteristic "laziness" is extracted as a candidate characteristic of the element characteristic estimated as the second characteristic in step S205. The detailed description of step S206, which is performed when the candidate characteristic estimated as the element characteristic of the first characteristic is extracted in step S205, is omitted.

[0174] Fig.10 This is a diagram showing an example of the relationship between the characteristics of the target user before updating and the number of executions of characteristic actions corresponding to each element characteristic. Fig.10 Shows Figure 7 The second characteristic information representing the second characteristic of the target user is shown as an example of the relationship between the number of executions of the characteristic action corresponding to each element characteristic included in the second characteristic. The number of executions of the characteristic action corresponding to each element characteristic included in the second characteristic is associated with the second characteristic information representing the second characteristic and stored in the user information storage unit 132.

[0175] The characteristic actions corresponding to each element characteristic refer to the operation or action of the machine 3 or device 5 that represents the characteristic group corresponding to the candidate characteristic representing each element characteristic in the first rule information. For example, the characteristic actions corresponding to the element characteristic "lazy" refer to the operation or action of the machine 3 or device 5 that represents the characteristic group corresponding to the candidate characteristic representing each element characteristic in the first rule information. Figure 8 ) establishes two features corresponding to the candidate characteristic "laziness" in the first rule information ( ), namely, the operation of the lighting and the operation of the refrigerator, which are "the frequency of operating the lighting is low" and "the refrigerator door is open for a long time". In addition, the so-called feature actions corresponding to the candidate characteristic "laziness" represent the actions that are respectively manifested in the first rule information ( Fig. 9 ) in the example above, two features corresponding to the candidate characteristic “laziness” are “the table is messy” and “the frequency of folding laundry is low”.

[0176] The number of executions of the characteristic action corresponding to the element characteristic "laziness" included in the second characteristic represents the minimum value of the number of times the lighting operation and the refrigerator operation as the characteristic action were respectively performed in the operation of the machine 3 or device 5 of the object user in the second environment shown in the second operation history information, and the number of times the above-mentioned actions as the characteristic action were performed in the actions of the object user in the second environment shown in the second action history information.

[0177] That is, the number of executions of the characteristic action corresponding to each element characteristic is the minimum number of executions of the operation and action of the machine 3 or device 5, which exhibits each characteristic included in the characteristic group corresponding to the candidate characteristic representing each element characteristic. However, this is not limited to this, and the number of executions of the characteristic action corresponding to each element characteristic may also be the average value or the maximum value of the number of executions of the operation and action of the machine 3 or device 5, which exhibits each characteristic included in the characteristic group corresponding to the candidate characteristic representing each element characteristic.

[0178] In step S206, the estimation unit 124 refers to the second operation history information and the second action history information, and calculates the number of executions of the characteristic action (second characteristic action) corresponding to the element characteristic included in the second characteristic and representing the candidate characteristic extracted in step S205. The estimation unit 124 adds the number of executions of the characteristic action corresponding to the element characteristic included in the second characteristic stored in the user information storage unit 132 to the number of executions of the characteristic action corresponding to the element characteristic calculated this time.

[0179] Fig.11 1 is a diagram showing an example of the relationship between the updated characteristics of the target user and the number of executions of the characteristic actions corresponding to each element characteristic. Fig.11In step S206, the estimation unit 124 calculates the number of executions of the characteristic action corresponding to the element characteristic "laziness" as "1 time", and compares the number of executions "1 time" with the Fig.10 The result obtained by adding up the number of execution times of the characteristic action corresponding to the element characteristic “slackness” included in the second characteristic shown is “11 times”.

[0180] Then, the estimating unit 124 sets the intensity of each element characteristic included in the second characteristic information based on the number of executions of the characteristic action corresponding to each element characteristic included in the second characteristic.

[0181] Specifically, the estimation unit 124 sets the ratio of the number of executions of the characteristic actions corresponding to each element characteristic included in the second characteristic to the sum of the number of executions of one or more characteristic actions corresponding to one or more element characteristics included in the second characteristic as the strength of each element characteristic. Thus, the sum of the strengths of the element characteristics included in the second characteristic is normalized to 1.

[0182] exist Fig.11 In the example of , the total number of executions of the five characteristic actions corresponding to the five element characteristics included in the second characteristic is 101 times (=11 times+10 times+50 times+10 times+20 times). Therefore, the estimation unit 124 sets the ratio of the number of executions of the characteristic action "11 times" corresponding to the element characteristic "laziness" to the total "101 times" as "0.109 (=11 times / 101 times)" as the intensity of the element characteristic "laziness".

[0183] Similarly, the inference unit 124 sets the ratios of "0.099", "0.495", "0.099", and "0.198" of the number of executions of the characteristic actions corresponding to the element characteristics "thrifty", "likes cleanliness", "neurotic", and "likes to communicate" "10 times", "50 times", "10 times", and "20 times" to the total number of executions of the five characteristic actions corresponding to the five element characteristics included in the second characteristic "101 times" as the intensities of the element characteristics "thrifty", "likes cleanliness", "neurotic", and "likes to communicate".

[0184] In addition, the method for setting the strength of each element characteristic included in the second characteristic is not limited to this. For example, the estimation unit 124 may also set the number of executions of the characteristic action corresponding to each element characteristic included in the second characteristic (e.g., "lazy") (e.g., "11 times") as the strength of each element characteristic included in the second characteristic.

[0185] The following modifications can be adopted for the structure of the first embodiment.

[0186] (1) In the first embodiment, an example is described in which the intensity of each element characteristic included in the first characteristic and the second characteristic is set using the number of executions of the characteristic action corresponding to each element characteristic in step S206. However, for example, the above-mentioned lighting operation and refrigerator operation, which are characteristic actions corresponding to the element characteristic "laziness", are likely to be executed many times a day. In contrast, for example, the characteristic actions corresponding to the element characteristic "regularity" are those that are respectively shown in the first rule information ( Figure 8 ) The two features "fixed lighting time in the morning" and "fixed coffee machine start-up time" that correspond to the candidate feature "regularity" are the lighting operations and coffee machine operations that the target user first performs after getting up. This feature action is an action that is performed only once a day.

[0187] In the first embodiment, for example, when the number of executions of characteristic actions corresponding to the element characteristics "lazy" and "regular" obtained from the first operation history information of a day is both "1 time", although the number of executions of each characteristic action that can be performed each day is different as described above, the same number of executions "1 time" is used to update the strengths of these element characteristics with the same weight.

[0188] For this purpose, as described below, the strength of each element characteristic may be updated with the same weight based on the number of executions per given time of a characteristic action corresponding to each element characteristic by a user different from the target user.

[0189] Specifically, in step S206, the inference unit 124 refers to the second operation history information and the second action history information to calculate the number of executions (second execution count) per given time (for example, one day) of the characteristic action corresponding to the element characteristic representing the candidate characteristic extracted in step S205 and included in the second characteristic of the target user. Hereinafter, for convenience of description, the characteristic action corresponding to the element characteristic representing the candidate characteristic extracted in step S205 and included in the second characteristic of the target user is recorded as the target characteristic action.

[0190] The estimation unit 124 obtains operation information including the user ID of one or more users (one or more other users) different from the target user from the operation information storage unit 133 as information indicating the operation of the one or more users on the machine 3 or the device 5 (hereinafter referred to as the third operation history information). In addition, the estimation unit 124 obtains sensor information including the detection date and time that matches the operation date and time included in the third operation history information and the space ID of the space 40 of the machine 3 or the device 5 where the machine ID included in the third operation history information is arranged from the sensor information storage unit 134 as information indicating the action based on the one or more users (hereinafter referred to as the third action history information). That is, the third operation history information and the third action history information represent an example of the third action information of the present disclosure.

[0191] The estimation unit 124 refers to the third operation history information and the third action history information, calculates the number of executions per given time of the target characteristic action by each of one or more users different from the target user, and calculates the average value (second average value).

