Information processing method, information processing device, and program

Abstract

In an information processing method executed by a computer, operation information indicating at least one of a machine operation and an action of a user is acquired, a plurality of characteristics of the user are estimated on the basis of the operation information, a service provided to the user is estimated on the basis of the plurality of characteristics, and service information indicating the service is output.

Description

Technical Field

[0001] The present disclosure relates to a technique for determining a service provided to a user. Background Art

[0002] Conventionally, there is known a service for estimating the intention and hobbies of a user based on the search history or browsing history of the user on a network space such as a WEB site, and providing the user with information such as advertisements corresponding to the intention and hobbies of the user. In addition, Patent Document 1 discloses the following technology: a technology for estimating the type of personality tendency of a user based on the usage history of a machine for processing a material.

[0003] However, in the above-mentioned conventional techniques, it is not considered to determine the service provided to the user based on a plurality of characteristics of the user estimated from the user's machine operation or behavior.

[0004] Prior Art Literature

[0005] Patent Literature

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

[0007] The present disclosure is 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 that can determine services provided to a user based on a plurality of characteristics of the user estimated from the user's machine operations or actions.

[0008] -Methods used to solve the problem-

[0009] An information processing method involved in one aspect of the present disclosure is an information processing method executed by a computer. In the information processing method, action information representing at least one of a user's machine operation and actions is obtained, multiple characteristics of the user are inferred based on the action information, and based on the multiple characteristics, a service to be provided to the user is determined, and service information representing the service is output. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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.

[0011] Figure 2 This is a block diagram showing an example of the structure of an information processing device.

[0012] Figure 3 : is a flowchart showing an example of the characteristic output process.

[0013] Figure 4It is a diagram showing an example of operation information.

[0014] Figure 5 : is a flowchart showing an example of the characteristic estimation process.

[0015] Figure 6 This is a diagram showing an example of characteristic information.

[0016] Figure 7 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] 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 characteristics of an action.

[0018] Fig. 9 1 is a diagram showing an example of the relationship between the characteristics of the target user before updating and the number of executions of the characteristic actions corresponding to each characteristic.

[0019] Fig.10 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 characteristic.

[0020] Fig.11 : is a flowchart showing an example of service information output processing.

[0021] Fig.12 This is a flowchart showing an example of service determination processing.

[0022] Fig.13 This is a diagram showing an example of the second rule information.

[0023] Fig.14 This is a diagram showing an example of information in which characteristic information and second rule information are associated with each other.

[0024] Fig.15 This is a diagram showing an example of a part of the third rule information.

[0025] Fig.16 FIG. 2 is a diagram showing an example of a remaining part of the third rule information.

[0026] Fig.17 This is a diagram showing another example of information in which characteristic information and the second rule are associated with each other.

[0027] Fig.18 This is a diagram showing an example of the fourth rule information.

[0028] Fig.19This is a diagram showing an example of the fifth rule information. DETAILED DESCRIPTION

[0029] (The process of realizing this disclosure)

[0030] Conventionally, there has been known a service for estimating the intention and preference of a user based on the search history or browsing history of the user on a network space such as a WEB site, and providing the user with information such as advertisements corresponding to the intention and preference of the user. However, in this conventional technology, the intention and preference of the user is estimated based on information reflecting the intention and preference of the user actively inputted by the user, such as inputting a search keyword or clicking on a product image. Therefore, in order to adopt this conventional technology, an environment is required in which the user actively inputs information.

[0031] In contrast, Patent Document 1 discloses a technique for estimating which type of personality tendency the user's personality tendency is classified into based on the user's machine usage history, without the user actively inputting information. However, Patent Document 1 does not consider estimating a plurality of characteristic personality tendencies of the user based on the user's machine usage history.

[0032] Therefore, in the above-mentioned conventional technology, it is difficult to determine the service provided to the user based on the multiple characteristics of the user estimated from the user's machine operation or behavior. For example, suppose the user has two characteristics, "troublesome" and "clumsy". In this case, in the above-mentioned conventional technology, even if one characteristic of the user, "troublesome", can be estimated and the automatic control service of the machine corresponding to the characteristic is provided, it is difficult to provide the automatic control service of the machine corresponding to the other characteristic of the user, "clumsy".

[0033] To this end, the present inventors conducted intensive research on a technology for determining services provided to a user based on a plurality of characteristics of the user estimated from the user's machine operations or actions, and ultimately came up with the following embodiments corresponding to the present disclosure.

[0034] (1) An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer, wherein action information representing at least one of a machine operation and an action of a user is obtained, multiple characteristics of the user are estimated based on the action information, a service to be provided to the user is determined based on the multiple characteristics, and service information representing the service is output.

[0035] According to this structure, multiple characteristics of the user are estimated based on the action information representing at least one of the machine operation and action of the user. And based on the multiple characteristics, the services provided to the user are determined. Therefore, this structure can determine the services provided to the user based on the multiple characteristics of the user estimated based on the machine operation or action of the user.

[0036] (2) In the information processing method described in (1) above, it may also be that, in determining the service, a first element for determining the service is determined based on the multiple characteristics, a second element associated with the first element is determined based on the multiple characteristics, and the service is determined based on the first element and the second element.

[0037] According to this structure, a first element and a second element associated with the first element are determined based on a plurality of characteristics inferred based on action information representing at least one of the machine operation and action of the user. And, based on the first element and the second element thus determined, the service provided to the user is determined. Therefore, this structure can determine the service provided to the user based on a plurality of characteristics of the user inferred based on the machine operation or action of the user.

[0038] (3) In the information processing method described in (2) above, it is also possible that, further, information defining a relationship between a plurality of element characteristics, one or more first candidate elements, and one or more second candidate elements is obtained; in determining the first element, for each of the one or more first candidate elements, a first number representing the number of characteristics common to the content and the element characteristics among the plurality of characteristics is calculated; from among the one or more first candidate elements, a first candidate element having the largest first number is determined, and the determined first candidate element is determined as the first element; in determining the second element, for each of the one or more first candidate elements, a first number representing the number of characteristics common to the content and the element characteristics is calculated; The one or more second candidate elements, calculate a second number representing the number of characteristics common to the content and the element characteristics among the multiple characteristics, determine the second candidate element with the largest second number from among the one or more second candidate elements, and determine the determined second candidate element as the second element; in determining the service, obtain rule information defining the relationship between one or more first elements, one or more second elements and multiple candidate services, determine the candidate service corresponding to the first element and the second element from among the multiple candidate services, and determine the determined candidate service as the service.

[0039] According to this configuration, the candidate services associated with the first candidate element and the second candidate element having the largest number of element characteristics common to the content and the plurality of characteristics of the user can be determined as the services to be provided to the user.

[0040] (4) In the information processing method described in (2) above, it is also possible that, further, based on the action information, the strength of each of the plurality of characteristics is calculated, and further, information defining the relationship between the plurality of element characteristics, one or more first candidate elements, and one or more second candidate elements is obtained, and in determining the first element, the first characteristic with the greatest strength among the plurality of characteristics is determined, and from the one or more first candidate elements, a first candidate element corresponding to an element characteristic common to the content and the first characteristic is determined, and the determined first candidate element is determined as the first element, and in determining the second element In the determination, a characteristic second only to the first characteristic in intensity among the multiple characteristics is determined as the second characteristic, a second candidate element corresponding to an element characteristic common to the content and the second characteristic is determined from among the one or more second candidate elements, and the determined second candidate element is determined as the second element, and in the determination of the service, rule information defining a relationship between one or more first elements, one or more second elements, and a plurality of candidate services is obtained, and a candidate service corresponding to the first element and the second element is determined from among the plurality of candidate services, and the determined candidate service is determined as the service.

[0041] According to this configuration, the candidate services associated with the first characteristic having the highest strength and the second characteristic having the second highest strength after the first characteristic among a plurality of characteristics of the user can be determined as services to be provided to the user.

[0042] (5) In the information processing method described in (3) or (4) above, it may also be that the one or more first elements are any one of the service fields to which the multiple candidate services belong, the types of the multiple candidate services, the execution timings of the multiple candidate services, and the execution methods of the multiple candidate services, and the one or more second elements are any one of the service fields, the types, the execution timings, and the execution methods that are different from the one or more first elements.

[0043] According to this structure, any two of the service field, type, execution timing and execution method can be determined as the first element and the second element based on multiple characteristics of the user, and the candidate services corresponding to the first element and the second element can be determined as the services provided to the user.

[0044] (6) In the information processing method described in (2) above, it is also possible that, further, based on the action information, the strength of each of the plurality of characteristics is calculated, and further, information defining the relationship between one or more first characteristics, one or more automatic controls of the machines, one or more second characteristics, and one or more detailed controls included in the automatic controls of the machines is obtained, in determining the first element, from among the one or more first characteristics, a first characteristic having content common to the plurality of characteristics is determined, from among the one or more automatic controls of the machines, the automatic control of the machine corresponding to the determined first characteristic is determined, and the determined second characteristic is used as the input element. The automatic control of the machine is determined as the first element. In determining the second element, a second characteristic corresponding to the automatic control of the machine determined as the first element and having content common to the plurality of characteristics is determined from among the one or more second characteristics. A detailed control corresponding to the determined second characteristic is determined from among the one or more detailed controls, and the determined detailed control is determined as the second element. In determining the service, an automatic control service for performing the detailed control represented by the second element, which is included in the automatic control of the machine represented by the first element, is determined as the service.

[0045] According to this configuration, an automatic control service for performing detailed control corresponding to a second characteristic common to multiple characteristics of the content and the user, included in automatic control of a device corresponding to a first characteristic common to multiple characteristics of the content and the user, can be determined as a service to be provided to the user.

[0046] (7) In the information processing method described in (3) above, the users may include a first user and a second user, and when the first user and the second user are in the same environment, the action information is acquired and the multiple characteristics are estimated for the first user and the second user, respectively, and the first element and the second element are determined using a multiple set of the multiple characteristics estimated for the first user and the second user as the multiple characteristics estimated for the users, and the service is determined and the service information is output.

[0047] According to this configuration, services provided to the first user and the second user in the same environment can be determined based on the plurality of characteristics included in the multi-set of the plurality of characteristics estimated for each of the first user and the second user in the same environment.

[0048] (8) In the information processing method described in (4) above, the users may include a first user and a second user, and when the first user and the second user are in the same environment, the action information is acquired, the plurality of characteristics are estimated, the strength of each of the plurality of characteristics is calculated, the first element is determined, the second element is determined, and the service is determined for each of the first user and the second user. Furthermore, information on the relationship between one or more conflicting service groups defining two or more conflicting services representing a conflict and a plurality of avoidance methods for avoiding a conflict is acquired, the plurality of avoidance methods including a first method for selecting a conflicting service from the two or more conflicting services and a second method for merging the two or more conflicting services. When the multiple sets of services determined for each of the first user and the second user include a service group having content common to the conflicting service group, the first set is selected from the multiple sets. Extract the service group, determine whether the object avoidance method is the first method or the second method, the object avoidance method is an avoidance method corresponding to a conflicting service group having content common to the service group, and when the object avoidance method is the first method, obtain the strength of the first characteristic of the user of the destination of each service for each of the two or more services included in the service group, select one service from the two or more services based on the strength of the first characteristic obtained for each of the two or more services, and output information representing the one service as the service information, and when the object avoidance method is the second method, obtain the strength of the second characteristic of the user of the destination of each service for each of the two or more services, merge the two or more services based on the strength of the second characteristic obtained for each of the two or more services, and output information representing the merged service after the merger as the service information.

[0049] In a multi-set of services determined as services provided to a first user and a second user respectively in the same environment, two or more conflicting services are sometimes included. According to this structure, in such a case, information indicating a service that can avoid the conflict between the two or more services can be output as service information indicating the service provided to the user.

[0050] Specifically, it is assumed that the object avoidance method corresponding to the conflicting service group having content common to the service group representing the two or more services is determined as the first method. In this case, information representing one service selected based on the strength of the first characteristic of the user of the destination of each service can be output as the service information from among the two or more services.

[0051] On the other hand, assuming that the object avoidance method is determined as the second method, in this case, the two or more services can be merged based on the strength of the second characteristic of the user of the respective destination of the two or more services, and information indicating the merged service can be output as the service information.

[0052] (9) In the information processing method described in (8) above, when the object avoidance method is the first method, in selecting the one service, a service that obtains the greatest strength of the first characteristic among the two or more services is selected as the one service.

[0053] According to this configuration, when the object avoidance method is determined to be the first method, information indicating a service that has obtained the greatest strength of the first characteristic among the two or more services can be output as information indicating a service that can avoid conflict between the two or more services.

[0054] (10) In the information processing method described in (8) above, it may also be that, when the object avoidance method is the second method, in the merging of the two or more services, the difference between the intensities of any two of the second characteristics obtained for the two or more services is calculated, and when the difference is less than a given value, the parameters that cause conflict between the two or more services are averaged so that the service obtained by merging the two or more services is set as the merged service; when the difference is greater than the given value, the service that obtains the largest intensity of the second characteristic among the two or more services is set as the merged service.

[0055] According to this structure, when the object avoidance method is determined to be the second method, information representing a merged service corresponding to any two differences among the strengths of the second characteristic obtained for the two or more services can be output as information representing a service that can avoid conflict between the two or more services.

[0056] Specifically, when the difference is less than a given value, the conflicting parameters between the two or more services can be averaged so that the service obtained by merging the two or more services can be set as a merged service, and information representing the merged service can be output as information representing the one service. On the other hand, when the difference is greater than the given value, the service that obtains the maximum strength of the second characteristic among the two or more services can be set as a merged service, and information representing the merged service can be output as information representing the one service.

[0057] (11) In the information processing method described in any one of (8) to (10) above, it may also be that, when the service group is extracted, the priority assigned to each of the multiple candidate services by one or more users among the first user and the second user is further obtained, and for each of the two or more services, it is determined whether the user of the provider destination of each service has assigned the priority to a candidate service included in the multiple candidate services and having content common to each service. When it is determined that one or more services among the two or more services are assigned the priority, the service assigned the highest priority among the one or more services is determined, and the service information representing the determined service is output.

[0058] In the above configuration, a service group having content common to the conflicting service group may be extracted from a multi-set of services determined as services to be provided to the first user and the second user respectively in the same environment.

[0059] According to the present structure, in such a case, when priority is given to one or more services included in the service group by one or more users among the first user and the second user, priority is given to the intention of the one or more users and information indicating the service assigned the highest priority among the one or more services is output as information indicating a service that can avoid conflict between the two or more services.

[0060] (12) In the information processing method described in (3) or (4) above, it may also be that, further, information defining the relationship between the multiple element characteristics and one or more environments is obtained, further, environmental information representing the environment in which the user currently exists is obtained, further, one or more element characteristics corresponding to the environment represented by the environmental information in the one or more environments are obtained from the multiple element characteristics, and in determining the first element and the second element, the multiple characteristics are replaced by one or more characteristics among the multiple characteristics whose contents are common to the one or more element characteristics.

[0061] According to this structure, the element characteristics used to determine the first element and the second element can be limited to one or more element characteristics corresponding to the environment the user is currently in. As a result, the service determined based on the first element and the second element can be limited to services suitable for one or more element characteristics corresponding to the environment the user is currently in.

[0062] (13) In the information processing method described in (4) above, it may be that, in the output of the service information, request information requesting a reply to information indicating whether to utilize the service represented by the service information is output to an output device used by the user together with the service information, and when information indicating not to utilize the service is replied from the output device, the determination of the first element, the determination of the second element, the decision of the service, and the output of the service information are re-executed, and in the determination of the second characteristic in the determination of the second element during the re-execution, a characteristic whose intensity is second only to the second characteristic among the multiple characteristics is determined as the second characteristic.

[0063] According to this configuration, every time information indicating that the service indicated by the service information is not used is replied from the output device, information indicating the service determined by changing the second element can be output to the output device as the service information.

[0064] (14) In the information processing method described in (13) above, it may also be that, in the output of the service information, permission / disapproval request information for requesting a reply to information indicating whether to permit the subsequent output of the service information is further output to the output device, and during the period until information indicating that the subsequent output of the service information is not permitted is replied from the output device, in the event that information indicating that the service is not to be utilized is replied from the output device, the determination of the first element, the determination of the second element, the decision on the service, and the output of the service information are re-executed.

[0065] According to this structure, during the period until information indicating that the output of future service information is not allowed is replied from the output device, each time information indicating that the service represented by the service information is not used is replied, information indicating the service determined by changing the second element can be output to the output device as service information.

[0066] (15) In the information processing method described in (3) or (4) above, it may also be that, further, information representing multiple utilization services that the user has utilized in the past is obtained, and in the output of the service information, the number of target utilization services is further calculated, and the target utilization service is a utilization service among the multiple utilization services, the content of which is common to the candidate service that has established a correspondence with the first element determined in the rule information, and when the number of the target utilization services is greater than a given number, information suggesting to stop the use of the target utilization service is output to the output device used by the user together with the service information.

[0067] According to this configuration, it is possible to recommend to a user who has used a predetermined number or more of target use services whose contents are common to a candidate service associated with the first element to stop using the predetermined number or more of the target use services.

[0068] (16) In the information processing method described in (3) or (4) above, it may also be that, further, information representing multiple utilization services that the user has utilized in the past and the utilization frequency of each of the multiple utilization services is obtained, and in the output of the service information, the number of target utilization services is further calculated, and the target utilization services are utilization services among the multiple utilization services, and the content of the target utilization services is common to the candidate services that have established a correspondence with the first element determined in the rule information. When the number of the target utilization services is greater than a given number, together with the service information, information is output to the output device used by the user, suggesting to stop using the target utilization service with the lowest utilization frequency among the target utilization services greater than the given number, and using the service represented by the service information.