[0192] The estimation unit 124 stores the result obtained by dividing the average value by the number of executions of the target characteristic action per the given time calculated by referring to the second operation history information and the second action history information in the user information storage unit 132 and adds the result to the number of executions of the target characteristic action.

[0193] Similarly, the estimating unit 124 updates the number of executions of the characteristic action corresponding to the element characteristic representing the candidate characteristic extracted in step S205 and included in the first characteristic of the target user.

[0194] (2) In the first embodiment, the following description is given of step S100 ( Figure 3 ), the first acquisition unit 121 acquires the operation information stored in the operation information storage unit 133 and the sensor information stored in the sensor information storage unit 134 after the previous characteristic output process is completed. However, instead of this method, in step S100, the first acquisition unit 121 may acquire the operation information stored in the operation information storage unit 133 and the sensor information stored in the sensor information storage unit 134 during the first given period. The first given period is, for example, the most recent week, month, three months, or one year.

[0195] Thus, in step S201, the first acquisition unit 121 may also acquire operation information (first information) indicating the operation of the target user's machine 3 and device 5 and sensor information (first information) indicating the behavior of the target user during the first given period. As a result, in step S202, the second acquisition unit 122 may also acquire presence history information (second information) indicating the history of the presence or absence of other users in the space 40 where the target user exists during the first given period.

[0196] (3) In the first embodiment and the above-mentioned modification, an example is described in which the intensity of each element characteristic included in the first characteristic and the second characteristic of the target user is set based on the operation history information and the action history information acquired in step S201 by executing the characteristic output processing. However, differently from this, each element characteristic and its intensity included in the first characteristic and the second characteristic set by the characteristic output processing may be updated based on the operation and action of the target user on the machine 3 or the device 5. This structure can be realized, for example, as follows.

[0197] Specifically, the estimation unit 124 refers to the first characteristic information and the second characteristic information of each user stored in the user information storage unit 132 at a given timing such as once a day. The estimation unit 124 refers to the operation information including the user ID of each user stored in the operation information storage unit 133. In addition, the estimation unit 124 refers to the sensor information stored in the sensor information storage unit 134, the sensor information including the detection date and time that coincides with the operation date and time included in the operation information, and the space ID of the space 40 of the machine 3 or the device 5 where the machine ID included in the operation information is arranged.

[0198] For each element characteristic included in the first characteristic information of each user, when the characteristic action corresponding to each element characteristic has not been performed for more than the first given time, the estimation unit 124 excludes each element characteristic from the first characteristic, or reduces the strength of each element characteristic at a given first reduction rate. Similarly, for each element characteristic included in the second characteristic information of each user, when the characteristic action corresponding to each element characteristic has not been performed for more than the second given time, the estimation unit 124 excludes each element characteristic from the second characteristic, or reduces the strength of each element characteristic at a given second reduction rate. In this way, it is possible to exclude inappropriate element characteristics included in the first and second characteristics of each user, or reduce the strength of the element characteristics, and to relatively increase the strength of element characteristics other than the element characteristics included in the first and second characteristics.

[0199] In addition, the first given time and the second given time are, for example, three days, one week, one month, three months or one year. The first given time and the second given time may be the same or different. The first reduction rate and the second reduction rate are, for example, 10%. The first reduction rate and the second reduction rate may be the same or different.

[0200] In addition, the first given time and the second given time may be determined based on the frequency of execution of the characteristic action corresponding to each element characteristic. For example, it is assumed that the estimation unit 124 determines whether the characteristic action corresponding to the element characteristic "laziness" has not been performed for more than the first given time (second given time) when determining whether the characteristic action corresponding to the element characteristic "laziness" has not been performed for three days. In this case, the estimation unit 124 may then determine the first given time (second given time) used to determine whether to exclude the element characteristic "laziness" or whether to reduce the intensity of the element characteristic "laziness" to be three days.

[0201] In addition, in this variation, the estimating unit 124 may normalize the intensity of each element characteristic included in the first characteristic and the second characteristic after excluding each element characteristic from the first characteristic and the second characteristic, or after reducing the intensity of each element characteristic included in the first characteristic and the second characteristic.

[0202] Specifically, the estimation unit 124 may calculate the ratio of the intensity of each element characteristic included in the first characteristic to the sum of the intensities of one or more element characteristics included in the first characteristic after the above-mentioned exclusion or reduction process, and reset the ratio as the intensity of each element characteristic included in the first characteristic. Similarly, the estimation unit 124 may calculate the ratio of the intensity of each element characteristic included in the second characteristic to the sum of the intensities of one or more element characteristics included in the second characteristic after the above-mentioned exclusion or reduction process, and reset the ratio as the intensity of each element characteristic included in the second characteristic.

[0203] Fig.12 is a diagram showing an example of changes in characteristic information. For example, Fig.12 An example of characteristic output processing at time t12 is shown. Specifically, the following example is shown: since the characteristic action corresponding to the candidate characteristic "neuroticism" is performed "once" based on the first operation history information and the first action history information indicating the operation and action of the machine 3 or the device 5 during the period from time t11 to time t12, the element characteristic "neuroticism" is added to the first characteristic, and the intensity of the element characteristic "neuroticism" is calculated to be "1.0".

[0204] Fig.12The following example is shown: After time t12, characteristic output processing is performed at time t22, and because the characteristic action corresponding to the candidate characteristic "like cleanliness" is performed "once" based on the first operation history information and the first action history information corresponding to the operation and action of the machine 3 or device 5 during the period from time t21 to time t22, the element characteristic "like cleanliness" is added to the first characteristic. As a result, the strength of the element characteristic "like cleanliness" is calculated as "0.5 (=1 time / (1 time+1 time))", and the strength of the element characteristic "neuroticism" is recalculated as "0.5 (=1 time / (1 time+1 time))".

[0205] Fig.12 The following example is shown: Afterwards, the characteristic output processing is performed at time t32 and time t42, and the first characteristic information is updated. Furthermore, at time t5, the characteristic action corresponding to the element characteristic "neuroticism" is not performed for the first given time, so the element characteristic "neuroticism" is excluded from the first characteristic information. In addition, Fig.12 The following example is shown: at time t5, by excluding the element characteristic "neuroticism" from the first characteristic information, the strengths of the element characteristics "like cleanliness" and "thrifty" included in the first characteristic are reset to "0.67 (=2 times / (2 times+1 time))" and "0.33 (=1 time / (2 times+1 time))".

[0206] Fig.12 The following example is shown: Thereafter, characteristic output processing is performed at time t62, and since the characteristic action corresponding to the element characteristic "thrifty" of the object user is performed "1 time", the strengths of the element characteristics "like cleanliness" and "thrifty" contained in the first characteristic information are reset to "0.50 (=2 times / (2 times+2 times))" and "0.5 (=2 times / (2 times+2 times))".

[0207] (4) In the first embodiment and the above-mentioned variations, an example is described in which the first characteristic is updated based on the operation and action of the machine 3 or device 5 of the target user in the first environment where no other users exist, and the second characteristic is updated based on the operation and action of the machine 3 or device 5 of the target user in the second environment where other users exist.

[0208] However, a candidate characteristic (hereinafter, a common candidate characteristic) may be predetermined, and the candidate characteristic may be considered to be an element characteristic (hereinafter, a common element characteristic) possessed by the target user in either the first environment or the second environment. Furthermore, when the operation or action of the machine 3 or device 5 of the target user in at least one of the first environment and the second environment includes a characteristic action (hereinafter, a common characteristic action) corresponding to the common candidate characteristic, the common candidate characteristic may be inferred as a common element characteristic included in both the first characteristic and the second characteristic, and added to the first characteristic and the second characteristic.

[0209] In this case, the strength of the common element characteristics included in both the first characteristic and the second characteristic may be set based on the total number of executions of the common characteristic actions in the operations and actions of the machine 3 or device 5 of the target user, without distinguishing whether the target user exists in the first environment or the second environment. This structure can be implemented, for example, as follows.