[0069] According to this structure, it is possible to recommend to users who have used a given number of object utilization services that have content common to candidate services corresponding to the first element in the past to stop using the object utilization service with the lowest utilization frequency among the given number of object utilization services and to use the service represented by the service information.

[0070] (17) Another embodiment of the present disclosure relates to an information processing device comprising: an acquisition unit for acquiring action information representing at least one of a machine operation and an action of a user; an inference unit for inferring multiple characteristics of the user based on the action information; a determination unit for determining a service to be provided to the user based on the multiple characteristics; and an output unit for outputting service information representing the service.

[0071] According to this configuration, the same operational effects as those of the information processing method described in (1) above can be obtained.

[0072] (18) In the information processing device described in (17) above, it may also be that the determination unit determines the first element for determining the service based on the multiple characteristics, the determination unit determines the second element associated with the first element based on the multiple characteristics, and the determination unit determines the service based on the first element and the second element.

[0073] According to this configuration, the same operational effects as those of the information processing method described in (2) above can be obtained.

[0074] (19) Another aspect of the present disclosure relates to a program that enables a computer to function, wherein the program enables the computer to function as: an acquisition unit that acquires action information representing at least one of a machine operation and an action of a user; an inference unit that infers multiple characteristics of the user based on the action information; a determination unit that determines a service to be provided to the user based on the multiple characteristics; and an output unit that outputs service information representing the service.

[0075] According to this configuration, the same operational effects as those of the information processing method described in (1) above can be obtained.

[0076] (20) In the program described in (19) above, it may also be that the determination unit determines the first element for determining the service based on the multiple characteristics, the determination unit determines the second element associated with the first element based on the multiple characteristics, and the determination unit determines the service based on the first element and the second element.

[0077] According to this configuration, the same operational effects as those of the information processing method described in (2) above can be obtained.

[0078] The present disclosure can also be realized as a system operated by such a program. In addition, it is needless to say that such a computer program can be distributed via a computer-readable non-transitory recording medium such as a CD-ROM or a communication network such as the Internet.

[0079] 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, it is also possible to combine the various contents in all embodiments.

[0080] (Implementation Method)

[0081] Hereinafter, embodiments 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] Alternatively, the facility 4 may be a company. When the facility 4 is a company, the space 40 may be, for example, an office, a conference room, a tea 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 company may be regarded as one space 40.

[0086] 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.

[0087] In addition, information processing devices such as computers, smartphones, and tablet terminals are not included in the machine 3 and the device 5. The operation of the machine 3 and the device 5 does not include the operation in which the user actively inputs information reflecting his or her intentions and preferences, such as inputting search keywords and clicking on product images.

[0088] 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 .

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The display is, for example, a display device provided in a television or a personal computer arranged 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 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.

[0095] The speaker is, for example, a smart speaker disposed in the facility 4. The speaker is not limited thereto, and may be a speaker provided in a portable terminal such as a smartphone or a tablet terminal that can be moved outside the facility 4, or a speaker provided in the machine 3 and the device 5. The speaker outputs a voice indicating information instructed by the information processing device 1.

[0096] 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 smartphone or a tablet terminal that can be moved outside the facility 4. Furthermore, the controller is wirelessly connected to the machine 3, the device 5, and the sensor 7 via the network 9 or without 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.

[0097] The information processing device 1 is constituted by a cloud server and 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.

[0098] The service server 8 is composed of a cloud server and a personal computer, etc. The service server 8 executes a service requested from the information processing device 1 via the network 9. The services executed by the service server 8 include a service for sending information instructed by the information processing device 1 to a database not shown and / or an external service server, a service for obtaining information instructed by the information processing device 1 from a database not shown and / or an external service server and responding, etc.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The processor 12 functions as an acquisition unit 121, an estimation unit 122, a determination unit 123, and an output unit 124. The acquisition unit 121 to the output unit 124 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.

[0103] 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.

[0104] 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.

[0105] The user information storage unit 132 stores information (hereinafter referred to as 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 referred to as attribute information), and information indicating the characteristics of the user (hereinafter referred to as characteristic information).

[0106] The attribute information includes, for example, the user's address, age, gender, group, role in the group, etc. In addition, the group includes, for example, family, company department, etc. The role includes, for example, father, mother, son, daughter, manager, section chief, etc.

[0107] 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.

[0108] The user information storage unit 132 also 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 present in the space 40 where the sensor 7 is arranged, and identifying the user who has operated the machine 3 and the device 5 in the same space 40 as the sensor 7. Specifically, the comparison data includes various data indicating the characteristics of the user, such as image data indicating an image of the user's face or whole body, voice data indicating the user's voice, and shape data indicating the user's shape, and the user ID of the user.

[0109] The user information storage unit 132 also stores user-specific information such as the user's TODO list, schedule, vital sign data, subscription services under contract, favorite external services, identification information of the output device 6 used by the user, and the IP address of the output device 6 .

[0110] The operation information storage unit 133 stores operation information received by the communication circuit 11 from the equipment 3 and the device 5 via the network 9 .

[0111] The sensor information storage unit 134 stores sensor information received by the communication circuit 11 from the sensor 7 via the network 9 .

[0112] The rule information storage unit 135 stores information indicating various rules used in various processes by the processor 12. Details of the information stored in the rule information storage unit 135 will be described later.

[0113] (Flow of feature output processing)

[0114] 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.

[0115] First, in step S100 , after the last characteristic output process is completed, the 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 .

[0116] Figure 4 is 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 the user ID "User A" performed an operation to turn on the machine 3 with the machine ID "Lighting 1".

[0117] 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 acquisition unit 121 refers to the sensor information containing the detection date and time that is consistent with the operation date and time contained in the operation information. The so-called consistency means consistency within a given allowable range, and the same applies to the subsequent description. The acquisition unit 121 determines the user who has operated the machine 3 or the device 5 in the space 40 represented by the detection information by comparing the detection information contained in the sensor information and the comparison data stored in the user information storage unit 132. The acquisition unit 121 obtains the user ID of the determined user as the user ID contained in the operation information.

[0118] Next, in step S200, the processor 12 uses the users of the one or more user IDs included in the operation information acquired in step S100 as the objects of estimation, and performs processing (hereinafter, characteristic estimation processing) of estimating the characteristics of the target users (hereinafter, target users). In the characteristic estimation processing, the characteristic information of the target users stored in the user information storage unit 132 is updated. The details of the characteristic estimation processing will be described later.

[0119] Next, in step S300, the output unit 124 outputs the characteristic information of each target user. For example, in step S300, the output unit 124 uses the communication circuit 11 to send (output) the characteristic information of each target user updated in step S200 to the output device 6 ( Figure 1 ) and other given external devices.

[0120] (Flow of characteristic estimation processing)

[0121] 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.

[0122] First, in step S201 , the acquisition unit 121 acquires operation information (action information) indicating the operation (machine operation) of the target user's machine 3 and device 5 and sensor information (action information) indicating the target user's behavior after the previous characteristic output process is completed.

[0123] Specifically, in step S201, the 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.

[0124] The 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 acquisition unit 121 acquires the sensor information that includes the sensor ID and the operation date and time included in the operation history information is consistent 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.

[0125] Next, in step S202 , the estimation unit 122 acquires the current characteristic information of the target user. Specifically, the estimation unit 122 acquires the characteristic information of the target user stored in the user information storage unit 132 .

[0126] Figure 6is a diagram showing an example of characteristic information. The characteristic information includes a plurality of characteristics and the strength of each characteristic. Figure 6 The characteristic information shown shows the following: the target user exhibits five element characteristics: "lazy", "thrifty", "cleanliness-loving", "neurotic", and "communicative", and the strengths of these characteristics are "0.1", "0.1", "0.5", "0.1", and "0.2", respectively.

[0127] In this embodiment, it is assumed that the larger the value of the intensity is, the more strongly the target user exhibits the element characteristic. The same applies to the following description. In other words, Figure 6 The characteristic information shown shows that the target user most strongly exhibits the characteristic of "liking cleanliness".

[0128] Next, in step S203 , the estimating unit 122 extracts candidate characteristics estimated as the characteristics of the target user based on the operation history information and the action history information acquired in step S201 .

[0129] Specifically, in step S203 , the inference unit 122 obtains the first rule information from the rule information storage unit 135 , which defines the relationship between one or more candidate characteristics that may become characteristics of the target user and one or more feature groups representing characteristics of the operation or action of the machine 3 or device 5 .

[0130] Figure 7 1 is a diagram showing an example of first rule information defining a relationship between one or more candidate characteristics and one or more feature groups representing characteristics of the operation of the machine 3 or the device 5. Figure 7 In 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.

[0131] For example, in Figure 7 In the first rule information shown, a correspondence is established between the candidate characteristic "laziness" and a feature group representing the characteristics of the user's operation of lighting with the candidate characteristic "laziness" as an element characteristic, namely "the frequency of operating lighting is low" and the characteristic of the user's operation of the refrigerator, namely "the refrigerator door is open for a long time".

[0132] Figure 8 1 is a diagram showing an example of first rule information defining a relationship between one or more candidate characteristics and one or more feature groups representing characteristics of an action. Figure 8 In the first rule information shown, eight candidate characteristics are associated with a feature group representing two characteristics of an action performed by a user with each candidate characteristic as an element characteristic.

[0133] For example, in Figure 8 In the first rule information shown, the candidate characteristic "laziness" is associated with a feature group indicating a characteristic "the desk is messy" and a characteristic "the frequency of folding laundry is low" of an action performed by a user having the candidate characteristic "laziness" as an element characteristic.

[0134] 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 are not necessarily clearly reflected in one feature of the user's operation of the machine 3 or device 5 or the action performed by the user.

[0135] Then, the estimating unit 122 refers to the first rule information, and extracts candidate characteristics estimated as the characteristics of the target user based on the operation history information and the action history information acquired in step S201 .

[0136] Specifically, the estimating unit 122 refers to Figure 7 The first rule information shown determines whether the operations of the machine 3 or device 5 of the target user indicated by the operation history information include operations of the machine 3 or device 5 that exhibit one or more feature groups included in the first rule information.

[0137] The operation of the machine 3 or device 5 that includes the characteristic group means that the number of executions of the operation of the machine 3 or device 5 that respectively exhibits one or more characteristics included in the characteristic group is all one or more. Information indicating the operation of the machine 3 or device 5 that exhibits each characteristic is stored in the rule information storage unit 135. The estimation unit 122 makes the above determination with reference to this information.

[0138] When determining that the operation of the machine 3 or the device 5 includes one or more feature groups, the inference unit 122 identifies one or more candidate characteristics corresponding to the one or more feature groups in the first rule information. The inference unit 122 infers the identified one or more candidate characteristics as characteristics of the target user and extracts the one or more candidate characteristics.

[0139] For example, suppose that in the operation of the machine 3 or device 5 of the target user represented by the operation history information, Figure 7The candidate characteristic "laziness" included in the first rule information shown in the figure establishes that the lighting operation including the characteristic "low frequency of lighting operation" included in the corresponding characteristic group is included at least once. In addition, it is assumed that the operation of the machine 3 or the device 5 of the target user represented by the operation history information also includes the refrigerator operation including the characteristic "refrigerator door is open for a long time" included in the characteristic group at least once. In this case, the estimation unit 122 estimates the candidate characteristic "laziness" corresponding to the characteristic group in the first rule information as the characteristic of the target user, and extracts the candidate characteristic "laziness".

[0140] In addition, the lighting operation that shows the characteristic of "low frequency of lighting operation" means, for example, an operation that turns the lighting on or off a given number of times (for example, 2 times) or less per day. 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 action history information including the detection date and time that matches the operation date and time included in the operation history information.

[0141] 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, after the user opens the refrigerator door, the ratio of the number of times a warning indicating that the door has been opened for more than a given time to the number of times the user opens the refrigerator door is greater than a given value (for example, 0.3).

[0142] Similarly, the estimation unit 122 refers to Figure 8 The first rule information shown determines whether or not the actions of the target user indicated by the action history information include actions that exhibit one or more feature groups included in the first rule information.

[0143] The number of times an action showing a feature group is performed means that the number of times an action showing one or more features included in the feature group is performed is all at least 1. Information indicating actions showing each feature is stored in the rule information storage unit 135. The estimation unit 122 refers to this information to make the above determination.

[0144] When determining that an action showing one or more feature groups is included, the inference unit 122 identifies one or more candidate characteristics associated with the one or more feature groups in the first rule information. The inference unit 122 infers the identified one or more candidate characteristics as characteristics of the target user and extracts the one or more candidate characteristics.

[0145] For example, suppose that the actions of the target user represented by the action history information show Figure 8 The candidate characteristic "laziness" included in the first rule information shown in the figure includes an action of the characteristic "the desk is messy" included in the corresponding characteristic group at least once. In addition, it is assumed that the behavior of the target user represented by the action history information also includes an action of the characteristic "the frequency of folding laundry" included in the characteristic group at least once. In this case, the estimation unit 122 estimates the candidate characteristic "laziness" associated with the characteristic group in the first rule information as a characteristic of the target user, and extracts the candidate characteristic "laziness".

[0146] 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 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.

[0147] Furthermore, the operation of extracting the candidate characteristic based on either the operation history information or the action history information acquired in step S201 by the estimating unit 122 in step S203 may be omitted.

[0148] If the candidate characteristic is not extracted in step S203 (No in step S203 ), the characteristic information acquired in step S202 is not updated, and the characteristic estimation process ends.

[0149] On the other hand, assuming that at least one candidate characteristic estimated as the characteristic of the target user is extracted in step S203 (Yes in step S203 ), in this case, in step S204 , the estimating unit 122 updates the characteristic information acquired in step S202 and ends the characteristic estimation process.

[0150] Specifically, in step S204, if the characteristic information acquired in step S202 does not include the candidate characteristic extracted in step S203, the estimation unit 122 adds the candidate characteristic to the characteristic information. Furthermore, the estimation unit 122 updates (sets) the strength of each characteristic included in the characteristic information, and updates the characteristic information of the target user stored in the user information storage unit 132 based on the updated characteristic information.

[0151] The following describes step S204 in detail. Fig. 9 1 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 characteristic. Fig. 9 Shows Figure 6 An example of the relationship between the characteristic information of the target user and the number of executions of the characteristic action corresponding to each characteristic included in the characteristic information is shown. The number of executions of the characteristic action corresponding to each characteristic is associated with the characteristic information and stored in the user information storage unit 132.

[0152] The characteristic actions corresponding to each characteristic refer to the operation or action of the machine 3 or device 5 that represents the characteristic group corresponding to the candidate characteristics common to the content and each characteristic in the first rule information. For example, the characteristic actions corresponding to the characteristic "lazy" refer to the operation or action of the machine 3 or device 5 that represents the characteristic group corresponding to the candidate characteristics common to the content and each characteristic in the first rule information. Figure 7 ) establishes two corresponding features, "the lighting operation is less frequent" and "the refrigerator door is open for a long time", corresponding to the candidate characteristic "laziness" that is common to the content and the characteristic "laziness", namely, the lighting operation and the refrigerator operation. In addition, the so-called characteristic actions corresponding to the candidate characteristic "laziness" represent the actions that are respectively manifested in the first rule information ( Figure 8 ) establishes two corresponding features "the table is messy" and "the frequency of folding laundry is low" in the candidate attribute "laziness".

[0153] The number of executions of the characteristic action corresponding to the characteristic "laziness" included in the characteristic information represents the minimum value between the number of times the lighting operation and the refrigerator operation as the characteristic action are respectively performed in the operation of the machine 3 or the device 5 of the object user represented by the operation history information, and the number of times the above-mentioned actions as the characteristic action are performed in the actions of the object user represented by the action history information.

[0154] That is, the number of executions of the characteristic action corresponding to each characteristic is the minimum number of executions of the operation and action of the machine 3 or device 5 that expresses each characteristic included in the characteristic group corresponding to the candidate characteristic common to the content and each characteristic. However, this is not limited to this, and the number of executions of the characteristic action corresponding to each characteristic may also be the average or maximum number of executions of the operation and action of the machine 3 or device 5 that expresses each characteristic included in the characteristic group corresponding to the candidate characteristic common to the content and each characteristic.

[0155] In step S204, the estimation unit 122 refers to the operation history information and the action history information to calculate the number of executions of the characteristic action corresponding to the characteristic contained in the characteristic information and having the same content as the candidate characteristic extracted in step S203. The estimation unit 122 adds the number of executions of the characteristic action corresponding to the characteristic contained in the characteristic information stored in the user information storage unit 132 to the number of executions of the characteristic action corresponding to the characteristic calculated this time.

[0156] Fig.10 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 characteristic. Fig.10 FIG. 204 shows that in step S204, the estimation unit 122 calculates the number of executions of the characteristic action corresponding to the characteristic "laziness" "1 time", and compares the number of executions "1 time" with the characteristic "laziness". Fig. 9 The result obtained by adding up the number of execution times of the characteristic actions corresponding to the characteristic “laziness” included in the characteristic information shown is “11 times”.

[0157] Furthermore, the estimating unit 122 sets the intensity of each characteristic included in the characteristic information based on the number of executions of the characteristic action corresponding to each characteristic included in the characteristic information.