[0210] In step S205 ( Figure 5 ), the inference unit 124 determines whether a common characteristic action is included in any one of the first operation history information, the first action history information, the second operation history information, and the second action history information.

[0211] When determining that a common characteristic action is included, the inference unit 124 infers the common candidate characteristic as a common element characteristic included in both the first characteristic and the second characteristic. Furthermore, for a characteristic that does not include the same element characteristic as the common candidate characteristic in the first characteristic and the second characteristic, the inference unit 124 adds the common candidate characteristic as a common element characteristic included in both the first characteristic and the second characteristic.

[0212] In this case, the estimation unit 124 calculates the number of executions of the common characteristic action (hereinafter, the first execution number) based on the first operation history information and the first action history information, and calculates the number of executions of the common characteristic action (hereinafter, the second execution number) based on the second operation history information and the second action history information. If the number of executions of the common characteristic action is not stored in the user information storage unit 132 in correspondence with the user information of the target user, the estimation unit 124 associates the sum of the first execution number and the second execution number with the user information of the target user as the number of executions of the common characteristic action, and stores it in the user information storage unit 132. If the number of executions of the common characteristic action is stored in the user information storage unit 132 in correspondence with the user information of the target user, the estimation unit 124 adds the execution number to the sum of the first execution number and the second execution number.

[0213] Furthermore, the inference unit 124 uses the number of executions of common characteristic actions stored in correspondence with the user information of the target user for the common element characteristics included in the first characteristic, and uses the number of executions of characteristic actions corresponding to the other element characteristics stored in correspondence with the first characteristic for each other element characteristics included in the first characteristic, to calculate the common element characteristics included in the first characteristic and the strength of each element characteristic in the same way as in the first embodiment.

[0214] Similarly, the inference unit 124 uses the number of executions of common feature actions stored in correspondence with the user information of the target user for the common feature characteristics included in the second feature, and uses the number of executions of feature actions corresponding to the other feature characteristics stored in correspondence with the second feature for the other feature characteristics included in the second feature, to calculate the common feature characteristics included in the second feature and the strength of each feature characteristic.

[0215] (5) In the above-mentioned variation example (4), an example is described of calculating the strength of the common element characteristics included in the first characteristic and the second characteristic by using the sum of the number of executions of the common characteristic action calculated based on the first operation history information and the first action history information (the first execution number) and the number of executions of the common characteristic action calculated based on the second operation history information and the second action history information (the second execution number).

[0216] In this case, the number of executions of the common characteristic action is calculated based on the operations and actions of the target user's machine 3, etc. during the period when the target user exists in the first environment and the second environment. On the other hand, the number of executions of characteristic actions corresponding to other element characteristics included in the first characteristic and the second characteristic is calculated based on the operations and actions of the target user's machine 3, etc. during the period when the target user exists in the first environment and the second environment respectively for a period shorter than the period for calculating the number of executions of the common characteristic action. Therefore, in each of the first characteristic and the second characteristic, it is highly likely that the strength of the common element characteristic is stronger than the strength of the other element characteristics.

[0217] For this purpose, in the above-mentioned modification (4), the strength of the common element characteristic included in each of the first characteristic and the second characteristic may be set based on the number of executions obtained by allocating the number of executions of the common characteristic action in proportion to the periods during which the target user exists in the first environment and the second environment, respectively. This configuration may be implemented, for example, as follows.

[0218] In the configuration of the above-mentioned modification (4), the estimation unit 124 further calculates the time that the target user stayed in the first environment (hereinafter referred to as the first time) from the earliest date and time to the latest date and time among the operation dates and times included in the first operation history information and the first action history information. Similarly, the estimation unit 124 calculates the time that the target user stayed in the second environment (hereinafter referred to as the second time) from the earliest date and time to the latest date and time among the operation dates and times included in the second operation history information and the second action history information.

[0219] The estimation unit 124 calculates the number of executions of the common characteristic action in the first environment by dividing the sum of the first time and the second time by the product of the sum of the first execution count and the second execution count and the first time. The estimation unit 124 calculates the number of executions of the common characteristic action in the second environment by dividing the sum of the first time and the second time by the product of the sum of the first execution count and the second execution count and the second time.

[0220] If the number of executions of the common characteristic action in the first environment (hereinafter, the first common execution number) is not stored in the user information storage unit 132 in correspondence with the first characteristic information of the target user, the estimation unit 124 associates the above-calculated number of executions of the common characteristic action in the first environment with the first characteristic information of the target user as the first common execution number and stores it in the user information storage unit 132. If the first common execution number is stored in the user information storage unit 132 in correspondence with the first characteristic information of the target user, the estimation unit 124 adds the first common execution number to the above-calculated number of executions of the common characteristic action in the first environment.

[0221] Similarly, when the number of executions of the common characteristic action in the second environment (hereinafter, the second common execution number) is not stored in the user information storage unit 132 in correspondence with the second characteristic information of the target user, the estimation unit 124 associates the above-calculated number of executions of the common characteristic action in the second environment with the second characteristic information of the target user as the second common execution number and stores it in the user information storage unit 132. When the second common execution number is stored in the user information storage unit 132 in correspondence with the second characteristic information of the target user, the estimation unit 124 adds the second common execution number to the above-calculated number of executions of the common characteristic action in the second environment.

[0222] Furthermore, the inference unit 124 uses the first common execution count stored in correspondence with the first characteristic information for the common element characteristics included in the first characteristic, and uses the execution count of feature actions corresponding to other candidate characteristics stored in correspondence with the first characteristic information for other element characteristics included in the first characteristic, to calculate the common element characteristics included in the first characteristic and the strength of each element characteristic in the same manner as in the first embodiment.

[0223] Similarly, the inference unit 124 uses the second common execution count stored in correspondence with the second characteristic information for the common element characteristics included in the second characteristic, and uses the execution count of feature actions corresponding to other candidate characteristics stored in correspondence with the second characteristic information for other element characteristics included in the second characteristic, to calculate the common element characteristics included in the second characteristic and the strength of each element characteristic.

[0224] (6) In the above-mentioned variation example (4), an example is described in which, in the operation and action of the target user on the machine 3, etc., when the number of executions of the common feature action corresponding to the predetermined common candidate characteristic is more than one, the common candidate characteristic is inferred to be a common element characteristic included in both the first characteristic and the second characteristic.

[0225] However, differently from this, the estimation unit 124 may refer to the first characteristic information and the second characteristic information of each user stored in the user information storage unit 132 at a given time, such as once a day. Furthermore, when there are identical element characteristics with similar strengths between the first characteristic and the second characteristic, the estimation unit 124 may estimate the identical element characteristics as common element characteristics included in both the first characteristic and the second characteristic.

[0226] The same element characteristics with similar strengths refer to the same element characteristics whose strengths are consistent within a given range. For example, when the strength of the element characteristic "lazy" included in the first characteristic and the strength of the element characteristic "lazy" included in the second characteristic are consistent within a given range (for example, 0.01), the inference unit 124 may also infer the element characteristic "lazy" as a common element characteristic included in both the first characteristic and the second characteristic.

[0227] In addition, the operations and actions of each user on the machine 3 or the device 5 may include similar characteristic actions, which are actions that express the first rule information ( Figure 8 , Fig. 9), and a characteristic action with similar execution times per unit time in each of the first environment and the second environment. In this case, the inference unit 124 may also infer the candidate characteristic corresponding to the characteristic group represented by the similar characteristic action as a common element characteristic included in both the first characteristic and the second characteristic of each user. The so-called similarity in the number of executions per unit time means that the number of executions per unit time is consistent within a given range. Similar characteristic actions can be determined, for example, as follows.

[0228] Specifically, the inference unit 124 obtains operation information (hereinafter, past operation information) including the user ID of each user from the operation information storage unit 133 in the same manner as step S201, and obtains sensor information (hereinafter, past action information) including a detection date and time consistent with the operation date and time included in the past operation information from the sensor information storage unit 134.