[0158] Specifically, the estimation unit 122 sets the ratio of the number of executions of the characteristic actions corresponding to each characteristic included in the characteristic information to the total number of executions of the characteristic actions corresponding to each characteristic included in the characteristic information as the strength of each characteristic. Thus, the total of the strengths of each characteristic included in the characteristic information becomes 1 and is normalized.

[0159] exist Fig.10 In the example, the total number of executions of the five characteristic actions corresponding to the five characteristics is 101 times (=11 times+10 times+50 times+10 times+20 times). Therefore, the estimation unit 122 sets the ratio of the number of executions of the characteristic action corresponding to the characteristic "laziness" "11 times" to the total "101 times" as "0.109 (=11 times / 101 times)" as the intensity of the characteristic "laziness".

[0160] Similarly, the inference unit 122 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 characteristics "thrifty", "likes cleanliness", "neurotic", and "likes to communicate" to the total number of executions of the five characteristic actions corresponding to the five characteristics, "101 times", as the intensities of the characteristics "thrifty", "likes cleanliness", "neurotic", and "likes to communicate".

[0161] The method of setting the strength of each characteristic is not limited thereto. For example, the estimating unit 122 may set the number of executions of a characteristic action corresponding to each characteristic (eg, “sluggish”) (eg, “11 times”) as the strength of each element characteristic.

[0162] In addition, in step S204 , the estimating unit 122 may not set the strength of each characteristic, and the strength of each characteristic may not be included in the characteristic information.

[0163] (Flow of service information output processing)

[0164] The following describes the flow of the service information output process performed in the information processing device 1. The service information output process refers to a process of determining a service to be provided to each user based on the characteristic information of the user output in the characteristic output process and outputting information indicating the service for the user who has been pre-registered as the subject of the service information output process.

[0165] Fig.11 1 is a flowchart showing an example of service information output processing. The service information output processing is performed at a given timing. The so-called given timing refers to, for example, every hour, every half day, every day, every week or every month. Hereinafter, the user who is the object of the service information output processing will be recorded as the object user.

[0166] First, in step S400 , the acquisition unit 121 acquires characteristic information of a target user from the user information storage unit 132 .

[0167] Next, in step S500 , the processor 12 executes a process for determining a service to be provided to the target user based on the characteristic information acquired in step S400 (hereinafter referred to as a service determination process). The details of the service determination process will be described later.

[0168] Next, in step S600, the output unit 124 outputs information (hereinafter, service information) indicating the service provided to the target user determined in step S500. For example, in step S600, the output unit 124 transmits (outputs) the service information indicating the service provided to the target user determined in step S500 to the output device 6 used by the target user using the communication circuit 11.

[0169] However, the present invention is not limited thereto, and in step S600 , the output unit 124 may store (output) the service information indicating the service provided to the target user determined in step S500 as the user information of the target user in the user information storage unit 132 .

[0170] (Service decision processing flow)

[0171] Next, the service determination process in step S500 will be described in detail. Fig.12 This is a flowchart showing an example of service determination processing.

[0172] First, in step S501, the decision unit 123 determines a first element based on a plurality of characteristics included in the characteristic information acquired in step S400. The first element is information derived based on the characteristics of the target user and is used to determine a service provided to the target user.

[0173] For example, a housework proxy service can be determined as a service belonging to the service field "life (housework)". On the other hand, a target user with the characteristic "dislikes trouble" finds housework troublesome, so it can be inferred that he or she has a tendency to use services belonging to the service field "life (housework)". In this way, the service field "life (housework)" can be derived based on the target user's characteristic "dislikes trouble".

[0174] As described above, the information indicating the service domain can be derived based on the characteristics of the target user and can determine the service provided to the target user. Therefore, the information indicating the service domain can be used as the first element. To this end, the following describes an example of using the information indicating the service domain as the first element.

[0175] Specifically, in step S501, the acquisition unit 121 acquires second rule information indicating a rule for determining the first element and the second element from the rule information storage unit 135. The second element, like the first element, is information derived based on the characteristics of the target user and is information for determining a service provided to the target user, and is information that is related to the first element but is different from the first element.

[0176] For example, a takeaway service can be determined as a service belonging to the category "outsourcing" of the service field "meals". On the other hand, a target user with the characteristic "disgusting" feels that cooking is troublesome, so it can be inferred that he or she has a tendency to use the service of the category "outsourcing" belonging to the service field "meals". In this way, the service category "outsourcing" can be derived based on the target user's characteristic "disgusting" and can be associated with the service field "meals".

[0177] As described above, the information indicating the type of service can be derived based on the characteristics of the target user, and the service provided to the target user can be determined. In addition, the information indicating the type of service can be associated with the information indicating the service field as the first element. Therefore, the information indicating the type of service can be used as the second element. For this reason, the following describes an example of using the information indicating the type of service as the second element.

[0178] That is, the second rule information is information that defines multiple element characteristics that may be included in the user's characteristics, the service field (first candidate element) to which the candidate service (hereinafter, candidate service) provided to users whose content has characteristics common to each element characteristic belongs, and the relationship with the type of the candidate service (second candidate element).

[0179] The service fields to which the candidate services belong include, for example, life (general), machines or equipment, life (health care), life (housework), life (EC), life (food), etc. The types to which the candidate services belong include, for example, notification, prediction, suggestion, control, outsourcing, etc.

[0180] Fig.13 FIG. 1 is a diagram showing an example of the second rule information. Fig.13 In the second rule information shown, the element characteristic "characteristic 1", the service field "life (general)", and the category "outsourcing" are associated with each other.

[0181] The decision unit 123 determines the first characteristic with the greatest strength among the plurality of characteristics included in the characteristic information acquired in step S400. The decision unit 123 determines the service field corresponding to the element characteristic common to the content and the first characteristic from among the service fields included in the second rule information acquired by the acquisition unit 121. The decision unit 123 determines the determined service field as the first element.

[0182] Fig.14 2 is a diagram showing an example of information in which characteristic information and second rule information are associated with each other. Fig.14 The example in which the characteristic information acquired in step S400 includes four characteristics "Characteristic 1" to "Characteristic 4" and the strengths of these characteristics "0.5", "0.2", "0.2", and "0.1" is shown. Fig.14 Shows the Fig.13 The example shown is an example in which a part of the second rule information including four element characteristics “characteristic 1” to “characteristic 4” having contents common to the four characteristics “characteristic 1” to “characteristic 4” included in the characteristic information is associated with the characteristic information.

[0183] In this example, the decision unit 123 determines the characteristic "Characteristic 1" having the largest intensity of "0.5" among the four characteristics included in the characteristic information as the first characteristic. Fig.14 As shown in the shaded portion of , the decision unit 123 determines the service field “life (general)” corresponding to the element characteristic “characteristic 1” common to the content and the first characteristic from among the service fields included in the second rule information. The decision unit 123 determines the determined service field “life (general)” as the first element.

[0184] Next, in step S502 , the determination unit 123 specifies the second element associated with the first element based on the plurality of characteristics included in the characteristic information acquired in step S400 .

[0185] Specifically, in step S502, the decision unit 123 determines the characteristic whose strength is second only to the first characteristic among the multiple characteristics included in the characteristic information acquired in step S400 as the second characteristic. The decision unit 123 determines the type of candidate service corresponding to the element characteristic common to the content and the second characteristic from the types of candidate services included in the second rule information acquired by the acquisition unit 121. The decision unit 123 determines the determined type of the candidate service as the second element associated with the first element determined in step S501.

[0186] For example, in the above example, the determination unit 123 sets the characteristic information ( Fig.14 ) Among the four characteristics included in the , the two characteristics "Characteristic 2" and "Characteristic 3" whose strength is second only to the first characteristic "Characteristic 1" are determined as the second characteristics. Fig.14 As shown in the shaded portion of , the decision unit 123 determines the types of two candidate services, “outsourcing” and “notification”, corresponding to the two element characteristics “characteristic 2” and “characteristic 3” that are common to the content and the second characteristic, from the types of candidate services included in the second rule information. The decision unit 123 determines the determined types of the two services, “outsourcing” and “notification”, as the second elements associated with the service field “life (general)” determined as the first element in step S501.

[0187] Furthermore, the decision unit 123 may determine, as the second characteristic, a characteristic whose strength is different from that of the first characteristic among the plurality of characteristics included in the characteristic information acquired in step S400. Fig.14 ) Among the four characteristics included in the second characteristic, three characteristics "Characteristic 2", "Characteristic 3" and "Characteristic 4" whose intensity is different from the first characteristic "Characteristic 1" are determined as the second characteristic.

[0188] Next, in step S503 , the determination unit 123 determines the service to be provided to the target user based on the first factor determined in step S501 and the second factor determined in step S502 .

[0189] Specifically, in step S503, the decision unit 123 obtains third rule information indicating a rule for deciding a service from the rule information storage unit 135. The third rule information is information defining the relationship between a service domain (one or more first elements), a service type (one or more second elements), and a plurality of candidate services.

[0190] The candidate services include various services that can be executed in the information processing device 1. For example, the candidate services include TODO list notification, past photo notification, today's schedule notification, machine or equipment alarm notification, machine or equipment maintenance period notification, delivery schedule date and time notification, consumables consumption status notification, current health status notification, health status prediction based on current health status, consumables consumption period prediction, life tips, automatic control of machines or equipment, automatic ordering of consumables, etc.

[0191] In addition, the candidate services include various services that can be executed in the external service server 8. For example, the candidate services include take-out (meals, food, etc.), housework agency (cleaning, meals, babysitting, etc.), meal reservation (transportation, restaurants, etc.), travel reservation (transportation, accommodation facilities, accommodation plans, etc.), ticket reservation (movies, events, entertainment facilities, amusement parks, etc.).

[0192] 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 a remaining portion of the third rule information. Fig.15 In the third rule information shown, 13 candidate services, the type of each candidate service, and the service field to which each candidate service belongs are associated with each other, and further, the execution timing of each candidate service and the execution method of each candidate service are associated with each other.

[0193] The execution timing of the candidate service includes, for example, the start time of the candidate service (e.g., 12 o'clock, anytime, etc.), the time interval for repeatedly performing the candidate service (e.g., every hour), the number of times the candidate service is repeatedly performed (e.g., 3 times), etc. In addition, the so-called "anytime" means when the information required for the execution of the candidate service is obtained.

[0194] The execution method of the candidate service includes, for example, the output destination and output instructions of the information output by the execution of the candidate service (hereinafter, the output information of the candidate service). The output information of the candidate service includes, for example, information inherent to the target user such as the user's TODO list, schedule, and vital sign data, control information of the machine 3 or device 5, and information requesting the execution of the service provided by the service server 8.

[0195] The output destination of the output information of the candidate service 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 candidate service, etc. The output instruction of the output information of the candidate service includes, for example, an instruction to display the output information of the candidate service, an instruction to output the output information by voice, an instruction to store the output information, an instruction to forward the output information, an instruction to execute the output information, etc.

[0196] For example, in Fig.15 In the third rule information shown, the type "notification", the candidate service "TODO list notification" and the service field "life (general)" are corresponded, and further, the execution timing "once / day at XX o'clock" and the execution method "instructions displayed on the user's output device" are corresponded.

[0197] exist Fig.16 In the third rule information shown, five candidate services, the type of each candidate service, and the service field to which each candidate service belongs are associated with each other, and further, the execution timing of each candidate service and the execution method of each candidate service are associated with each other.

[0198] For example, in Fig.16 In the third rule information shown, the category "out-of-home ordering", the candidate service "takeout (meals, food)" and the service field "life (diet)" are matched, and further, the execution timing "anytime" and the execution method "instructions displayed on the user's output device" are matched.

[0199] The decision unit 123 determines a candidate service corresponding to the first element determined in step S501 and the second element determined in step S502 from among the plurality of candidate services included in the third rule information. The decision unit 123 determines the determined candidate service as a service to be provided to the target user.

[0200] For example, as in the above example, assume that the first element determined in step S501 is the service field "life (general)", and the second element determined in step S502 is the two service types "outsourcing" and "notification". Fig.15 as well as Fig.16 The third rule information is shown.

[0201] In this case, the decision unit 123 will Fig.16 In the third rule information shown, two candidate services, "Travel Reservation (Transportation Methods, Accommodation Facilities, Accommodation Plans, etc.)" corresponding to the service field "Life (Overall)" and the service type "Outsourcing" are established, and "Travel Reservation (Transportation Methods, Accommodation Facilities, Accommodation Plans, etc.)" are determined as the services provided to the target users.

[0202] Furthermore, the decision unit 123 will Fig.15 In the third rule information shown, four candidate services corresponding to the service field "Life (General)" and the service type "Notification" are determined as the services to be provided to the target users: "TODO List Notification", "Past Photo Notification", "Today's Schedule Notification", and "Delivery Scheduled Date and Time Notification".

[0203] Furthermore, in this case, in step S600 ( Fig.11 ), the output unit 124 outputs the character string information (text) (e.g., "TODO list notification") indicating the service determined in step S503 as service information. However, the present invention is not limited thereto, and the output unit 124 may refer to the third rule information to make the service information further include character string information indicating one or more of the type, service field, execution timing, and execution method corresponding to the candidate service having the same content as the service determined in step S503.

[0204] The following modifications can be adopted for the structure of this embodiment.

[0205] (1) In the above embodiment, it is described that in step S501 ( Fig.12 ) and step S502 ( Fig.12 ) is an example in which the first element and the second element are determined based on the strength of multiple characteristics included in the characteristic information. However, as shown below, the first element and the second element may also be determined based on multiple characteristics included in the characteristic information.

[0206] Specifically, in step S501 , the acquisition unit 121 acquires the second rule information from the rule information storage unit 135 .

[0207] The decision unit 123 calculates the number of characteristics (hereinafter referred to as the first number) that are common to the element characteristics corresponding to each service field among the plurality of characteristics included in the characteristic information for each of the one or more service fields included in the second rule information. The decision unit 123 determines the service field with the largest first number from among the one or more service fields included in the second rule information, and determines the determined service field as the first element.

[0208] Fig.17 2 is a diagram showing another example of information in which characteristic information and second rule information are associated with each other. Fig.17 An example is shown in which the characteristic information acquired in step S400 includes four characteristics, "Characteristic 1" to "Characteristic 4". Fig.17 The following example is shown: Fig.13The second rule information shown corresponds to the characteristic information, including a portion of the second rule information including four element characteristics “characteristic 1” to “characteristic 4” having contents common to the four characteristics “characteristic 1” to “characteristic 4” included in the characteristic information.

[0209] In this example, the decision unit 123 calculates the first number for each of the three service fields "Life (General)", "Life (Diet)", and "Life (Housework)" included in the second rule information. Specifically, the decision unit 123 calculates the number "2" of the characteristics "Characteristic 1" and "Characteristic 3" that are common to the element characteristics "Characteristic 1" and "Characteristic 3" corresponding to the service field "Life (General)" among the four characteristics "Characteristic 1" to "Characteristic 4" included in the characteristic information as the first number for the service field "Life (General)". Similarly, the decision unit 123 calculates the first number "1" for the service field "Life (Diet)" and the service field "Life (Housework)".

[0210] Furthermore, the decision unit 123 determines the service area "life (general)" having the largest first number "2" from among the three service areas "life (general)", "life (food)", and "life (housework)" included in the second rule information, and determines the determined service area "life (general)" as the first element.

[0211] In step S502, similarly to step S501, the decision unit 123 calculates the number of characteristics (hereinafter referred to as the second number) that have common content with the element characteristics corresponding to the type of each candidate service among the plurality of characteristics included in the characteristic information for each of the types of the candidate service included in the second rule information. The decision unit 123 determines the type of the candidate service with the largest second number from among the types of the candidate service included in the second rule information, and determines the determined type of the candidate service as the second element.

[0212] For example, in the above example, the decision unit 123 calculates the second number for each of the two candidate service types "outsourcing" and "notification" included in the second rule information. For example, the decision unit 123 calculates the second number for the candidate service type "outsourcing" as the number "3" of the characteristics "characteristic 1", "characteristic 2", and "characteristic 4" that have the same content as the element characteristics "characteristic 1", "characteristic 2", and "characteristic 4" corresponding to the candidate service type "outsourcing" among the four characteristics "characteristic 1" to "characteristic 4" included in the characteristic information. Similarly, the decision unit 123 calculates the second number "1" for the candidate service type "notification".

[0213] Then, the decision unit 123 determines the candidate service type "outsourcing" whose second number is the largest "3" from among the two candidate service types "outsourcing" and "notification" included in the second rule information, and determines the determined candidate service type "outsourcing" as the second element.

[0214] (2) In the above-mentioned embodiments and modifications, in step S501 ( Fig.12 ) and step S502 ( Fig.12 ), the second rule information ( Fig.13 ), an example of determining the first element and the second element is described. However, the second rule information is not limited to this, and can also be information that defines the relationship between any two of multiple element characteristics, the service field to which the candidate service belongs, the type of the candidate service, the execution timing of the candidate service, and the execution method of the candidate service.

[0215] (3) In the above-mentioned embodiments and modifications, in step S501 ( Fig.12 ) and step S502 ( Fig.12 ) is described as an example of using the second rule information to determine the first element and the second element. However, in step S501 ( Fig.12 ) and step S502 ( Fig.12 ), as shown below, the fourth rule information may be used to determine the first element and the second element. Accordingly, step S503 ( Fig.13 ).

[0216] Specifically, in step S501, the acquisition unit 121 acquires the fourth rule information indicating the rule for determining the first element and the second element from the rule information storage unit 135. The fourth rule information is information defining the relationship between one or more first characteristics that may be included in the user's characteristics, one or more automatic controls of the machines 3 or devices 5, one or more second characteristics that may be included in the user's characteristics, and one or more detailed controls included in the automatic controls of each machine 3 or each device 5.