[0229] The estimation unit 124 compares the detection information included in the past behavior information with the comparison data stored in the user information storage unit 132 , thereby identifying one or more persons existing in the space 40 indicated by the detection information.

[0230] When it is determined that only one user exists in the space 40 indicated by the detection information, the estimation unit 124 obtains the past action information including the detection information as information indicating the action of each user in the first environment (hereinafter, first past action information). On the other hand, it is assumed that the estimation unit 124 determines that each user and a user whose user ID is different from that of each user exist in the space 40 indicated by the detection information, or each user and a person who cannot be identified using the comparison data exist. In these cases, the estimation unit 124 obtains the past action information including the detection information as information indicating the action of each user in the second environment (hereinafter, second past action information).

[0231] The inference unit 124 refers to the machine information stored in the machine information storage unit 131, and obtains past operation information including the machine ID of the machine 3 or device 5 configured in the same space 40 as the sensor 7 of the sensor ID included in the first past action information, and including the operation date and time consistent with the detection date and time included in the first past action information, as information representing the operation of the machine 3 or device 5 of each user in the first environment (hereinafter, the first past operation information).

[0232] Similarly, the inference unit 124 refers to the machine information stored in the machine information storage unit 131, and obtains past operation information including the machine ID of the machine 3 or device 5 configured in the same space 40 as the sensor 7 of the sensor ID included in the second past action information, and including the operation date and time consistent with the detection date and time included in the second past action information, as information representing the operation of the machine 3 or device 5 of each user in the second environment (hereinafter, the second past operation information).

[0233] The estimation unit 124 identifies the operation and behavior of each user in the first environment indicated by the first past operation information and the first past behavior information as being consistent with the first rule information ( Figure 8 , Fig. 9 ) is calculated as the number of executions per unit time of each characteristic action corresponding to each characteristic group included in the first environment. The inference unit 124 calculates the number of executions per unit time of each characteristic action in the first environment. The inference unit 124 calculates the number of executions per unit time of each characteristic action in the second environment indicated by the second past operation information and the second past action information. Figure 8 , Fig. 9 ) is calculated by calculating the number of executions per unit time of each feature action corresponding to each feature group contained in the second environment.

[0234] The estimation unit 124 determines, for each characteristic action, whether the number of executions per unit time of each characteristic action in the first environment and the number of executions per unit time of each characteristic action in the second environment are consistent within a given range. The estimation unit 124 determines the characteristic actions that are determined to be consistent within the given range as similar characteristic actions.

[0235] (7) In the first embodiment and the above-mentioned modified examples (1) to (6), an example of estimating the second characteristic of the target user in the second environment where other users exist is described. However, for example, when the other user is a user in the target user's household, the target user tends to show the same characteristics as when alone, but when the other user is a user outside the target user's household, it is believed that the target user tends to show characteristics that are concerned about other users. In this way, it is believed that the characteristics of the target user in the second environment will be greatly different depending on the attributes of the other users existing in the second environment.

[0236] To this end, the estimation unit 124 may estimate the characteristics (third characteristics) of the target user existing in the third environment based on the operation and behavior of the machine 3 or device 5 of the target user in the environment (hereinafter, the third environment) where the other users with the respective attributes exist in the second environment, for each of the one or more attributes of other users different from the target user. Furthermore, the output unit 125 may output information indicating the characteristics of the target user existing in the third environment.

[0237] For example, based on the operation and behavior of the machine 3 or device 5 of the target user in the third environment where other users with attribute "A" exist, the characteristics of the target user in the third environment may be estimated, and information indicating the characteristics may be output. In addition, based on the operation and behavior of the machine 3 or device 5 of the target user in the third environment where other users with attribute "B" exist, the characteristics of the target user in the third environment may be determined, and information indicating the characteristics may be output.

[0238] This structure can be realized, for example, as follows.

[0239] In step S205 , the estimation unit 124 further acquires third operation history information corresponding to each attribute from the second operation history information for each of one or more attributes of other users.

[0240] Specifically, the estimating unit 124 acquires attribute information included in user information of other users different from the target user stored in the user information storage unit 132 , and extracts one or more attributes that are repeated in a given number or more of other users, for example.

[0241] The estimation unit 124 performs the following processing for each of the one or more attributes. The estimation unit 124 refers to the user information and the comparison data stored in the user information storage unit 132, and extracts the sensor information including the detection information indicating the information related to the space 40 where other users of each attribute exist from the second action history information. The estimation unit 124 obtains the sensor information as the information indicating the action of the target user in the second environment where other users of each attribute exist (hereinafter, the third action history information corresponding to each attribute).

[0242] In addition, the estimation unit 124 refers to the machine information stored in the machine information storage unit 131, and extracts operation information from the second operation history information, the operation information including the operation date and time that matches the detection date and time included in the third action history information corresponding to each attribute, and the machine ID of the machine 3 or device 5 arranged in the space 40 where the sensor 7 with the sensor ID included in the third action history information is arranged. The estimation unit 124 obtains the operation information as information indicating the operation of the machine 3 or device 5 by the target user in the second environment where other users of each attribute exist (hereinafter, the third operation history information corresponding to each attribute).

[0243] Furthermore, the inference unit 124 infers the candidate characteristics corresponding to the characteristic actions executed more than once, which are included in the third operation history information corresponding to each attribute and the third action history information, as element characteristics of the characteristics of the target user existing in the third environment where other users of the respective attributes exist (hereinafter, the third characteristics corresponding to each attribute). In addition, the inference unit 124 and step S206 ( Figure 5 ) Similarly, the strength of each element characteristic included in the third characteristic corresponding to each attribute is calculated, and information representing the third characteristic corresponding to each attribute is stored in the user information storage unit 132 as characteristic information of the target user.

[0244] In this case, the output unit 125 and step S300 ( Figure 3 ) Similarly, the information indicating the third characteristic corresponding to each attribute is output. Specifically, the output unit 125 uses the communication circuit 11 to send (output) the information indicating the third characteristic corresponding to each attribute to a given external device such as the output device 6.

[0245] (8) In the first embodiment and the above-mentioned modifications (1) to (7), an example of setting the strength of each element characteristic included in the first characteristic and the second characteristic is described. However, in step S206 ( Figure 5 ), the estimating unit 124 may not set the strength of each element characteristic, and may not include the strength of each element characteristic in the first characteristic information and the second characteristic information.

[0246] (Second embodiment)

[0247] The second embodiment of the present disclosure is described below. The second embodiment is a method of determining a service provided to each user based on characteristic information indicating characteristics of each user according to the environment in which each user exists, which is generated in the first embodiment or its modified example, and executing the service.

[0248] Specifically, in the second embodiment, the processor 12 further functions as a determination unit 126 and an execution unit 127 ( Figure 2 ) to function. When the user is in the first environment, the decision unit 126 determines the service provided to the user based on the first characteristic information of the user. When the user is in the second environment, the decision unit 126 determines the service provided to the user based on the second characteristic information of the user. The execution unit 127 executes the service determined by the decision unit 126.

[0249] (Process flow of service provision)

[0250] The following describes the flow of the service provision process performed by the information processing device 1 of the second embodiment. The service provision process is a process of providing a service suitable for the characteristics of a user in an environment where the user exists, to a user registered in advance as a subject of the service provision process. Fig.13 1 is a flowchart showing an example of a service provision process. The service provision process is executed at a given timing. The so-called given timing is, for example, every hour, every half day, every day, every week, or every month. Hereinafter, as in the first embodiment, the user who is the object of the service provision process is recorded as the object user.

[0251] First, in step S400 , the determination unit 126 acquires characteristic information of the target user corresponding to the environment in which the target user currently exists.