[0217] Fig.18 FIG. 4 is a diagram showing an example of the fourth rule information. Fig.18In the fourth rule information shown, the first characteristic "troublesome" and the automatic control "adjusting the set temperature of the air conditioner" of the machine 3 or the device 5 are associated with the second characteristic "smart" and the detailed control "preset menu" included in the automatic control "adjusting the set temperature of the air conditioner". In addition, the detailed control "preset menu" included in the automatic control "adjusting the set temperature of the air conditioner" refers to the control of automatically adjusting the set temperature of the air conditioner according to the preset menu.

[0218] In addition, the first characteristic "troublesome" and the automatic control "adjustment of the set temperature of the air conditioner" of the device 3 or the equipment 5 are associated with the second characteristic "clumsy" and the detailed control "user customized menu" included in the automatic control "adjustment of the set temperature of the air conditioner". In addition, the detailed control "user customized menu" included in the automatic control "adjustment of the set temperature of the air conditioner" refers to the control of automatically adjusting the set temperature of the air conditioner according to the customized menu preset by the user.

[0219] In addition, Fig.18 In the fourth rule information shown, the first characteristic "fear of heat" is also associated with the automatic control "adjusting the set temperature of the air conditioner" of the machine 3 or the device 5. In addition, the first characteristic "fear of heat" and the automatic control "adjusting the set temperature of the air conditioner" of the machine 3 or the device 5 are associated with the second characteristic "thrifty" and the detailed control "thrifty mode" included in the automatic control "adjusting the set temperature of the air conditioner", and the second characteristic "wasteful" and the detailed control "powerful mode" included in the automatic control "adjusting the set temperature of the air conditioner".

[0220] Furthermore, the detailed control "saving mode" included in the automatic control "adjusting the set temperature of the air conditioner" refers to the control of automatically adjusting the set temperature of the air conditioner in the saving mode. The detailed control "powerful mode" included in the automatic control "adjusting the set temperature of the air conditioner" refers to the control of automatically adjusting the set temperature of the air conditioner in the power mode.

[0221] In addition, Fig.18In the fourth rule information shown, the first characteristic "likes cleanliness" is associated with the automatic control "sets the cleaning frequency of the cleaning robot to high frequency" of the machine 3 or device 5. In addition, the first characteristic "likes cleanliness" and the automatic control "sets the cleaning frequency of the cleaning robot to high frequency" of the machine 3 or device 5 are associated with the second characteristic "rigorous" and the detailed control "detailed log output" included in the automatic control "sets the cleaning frequency of the cleaning robot to high frequency", and the second characteristic "dislikes trouble" and the detailed control "log output closed" included in the automatic control "sets the cleaning frequency of the cleaning robot to high frequency".

[0222] Furthermore, the detailed control "detailed log output" included in the automatic control "setting the cleaning frequency of the cleaning robot to high frequency" refers to the control of the setting that sets the cleaning frequency of the cleaning robot to high frequency and outputs detailed log information related to the cleaning operation. The detailed control "log output off" included in the automatic control "setting the cleaning frequency of the cleaning robot to high frequency" refers to the control of the setting that sets the cleaning frequency of the cleaning robot to high frequency and does not output log information related to the cleaning operation.

[0223] The decision unit 123 determines the content of the one or more first characteristics included in the fourth rule information acquired by the acquisition unit 121 and the content of the first characteristics of step S400 ( Fig.11 ). The decision unit 123 determines the automatic control of the machine 3 or device 5 corresponding to the determined first characteristic from the automatic control of one or more machines 3 or devices 5 included in the fourth rule information. The decision unit 123 determines the determined automatic control of the machine 3 or device 5 as the first element.

[0224] For example, assuming that in step S400 ( Fig.11 ) contains two characteristics: "troublesome" and "skillful". In addition, it is assumed that the acquisition unit 121 acquires Fig.18 Rule 4 information is shown.

[0225] In this case, the decision unit 123 determines the first characteristic "troublesome" whose content is common to the two characteristics "troublesome" and "skillful" included in the characteristic information from among the three first characteristics "troublesome", "fear of heat", and "like cleanliness" included in the fourth rule information. Furthermore, the decision unit 123 determines the automatic control of the machine 3 or device 5 "adjusting the set temperature of the air conditioner" corresponding to the determined first characteristic "troublesome" from among the two automatic controls of the machine 3 or device 5 "adjusting the set temperature of the air conditioner" and "setting the cleaning frequency of the cleaning robot to high frequency" included in the fourth rule information. The decision unit 123 determines the determined automatic control of the machine 3 or device 5 "adjusting the set temperature of the air conditioner" as the first element.

[0226] In step S502 ( Fig.12 ), the decision unit 123 determines a second characteristic from one or more second characteristics included in the fourth rule information acquired by the acquisition unit 121, the second characteristic corresponding to the automatic control of the machine 3 or the device 5 determined by the first element in step S501, and the content is the same as that in step S400 ( Fig.11 ) are common to multiple characteristics included in the characteristic information obtained by the fourth rule information. Furthermore, the decision unit 123 determines the detailed control corresponding to the determined second characteristic from one or more detailed controls included in the automatic control of each machine 3 or each device 5 included in the fourth rule information. The decision unit 123 determines the determined detailed control as the second element.

[0227] For example, in the above example, the decision unit 123 selects the fourth rule information ( Fig.18 ), the second characteristic “skillful” is determined from the six second characteristics “clever”, “clumsy”, “thrifty”, “wasteful”, “rigorous”, and “troublesome” included in step S501, and the second characteristic “skillful” corresponds to the automatic control “adjusting the set temperature of the air conditioner” of the machine 3 or the device 5 determined as the first element in step S501, and the content is consistent with step S400 ( Fig.11 ) are common to the two characteristics "troublesome" and "skillful" included in the characteristic information obtained from the fourth rule information. Furthermore, the decision unit 123 determines the detailed control "preset menu" corresponding to the determined second characteristic "skillful" from the six detailed controls included in the automatic control of each machine 3 or each device 5 included in the fourth rule information. The decision unit 123 determines the determined detailed control "preset menu" as the second element.

[0228] In step S503 ( Fig.12 ), the decision unit 123 performs step S501 ( Fig.12 ) includes the automatic control of the machine 3 or the device 5 indicated by the first element specified in step S502 ( Fig.12 ) The automatic control service of the above-mentioned detailed control represented by the second element determined in is determined to be the service provided to the user.

[0229] In the above example, the decision unit 123 determines the automatic control service of the detailed control "preset menu" represented by the second element, which is included in the automatic control "adjusting the set temperature of the air conditioner" of the machine 3 or device 5 represented by the first element, as the service to be provided to the user.

[0230] (4) In the configurations of the above-described embodiment and the modified example, the first user and the second user who are pre-registered as the subject of the service information output process are respectively subjected to the characteristic output process as the subject users. Figure 3 ). Afterwards, when the first user and the second user exist in the same space 40, the service information output process may be performed with the first user and the second user as target users. In this case, there is a concern that the multiple sets of services determined as services provided to each target user may include two or more conflicting services.

[0231] For example, it is assumed that based on the first user's characteristic "fear of heat", the service provided to the first user is determined to be the service "automatic control of machines or equipment". The service "automatic control of machines or equipment" is a service that automatically controls the machine 3 or equipment 5 existing in the space 40 where the user exists according to the user's characteristics. Similarly, it is assumed that based on the second user's characteristic "fear of cold", the service provided to the second user is determined to be the service "automatic control of machines or equipment".

[0232] In this case, when the service "automatic control of a machine or device" provided to the first user is executed, control information for setting the set temperature of the air conditioner in the space 40 where the first user is present to "25 degrees" is output to the air conditioner based on the first user's characteristic of "being afraid of heat". On the other hand, when the service "automatic control of a machine or device" provided to the second user is executed, control information for setting the set temperature of the air conditioner in the space 40 where the second user is present to "27 degrees" is output to the air conditioner based on the second user's characteristic of "being afraid of cold". Therefore, a conflict occurs between the set temperature, which is a parameter used in the automatic control of the air conditioner in the space 40 where the first user and the second user are present.

[0233] Therefore, as described above, assuming that the first user and the second user exist in the same environment, the first user and the second user are respectively regarded as target users for service information output processing, and in step S503 ( Fig.12 ) contains two or more conflicting services. In this case, in step S600 ( Fig.11) in which the output unit 124 may output service information indicating a service for avoiding the conflict as shown below.

[0234] Specifically, in step S600 ( Fig.11 ), the output unit 124 first determines whether the first user and the second user exist in the same environment.