[0252] Specifically, the decision unit 126 refers to the comparison data including the user ID of the target user stored in the user information storage unit 132, and obtains the sensor information including the detection information related to the space 40 where the target user exists from the sensor information storage unit 134. The decision unit 126 obtains the sensor information including the most recent detection date and time among the obtained sensor information as information (hereinafter, current environment information) indicating the behavior of the target user in the space 40 (hereinafter, fourth environment) where the target user currently exists. That is, the current environment information indicates an example of the third information of the present disclosure.

[0253] The determination unit 126 determines whether the fourth environment is the first environment or the second environment based on the current environment information.

[0254] Specifically, the determination unit 126 compares the inspection information included in the current environment information with the comparison data stored in the user information storage unit 132 to identify one or more persons existing in the fourth environment.

[0255] When determining that only the target user exists in the fourth environment, the determination unit 126 determines that the fourth environment is the first environment and acquires the first characteristic information of the target user from the user information storage unit 132 .

[0256] On the other hand, when determining that the target user and the person identified by the matching data cannot be used exist in the fourth environment, the determination unit 126 determines that the fourth environment is the second environment and acquires the second characteristic information of the target user from the user information storage unit 132 .

[0257] When determining that the target user and a user whose user ID is different from that of the target user exist in the fourth environment, the determination unit 126 determines that the fourth environment is the second environment and acquires the second characteristic information of the target user from the user information storage unit 132 .

[0258] In addition, as shown in the above-mentioned modification (7), it is assumed that the third characteristic information corresponding to each attribute is stored in the user information storage unit 132. In this case, the determination unit 126 may also refer to the attribute information contained in the user information of the user whose user ID is different from that of the target user and stored in the user information storage unit 132. Furthermore, the determination unit 126 may also determine whether the third characteristic information corresponding to the attribute indicated by the attribute information is stored in the user information storage unit 132. Furthermore, when it is determined that the third characteristic information is stored, the determination unit 126 may determine that the fourth environment is the third environment of another user having the attribute, and obtain the third characteristic information from the user information storage unit 132.

[0259] Hereinafter, the determination unit 126 collectively refers to the first characteristic information, the second characteristic information, or the third characteristic information acquired in step S400 as target characteristic information, and also records the characteristics of the target user indicated by the target characteristic information as target characteristics.

[0260] Next, in step S500 , the determination unit 126 determines the service to be provided to the target user based on the target characteristic information acquired in step S400 .

[0261] Specifically, in step S500 , the decision unit 126 acquires, from the rule information storage unit 135 , second rule information defining a relationship between one or more element characteristic groups indicating one or more element characteristics and one or more provided services.

[0262] Fig.14 FIG. 1 is a diagram showing an example of the second rule information. Fig.14 In the second rule information shown, three element characteristic groups representing two element characteristics are associated with the service provided to the user including the target characteristics of each element characteristic group. Fig.14In the second rule information shown, the element characteristic group representing two element characteristics, the element characteristic "thrifty" and the element characteristic "regular", is associated with the provision of the service "prediction of the consumption period of consumables". The provision of the service "prediction of the consumption period of consumables" is a service that predicts the consumption period (lifespan) of consumables used by the machine 3 or device 5 used by the target user in the past and outputs information indicating the prediction result.

[0263] Furthermore, in the second rule information, the number of element characteristics included in the element characteristic group associated with the provided service is not limited to two, and may be one or three or more.

[0264] When the target characteristics include one or more element characteristic groups included in the second rule information, the decision unit 126 determines one or more provided services corresponding to the one or more element characteristic groups, and determines the one or more provided services as services provided to the target user.

[0265] For example, suppose that the object characteristics include three element characteristics: "thrifty", "regular", and "cleanliness-loving". In this case, the decision unit 126 will Fig.14 In the second rule information shown, the service "prediction of the consumption period of consumables" is associated with the element characteristic group indicating two element characteristics "thrifty" and "regular", and is determined as the service to be provided to the target user.

[0266] In addition, the decision unit 126 will Fig.14 In the second rule information shown, the service "TIPS for life" corresponding to the element characteristic group representing two element characteristics "thrifty" and "cleanliness-loving" is determined to be a service provided to the target user. The service "TIPS for life" is a service that outputs information associated with the space 40 where the target user exists at a frequency higher than a given frequency.

[0267] The method of determining the service in step S500 is not limited thereto. For example, as described below, the determination unit 126 may determine the service to be provided to the target user based on the strength of each element characteristic included in the target characteristic information.

[0268] Specifically, in step S500, the decision unit 126 determines, in the second rule information, one or more service offerings (first service offerings, second service offerings) that correspond to one or more element characteristic groups included in the object characteristic, as described above. The decision unit 126 determines the one or more service offerings that have been determined as candidates for services to be provided to the object user (hereinafter, service candidates).

[0269] The decision unit 126 acquires, from the rule information storage unit 135 , third rule information that associates one or more provided services, coefficients assigned to each provided service, and service areas to which each provided service belongs.

[0270] Fig.15 This is a diagram showing an example of a part of the third rule information. Fig.16 FIG. 1 is a diagram showing an example of the remaining part of the third rule information. Fig.15 In the third rule information shown, 13 service offerings are provided, and a coefficient assigned to each service offering is associated with a service field to which each service offering belongs, and further associated with a type of each service offering.

[0271] For example, in Fig.15 In the third rule information shown, the type of service "notification", the service "TODO list notification" provided, and the coefficient "0.1" assigned in the service are associated with the service field "Life (all)" to which the service is provided. The service "TODO list notification" is a service that outputs the TODO list of the target user stored in the user information storage unit 132.

[0272] exist Fig.16 In the third rule information shown, five services are provided, and a coefficient assigned to each service is associated with a service field to which each service belongs, and further associated with a type of each service provided.

[0273] For example, in Fig.16 In the third rule information shown, the type of service "out-of-home ordering", the service "take-out (meals, food)", and the coefficient "0.1" assigned to the service are associated with the service field "life (food)" to which the service belongs. The service "take-out (meals, food)" is a service that outputs information for guiding access to a WEB site for the take-out service that the target user has used in the past.

[0274] The decision unit 126 refers to the third rule information and calculates, for each of the one or more service offers included in the service candidates, the product (first product, second product) of the sum of the strengths of one or more element characteristics indicated by the element characteristic group included in the object characteristics and corresponding to each service offer and the coefficient assigned to each service offer.

[0275] For example, similar to the above specific example, it is assumed that the object characteristics include three element characteristics: "thrifty", "regular", and "cleanliness-loving". In addition, it is assumed that the strengths of these element characteristics are "0.2", "0.3", and "0.5", respectively. Therefore, it is assumed that the decision unit 126 refers to the second rule information and determines two services "consumables consumption period prediction" and "life tips" as service candidates.

[0276] In this case, the decision unit 126 calculates the product of the coefficient "0.1" assigned to the service "prediction of consumption period of consumables" as "0.05 (=0.1×0.5)" of the sum of the respective intensities of the two element characteristics "thrifty" and "regular" shown in the element characteristic group corresponding to the service "prediction of consumption period of consumables" included in the object characteristics for the service candidate, and as "0.1".

[0277] In addition, the decision unit 126 calculates the product of the coefficient "0.1" assigned to the service "TIPS suggestions for life" and the coefficient "0.07 (=0.1×0.7)" of the sum of the respective intensities of the two element characteristics "thrifty person" and "like cleanliness" shown in the element characteristic group corresponding to the service "TIPS suggestions for life" contained in the object characteristic information and the service candidate, and the service candidate, which is included in the service candidate.

[0278] Furthermore, the decision unit 126 refers to the third rule information and, for each of one or more service fields to which one or more services included in the service candidates belong, determines at least one service as a service to be provided to the target user when the sum of at least one of the above-mentioned products calculated for at least one service included in the service candidates belonging to each service field is greater than the first given value.

[0279] For example, suppose that the service candidates include three services: "Alarm notification of machine or equipment", "Consumables consumption period prediction", and "Life Tips". In addition, suppose that the products calculated for each of these services are "0.03", "0.05", and "0.07" respectively.

[0280] In this case, the decision unit 126 determines, for each of the two service areas "machines or equipment" and "life (housework)" belonging to the three service offerings "machine or equipment alarm notification", "consumables consumption period prediction", and "life TIPS suggestions" included in the service candidates, that the at least one service is a service provided to the target user, if the sum of at least one of the above products calculated for at least one service included in the service candidates belonging to each service area is greater than the first given value.

[0281] Specifically, with respect to the service field “machine or equipment”, when the sum “0.08 (=0.03+0.05)” of the two products “0.03” and “0.05” calculated for the two services “alarm notification of machine or equipment” and “prediction of consumption period of consumables” included in the service candidates belonging to the service field “machine or equipment” is greater than the first given value, the decision unit 126 determines the two services “alarm notification of machine or equipment” and “prediction of consumption period of consumables” as the services to be provided to the target user.

[0282] In addition, the decision unit 126 determines, with respect to the service field “life (housework)”, that a service “TIPS suggestions for life” is provided as the service to be provided to the target user when the sum “0.07” of a product “0.07” calculated for a service candidate providing “TIPS suggestions for life” that belongs to the service field “life (housework)” is greater than a first given value.

[0283] Next, in step S600 , the execution unit 127 executes one or more services determined as services to be provided to the target user in step S500 .

[0284] Specifically, in step S600 , the execution unit 127 acquires, from the rule information storage unit 135 , fourth rule information that associates one or more service offerings, the execution timing of each service offering, and the execution method of each service offering.

[0285] The execution timing of providing the service includes, for example, the start time of the service (for example, 12 o'clock, immediately, etc.), the time interval for repeatedly providing the service (for example, every hour), the number of times the service is repeatedly provided (for example, 3 times), etc.

[0286] The execution method of the provided service includes, for example, the output destination and output instructions of the information output by the execution of the provided service (hereinafter, the output information of the provided service). The output information of the provided service includes, for example, the user's TODO list, schedule, life data and other information inherent to the target user, the control information of the machine 3 or the device 5, and the information requesting the execution of the service provided by the service server 8.

[0287] The output destination of the output information of the service provided includes, for example, the output device 6 used by the target user, the service server 8, the machine 3 or the device 5 indicated by the output information of the service provided, etc. The output instruction of the output information of the service provided includes, for example, an instruction to display the output information of the service provided, an instruction to output voice, an instruction to store, an instruction to forward, an instruction to execute, etc.

[0288] The execution unit 127 refers to the fourth rule information, and at the execution timing of each service provided determined in step S500, executes the execution program of each service provided stored in the memory 13. The execution unit 127 refers to the fourth rule information, and outputs the output information of each service provided to the output destination specified by the execution method of each service provided, together with the output instruction specified by the execution method of each service provided. Thus, the output information of each service provided is output to the output destination according to the output instruction.

[0289] The following modifications can be adopted for the structure of the second embodiment.

[0290] (1) In the configuration of the second embodiment, when a plurality of users who are pre-registered as subjects of the service provision process exist in the same space 40 , the service provision process is executed with each of the plurality of users as a subject user.

[0291] Thus, when the decision unit 126 determines the service provided to each target user based on the second characteristic information of each target user, the determined multiple services (second services) may include multiple services "automatic control of machines or devices". The service "automatic control of machines or devices" is a service that automatically controls the machine 3 or device 5 existing in the space 40 where the user exists according to the characteristics of the user. In this case, when the execution unit 127 executes the multiple services "automatic control of machines or devices", there is a concern that the contents of the automatic control of the machine 3 or device 5 will conflict.

[0292] For example, it is assumed that user A and user B exist in the same space 40, and the service provision process is performed on the two users as target users. In this case, it is assumed that the decision unit 126 determines the service provided to user A to be the service "automatic control of a machine or device" based on the element characteristic "fear of heat" included in the second characteristic of user A. Similarly, it is assumed that the decision unit 126 determines the service provided to user B to be the service "automatic control of a machine or device" based on the element characteristic "fear of cold" included in the second characteristic of user B.

[0293] In this case, in the configuration of the second embodiment, the execution unit 127 executes the service "automatic control of machines or equipment" provided to user A, and outputs control information to set the set temperature of the air conditioner in the space 40 where user A is located to 25 degrees based on the element characteristic "fear of heat" of user A. In addition, the execution unit 127 executes the service "automatic control of machines or equipment" provided to user B, and outputs control information to set the set temperature of the air conditioner in the space 40 where user B is located to 27 degrees based on the element characteristic "fear of cold" of user B. Therefore, the set temperatures, which are parameters used in the automatic control of the air conditioners in the space 40 where users A and B are located, conflict.

[0294] To this end, as described above, when the multiple services provided determined by the decision unit 126 include multiple services of "automatic control of machines or equipment", the execution unit 127 can also average and execute the parameters used in the automatic control of the machine 3 or the device 5 based on the multiple services "automatic control of machines or equipment".

[0295] For example, in the above example, the execution unit 127 may average the set temperature, a parameter used in the automatic control of the air conditioner based on the service "automatic control of a machine or device" provided to user A and user B, to 26 degrees (= (25 degrees + 27 degrees) / 2), and execute the service.

[0296] (2) As in the variation example (1) of the second embodiment, when the multiple services determined by the determination unit 126 include multiple services of "automatic control of machines or equipment", the execution unit 127 may also obtain the priority level pre-assigned to each of the multiple users who provide the multiple services of "automatic control of machines or equipment".

[0297] Specifically, the priority pre-assigned to each user of the information processing system 100 may be stored in the user information storage unit 132. For example, the priority may be assigned as follows: the more difficult it is for a user with one or more element characteristics (e.g., laziness, wastefulness) included in the second characteristic of the user to tolerate one or more element characteristics different from the one or more element characteristics (e.g., not lazy, not wasteful), the higher the priority. However, the method of assigning priority to each user is not limited to this. The execution unit 127 may obtain from the user information storage unit 132 the priority pre-assigned to each of the multiple users who provide the above-mentioned multiple services of "automatic control of machines or equipment".

[0298] In this case, the execution unit 127 may execute the service determined as the service provided to the user with the highest priority among the plurality of users among the plurality of provided services "automatic control of equipment or devices".

[0299] (3) As in the modification (1) of the second embodiment, when the plurality of services determined by the determination unit 126 include a plurality of services of “automatic control of machines or equipment”, the execution unit 127 may execute the largest number of services among the plurality of services of “automatic control of machines or equipment”.

[0300] For example, it is assumed that the decision unit 126 determines that the service provided to user A is the provision of the service "automatic control of a machine or device" based on the element characteristic "fear of heat" included in the second characteristic of user A, and determines that the service provided to user B is the provision of the service "automatic control of a machine or device" based on the element characteristic "fear of cold" included in the second characteristic of user B. In addition, it is assumed that the decision unit 126 determines that the service provided to user C is the provision of the service "automatic control of a machine or device" based on the element characteristic "fear of heat" included in the second characteristic of user C who exists in the same environment as users A and B.

[0301] In this case, the execution unit 127 may also execute the service of outputting control information to the air conditioner in the space 40 where the user exists, setting the set temperature of the air conditioner to 25 degrees based on the service "automatic control of machines or equipment" which is the most numerous among the three services provided, that is, the element characteristic "fear of heat".

[0302] (4) Rule 2 Information ( Fig.14 ) may be associated with a priority order in advance. For example, the priority order may be associated with the following situation: the more difficult it is for a user to tolerate one or more element characteristics (e.g., lazy, wasteful) indicated by an element characteristic group to have one or more element characteristics (e.g., not lazy, not wasteful) indicated by an element characteristic group different from the element characteristic group, the lower the priority order.

[0303] Furthermore, as in the modification (1) of the second embodiment, when the plurality of services determined by the determination unit 126 include a plurality of services of “automatic control of equipment or devices”, the execution unit 127 may also obtain the second rule information ( Fig.14 ) establishes a corresponding priority for each of the one or more element characteristic groups contained in it.

[0304] Furthermore, the execution unit 127 may execute the service “automatic control of a machine or device” associated with the element characteristic group with the lowest priority among the plurality of services “automatic control of a machine or device” determined by the determination unit 126 .

[0305] For example, assume that in the second rule information of this variation, the element characteristic group representing two element characteristics, "lazy" and "wasteful", is associated with the provision of the service "automatic control of a machine or device" and the priority "1". Also, assume that the element characteristic group representing two element characteristics, "regular" and "thrifty", is associated with the provision of the service "automatic control of a machine or device" and the priority "2".

[0306] Here, as in the variation example (1) of the second embodiment, it is assumed that among the multiple service offerings determined by the determination unit 126, there is included the service "automatic control of machines or equipment" corresponding to the element characteristic group representing the two element characteristics "lazy" and "wasteful", and the service "automatic control of machines or equipment" corresponding to the element characteristic group representing the two element characteristics "regular" and "thrifty".

[0307] In this case, the execution unit 127 may also obtain the priority order corresponding to each of the one or more element characteristic groups included in the second rule information. In addition, the execution unit 127 may also execute the service "automatic control of a machine or device" corresponding to the element characteristic group "1" representing the two element characteristics "lazy" and "wasteful" which has the lowest priority among the plurality of services "automatic control of a machine or device" determined by the decision unit 126.

[0308] In addition, the present disclosure may arbitrarily combine the above-mentioned first embodiment, modifications (1) to (8) of the first embodiment, the second embodiment, and modifications (1) to (4) of the second embodiment.

[0309] Industrial Applicability

[0310] According to the present disclosure, it is useful to provide a service that is suitable for the characteristics of a user in each of the cases where other users exist in the same environment as the user and where other users do not exist.

Claims

1. An information processing method for estimating characteristics of a user in a computer, In the information processing method, acquiring first information indicating the machine operation and action of the estimated target user, acquiring second information indicating whether or not another user different from the target user exists in an environment where the target user exists, Based on the first information and the second information, extracting first action information indicating at least one of the machine operation and action of the target user in a first environment where the other user does not exist, and second action information indicating at least one of the machine operation and action of the target user in a second environment where the other user exists, inferring a first characteristic as a characteristic of the target user existing in the first environment based on the first action information, and inferring a second characteristic as a characteristic of the target user existing in the second environment based on the second action information, At least one of first characteristic information indicating the first characteristic and second characteristic information indicating the second characteristic is output.

2. The information processing method according to claim 1, wherein: In estimating the first characteristic and the second characteristic, acquiring first rule information defining a relationship between one or more candidate characteristics and one or more feature groups representing characteristics of a machine operation or action of the target user, In a case where the first action information includes a machine operation or action that exhibits one or more first feature groups included in the one or more feature groups, one or more first candidate characteristics corresponding to the one or more first feature groups are determined from the one or more candidate characteristics, and the one or more determined first candidate characteristics are inferred as the first characteristics. In a case where the second action information includes a machine operation or behavior that exhibits one or more second feature groups included in the one or more feature groups, one or more second candidate characteristics corresponding to the one or more second feature groups are determined from the one or more candidate characteristics, and the determined one or more second candidate characteristics are inferred as the second characteristics.

3. The information processing method according to claim 2, wherein: The first characteristic includes one or more first element characteristics, The second characteristic includes one or more second element characteristics, Furthermore, for each of the one or more first element characteristics, based on the first action information, the number of executions of the first characteristic action as a machine operation or action that represents a characteristic group corresponding to each first element characteristic is calculated, and the calculated number of executions of the first characteristic action is set as the strength of each first element characteristic. Furthermore, for each of the one or more second element characteristics, based on the second action information, the number of executions of the second characteristic action as a machine operation or action that represents a characteristic group corresponding to each second element characteristic is calculated, and the calculated number of executions of the second characteristic action is set as the strength of each second element characteristic. The first characteristic information includes the strength of each first element characteristic. The second characteristic information includes the strength of each second element characteristic.

4. The information processing method according to claim 3, wherein: In setting the strength of each first element characteristic and each second element characteristic, Furthermore, third action information indicating machine operations and actions of one or more other users different from the target user is obtained, Based on the third action information, a first average value, which is an average value of the number of executions of the first characteristic action at each given time based on each of the one or more other users, is calculated; based on the first action information, a first execution number, which is the number of executions of the first characteristic action at each given time, is calculated; and a result obtained by dividing the first average value by the first execution number is set as the strength of each first element characteristic. Based on the third action information, the average of the number of executions of the second characteristic action at each of the one or more other users, i.e., the second average value, is calculated; based on the second action information, the second number of executions, which is the number of executions of the second characteristic action at each of the given time, is calculated; and the result obtained by dividing the second average value by the second number of executions is set as the intensity of each second element characteristic.

5. The information processing method according to claim 3, wherein: In estimating the first characteristic and the second characteristic, When at least one of the first action information and the second action information includes a machine operation or action that exhibits a feature group corresponding to a common candidate characteristic predetermined from among the one or more candidate characteristics, i.e., a common feature action, the common candidate characteristic is inferred to be a common element characteristic included in both the first characteristic and the second characteristic, In setting the strength of each first element characteristic and each second element characteristic, Based on the first action information, the first execution number is calculated as the execution number of the common feature action, and based on the second action information, the second execution number is calculated as the execution number of the common feature action, and the sum of the first execution number and the second execution number is set as the intensity of the common element characteristic.

6. The information processing method according to claim 3, wherein: In estimating the first characteristic and the second characteristic, When at least one of the first action information and the second action information includes a machine operation or action that exhibits a feature group corresponding to a common candidate characteristic predetermined from among the one or more candidate characteristics, i.e., a common feature action, the common candidate characteristic is inferred to be a common element characteristic included in both the first characteristic and the second characteristic, In setting the strength of each first element characteristic and each second element characteristic, calculating a first time that the target user stays in the first environment based on the first action information, and calculating a second time that the target user stays in the second environment based on the second action information, Based on the first action information, a first execution count as the execution count of the common feature action is calculated, and based on the second action information, a second execution count as the execution count of the common feature action is calculated, a result obtained by dividing the sum of the first time and the second time by the product of the sum of the first execution count and the second execution count and the first time as the intensity of the common element characteristic included in the first characteristic, A result obtained by dividing the sum of the first time and the second time by the product of the sum of the first execution count and the second execution count and the second time is set as the intensity of the common element characteristic included in the second characteristic.

7. The information processing method according to claim 5, wherein: Furthermore, when there is a same element characteristic having similar strength between the first characteristic and the second characteristic, the same element characteristic is estimated as the common element characteristic.

8. The information processing method according to claim 5, wherein: Furthermore, in the case where the first information includes a machine operation or action that exhibits any one of the one or more feature groups, and the machine operation or action has a similar number of executions per unit time in each of the first environment and the second environment, that is, similar feature actions, the candidate characteristic corresponding to the feature group exhibited by the similar feature action is inferred as the common element characteristic.

9. The information processing method according to any one of claims 3 to 8, wherein: Furthermore, for each of the one or more first element characteristics, when a machine operation or action showing a characteristic group corresponding to each first element characteristic has not been performed for a first given time or longer, the intensity of each first element characteristic is reduced at a first reduction rate, Furthermore, for each of the one or more second element characteristics, when the machine operation or action showing the characteristic group corresponding to each second element characteristic has not been performed for more than a second given time, the intensity of each second element characteristic is reduced at a second reduction rate.

10. The information processing method according to any one of claims 3 to 8, wherein: Further, for each of the one or more first element characteristics, a ratio of the intensity of each first element characteristic to the sum of the intensities of the one or more first element characteristics is calculated, and the calculated ratio is set as the intensity of each first element characteristic. Furthermore, for each of the one or more second element characteristics, a ratio of the intensity of each second element characteristic to the total of the intensities of the one or more second element characteristics is calculated, and the calculated ratio is set as the intensity of each second element characteristic.

11. The information processing method according to claim 9, wherein: Further, for each of the one or more first element characteristics, a ratio of the intensity of each first element characteristic to the sum of the intensities of the one or more first element characteristics is calculated, and the calculated ratio is set as the intensity of each first element characteristic. Furthermore, for each of the one or more second element characteristics, a ratio of the intensity of each second element characteristic to the total of the intensities of the one or more second element characteristics is calculated, and the calculated ratio is set as the intensity of each second element characteristic.

12. The information processing method according to claim 2, wherein: Furthermore, for each of the one or more first element characteristics, when a machine operation or action showing a characteristic group corresponding to each first element characteristic has not been performed for more than a first given time, each first element characteristic is excluded from the first characteristics, Furthermore, for each of the one or more second element characteristics, when the machine operation or action showing the feature group corresponding to each second element characteristic has not been performed for more than a second given time, each second element characteristic is excluded from the second characteristics.

13. The information processing method according to claim 1, wherein: Further, obtaining information representing one or more attributes of the other user, Furthermore, for each of the one or more attributes, fourth action information representing the machine operation and behavior of the target user in the third environment of the other users having each attribute is extracted from the second action information, and based on the fourth action information, a third characteristic as a characteristic of the target user existing in the third environment is inferred, and third characteristic information related to the third characteristic is output.

14. The information processing method according to claim 1, wherein: In acquiring the first information, acquiring, as the first information, information indicating the machine operation and action of the target user within a first given period, In acquiring the second information, Information indicating a history of presence or absence of the other user in an environment where the target user exists during the first given period is acquired as the second information.

15. The information processing method according to claim 1, wherein: Further, third information indicating an action of the target user in an environment where the target user currently exists is obtained, Further, based on the third information, it is determined whether the fourth environment in which the target user currently exists is the first environment or the second environment, When the fourth environment is the first environment, the first characteristic information is acquired, a first service to be provided to the target user is determined based on the first characteristic information, and the first service is executed. When the fourth environment is the second environment, the second characteristic information is acquired, a second service to be provided to the target user is determined based on the second characteristic information, and the second service is executed.

16. The information processing method according to claim 15, wherein: The first characteristic includes one or more first element characteristics, The second characteristic includes one or more second element characteristics, In determining the first service and the second service, acquiring second rule information defining a relationship between one or more element characteristic groups representing one or more element characteristics and one or more provided services, When the first characteristic includes one or more first element characteristic groups included in the one or more element characteristic groups, determining one or more first service offerings corresponding to the one or more first element characteristic groups, and determining the one or more first service offerings determined as the first service. When the second characteristic includes one or more second element characteristic groups included in the one or more element characteristic groups, one or more second service offerings corresponding to the one or more second element characteristic groups are identified, and the identified one or more second service offerings are determined as the second service.

17. The information processing method according to claim 3, wherein: Further, third information indicating an action of the target user in an environment where the target user currently exists is obtained, Further, based on the third information, it is determined whether the fourth environment in which the target user currently exists is the first environment or the second environment, When the fourth environment is the first environment, the first characteristic information is acquired, a first service to be provided to the target user is determined based on the first characteristic information, and the first service is executed. When the fourth environment is the second environment, the second characteristic information is acquired, a second service to be provided to the target user is determined based on the second characteristic information, and the second service is executed.

18. The information processing method according to claim 17, wherein: In determining the first service and the second service, acquiring second rule information defining a relationship between one or more element characteristic groups representing one or more element characteristics and one or more provided services, When the first characteristic includes one or more first element characteristic groups included in the one or more element characteristic groups, one or more first provision services corresponding to the one or more first element characteristic groups are determined, and when the second characteristic includes one or more second element characteristic groups included in the one or more element characteristic groups, one or more second provision services corresponding to the one or more second element characteristic groups are determined, Acquire third rule information that associates one or more services, coefficients assigned to each service, and service fields to which each service belongs, For each of the one or more first provided services, a first product is calculated, the first product being the product of the sum of the strengths of the one or more element characteristics indicated by the element characteristic group corresponding to each first provided service included in the first characteristic and the coefficient assigned to each first provided service, For each of the one or more first service areas to which the one or more first service offerings belong among the one or more service areas, when the sum of the first products calculated for each of at least one first service offering belonging to each first service area is greater than a first given value, the at least one first service offering is determined to be the first service, For each of the one or more second provided services, a second product is calculated, the second product being the product of the sum of the strengths of the one or more element characteristics indicated by the element characteristic group corresponding to each second provided service included in the second characteristic and the coefficient assigned to each second provided service, For each of the one or more second service areas to which the one or more second service offerings belong among the one or more service areas, when the sum of the second products calculated for each of at least one second service offering belonging to each second service area is greater than the first given value, the at least one second service offering is determined to be the second service.

19. The information processing method according to any one of claims 15 to 18, wherein: When there are a plurality of users in the same environment, the second service is determined with each of the plurality of users as the target user. When the determined plurality of second services include a plurality of services for automatically controlling devices in an environment where the user exists according to characteristics of the user, parameters used for automatic control of the devices based on the plurality of services are averaged during execution of the plurality of services.

20. The information processing method according to any one of claims 15 to 18, wherein: When there are a plurality of users in the same environment, the second service is determined with each of the plurality of users as the target user. In a case where the determined multiple second services include multiple services for automatically controlling machines existing in an environment where the user exists based on characteristics of the user, a priority level pre-assigned to each of the multiple users is obtained, and during the execution of the multiple services, the service among the multiple services that is determined to be the second service provided to the user with the highest priority among the multiple users is executed.

21. The information processing method according to any one of claims 15 to 18, wherein: When there are a plurality of users in the same environment, the second service is determined with each of the plurality of users as the target user. When the determined plurality of second services include a plurality of services for automatically controlling devices existing in an environment where the user exists according to characteristics of the user, the largest number of services among the plurality of services are executed.

22. The information processing method according to claim 16 or 18, wherein: When there are a plurality of users in the same environment, the second service is determined with each of the plurality of users as the target user. When the determined multiple second services include multiple services for automatically controlling machines existing in the environment where the user exists according to the characteristics of the user, a predetermined priority order is obtained for each of the one or more element characteristic groups, and in the execution of the multiple services, the service corresponding to the element characteristic group with the lowest priority order among the multiple services is executed.

23. An information processing device for estimating characteristics of a user, The information processing device comprises: a first acquisition unit that acquires first information indicating a device operation and action of an estimated target user; a second acquisition unit that acquires second information indicating whether or not a user other than the target user exists in an environment where the target user exists; an extraction unit that extracts, based on the first information and the second information, first action information indicating at least one of the machine operation and action of the target user in a first environment where the other user does not exist, and second action information indicating at least one of the machine operation and action of the target user in a second environment where the other user exists; an estimating unit that estimates a first characteristic as a characteristic of the target user existing in the first environment based on the first action information, and estimates a second characteristic as a characteristic of the target user existing in the second environment based on the second action information; and The output unit outputs at least one of first characteristic information indicating the first characteristic and second characteristic information indicating the second characteristic.

24. A program that causes a computer to function so as to estimate characteristics of a user, The program causes the computer to function as: a first acquisition unit that acquires first information indicating a device operation and action of an estimated target user; a second acquisition unit that acquires second information indicating whether or not a user other than the target user exists in an environment where the target user exists; an extraction unit that extracts, based on the first information and the second information, first action information indicating at least one of the machine operation and action of the target user in a first environment where the other user does not exist, and second action information indicating at least one of the machine operation and action of the target user in a second environment where the other user exists; an estimating unit that estimates a first characteristic as a characteristic of the target user existing in the first environment based on the first action information, and estimates a second characteristic as a characteristic of the target user existing in the second environment based on the second action information; and The output unit outputs at least one of first characteristic information indicating the first characteristic and second characteristic information indicating the second characteristic.

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