[0235] Specifically, the output unit 124 refers to the comparison data including the user ID of the first user stored in the user information storage unit 132, and acquires the sensor information including the detection information about the space 40 where the first user exists from the sensor information storage unit 134. The output unit 124 acquires the sensor information including the most recent detection date and time among the acquired sensor information as information indicating the action of the first user in the space 40 where the first user currently exists (hereinafter, first action information). Similarly, the output unit 124 acquires information indicating the action of the second user in the space 40 where the second user currently exists (hereinafter, second action information).

[0236] The output unit 124 refers to the machine information stored in the machine information storage unit 131, and determines whether the space 40 where the sensor 7 with the sensor ID included in the first action information is arranged is consistent with the space 40 where the sensor 7 with the sensor ID included in the second action information is arranged. If the output unit 124 determines that they are consistent, it determines that the first user and the second user exist in the same environment, and if the output unit 124 determines that they are inconsistent, it determines that the first user and the second user do not exist in the same environment.

[0237] When the output unit 124 determines that the first user and the second user are not in the same environment, the first user and the second user are respectively regarded as target users, and step S600 ( Fig.11 ).

[0238] On the other hand, when it is determined that the first user and the second user are not in the same environment, the output unit 124 acquires the fifth rule information from the rule information storage unit 135. The fifth rule information is information defining a relationship between one or more conflicting service groups representing two or more conflicting services and a plurality of avoidance methods for avoiding conflicts caused by the two or more conflicting services included in each conflicting service group.

[0239] Fig.19 FIG. 5 is a diagram showing an example of the fifth rule information. Fig.19 In the fifth rule information shown, two conflicting service groups are associated with two avoidance methods. Fig.19In the fifth rule information shown, a conflict service group indicating two conflict services "Service A" and "Service B" is associated with an avoidance method "Select any one" for avoiding a conflict caused by the two conflict services "Service A" and "Service B" included in the conflict service group. The avoidance method "Select any one" indicates selecting a conflict service from two or more conflict services included in the conflict service group. Hereinafter, the avoidance method "Select any one" will be described as the first method.

[0240] In addition, Fig.19 In the fifth rule information shown, a conflict service group representing two conflict services "Service C1" and "Service C2" is associated with an avoidance method "Merge" for avoiding a conflict caused by the two conflict services "Service C1" and "Service C2" included in the conflict service group. The avoidance method "Merge" means merging two or more conflict services included in the conflict service group. Hereinafter, the avoidance method "Merge" will be recorded as the second method.

[0241] The output unit 124 refers to the fifth rule information and determines whether the multi-sets of services determined for the first user and the second user respectively include a service group having content common to the conflicting service group. If it is determined that the service group having content common to the conflicting service group is included, the output unit 124 extracts the service group from the multi-set of services.

[0242] For example, assume that the multi-set of services determined for the first user and the second user respectively includes four services: "Service A", "Service B", "Service C", and "Service D". In addition, assume that the output unit 124 obtains Fig.19 In this case, the output unit 124 extracts the service group representing the two services "Service A" and "Service B" whose contents are common to the conflicting service group representing the two conflicting services "Service A" and "Service B" from the multiset of the service.

[0243] On the other hand, when the output unit 124 determines that the service group having the same content as the conflicting service group is not included, the first user and the second user are respectively regarded as target users, and step S600 ( Fig.11 ).

[0244] In addition, the multiple sets of services may include not only service groups with the same content as the conflicting service group, but also service groups different from the conflicting service group. In this case, the output unit 124 and the above step S600 ( Fig.11) Similarly, for each service included in the different service group (in the above example, "Service C", "Service D"), service information representing each service is output to the output device 6 used by the first user or the second user at the destination of each service.

[0245] When a service group having content common to the conflicting service group is extracted, the output unit 124 refers to the fifth rule information and determines whether the avoidance method (hereinafter, target avoidance method) corresponding to the conflicting service group having content common to the service group is the first method or the second method.

[0246] In the above example, in the fifth rule information ( Fig.19 ), a conflicting service group representing two conflicting services "Service A" and "Service B" having the same content as the service group extracted by the output unit 124 is associated with the avoidance method "Select any one" (first method). Therefore, the output unit 124 determines that the target avoidance method is the first method.

[0247] When determining that the target avoidance method is the first method, the output unit 124 acquires the strength of the first characteristic of the user who provides each service for two or more services included in the extracted service group.

[0248] In the above example, the service group extracted by the output unit 124 includes two services, "Service A" and "Service B". Therefore, the output unit 124 obtains the strength of the first characteristic of the first user or the second user who is the destination of the service "Service A", that is, the maximum strength among the strengths of the multiple characteristics of the first user or the second user. Similarly, the output unit 124 obtains the strength of the first characteristic of the first user or the second user who is the destination of the service "Service B".

[0249] Then, the output unit 124 selects one service from among the two or more services based on the strengths of the first characteristics acquired for each of the two or more services included in the extracted service group.

[0250] Specifically, the output unit 124 selects a service that has the largest strength of the first characteristic among the two or more services included in the extracted service group as the one service. The output unit 124 outputs information indicating the selected one service as service information.

[0251] For example, in the above example, it is assumed that the output unit 124 obtains "0.5" as the strength of the first characteristic for the service "Service A" and obtains "0.3" as the strength of the first characteristic for the service "Service B". In this case, the output unit 124 selects the service "Service A" that has obtained the largest strength of the first characteristic "0.5" among the two services "Service A" and "Service B" included in the extracted service group as the above one service.

[0252] In addition, the output unit 124 is not limited to selecting the service that obtains the greatest strength of the first characteristic among the two or more services included in the extracted service group. For example, it can also select the service that obtains the smallest strength of the first characteristic, etc., and select the above one service through other given methods.

[0253] Furthermore, the output unit 124 and the above-mentioned step S600 ( Fig.11 ) Similarly, information representing a selected service is output as service information.

[0254] On the other hand, when determining that the target avoidance method is the second method, the output unit 124 acquires the strength of the second characteristic of the user providing the respective services for each of the two or more services included in the extracted service group.

[0255] For example, it is assumed that the service group extracted by the output unit 124 includes two services, "Service C1" and "Service C2". Fig.19 The fifth rule information shown determines that the target avoidance method is the second method (avoidance method "merge").

[0256] In this case, the output unit 124 obtains the strength of the second characteristic of the first user or the second user at the destination of the service "Service C1", that is, the strength of the first user or the second user that is the second strongest strength among the strengths of the multiple characteristics. In addition, the output unit 124 obtains the strength of the second characteristic of the first user or the second user at the destination of the service "Service C2", that is, the strength of the first user or the second user that is the second strongest strength among the strengths of the multiple characteristics.

[0257] Then, the output unit 124 combines two or more services included in the extracted service group based on the strengths of the second characteristics acquired for the two or more services, respectively. Hereinafter, the service resulting from combining the two or more services is recorded as a combined service.

[0258] Specifically, the output unit 124 calculates the difference between the strengths of any two second characteristics among the strengths of the second characteristics obtained for the two or more services included in the extracted service group. For example, the output unit 124 calculates the difference between the maximum strength of the second characteristic and the minimum strength of the second characteristic among the strengths of the second characteristics obtained for the two or more services included in the extracted service group.

[0259] When the calculated difference is less than a given value, the output unit 124 averages the conflicting parameters between the two or more services to merge the two or more services. On the other hand, when the calculated difference is greater than a given value, the output unit 124 sets the service that obtains the largest strength of the second characteristic among the two or more services as the merged service.

[0260] For example, in the above example, it is assumed that the output unit 124 obtains "0.5" as the strength of the second characteristic for the service "service C1" and obtains "0.3" as the strength of the second characteristic for the service "service C2". In addition, it is assumed that the above given value is "0.3".

[0261] In this case, the output unit 124 calculates the difference "0.2" between the intensity "0.5" of the second characteristic acquired for the service "Service C1" and the intensity "0.3" of the second characteristic acquired for the service "Service C2". Since the calculated difference "0.2" is smaller than the given value "0.3", the output unit 124 averages the conflicting parameters between the two services "Service C1" and "Service C2" and merges the two or more services.

[0262] For example, assume that the service "Service C1" is a service for automatically setting the set temperature of the air conditioner to "25 degrees", and the service "Service C2" is a service for automatically setting the set temperature of the air conditioner to "27 degrees". In this case, the output unit 124 averages the conflicting parameter between the two services "Service C1" and "Service C2", that is, the set temperature of the air conditioner, to "26 degrees (= (25 degrees + 27 degrees) / 2)", thereby merging the two or more services. In other words, the merged service after merging the two or more services becomes a service for automatically setting the set temperature of the air conditioner to "26 degrees".

[0263] On the other hand, assume that the given value is "0.2". In this case, since the calculated difference "0.2" is greater than the given value "0.2", the output unit 124 sets the service "Service C1" that obtains the largest intensity "0.5" of the second characteristic among the two services "Service C1" and "Service C2" as the merged service.

[0264] Furthermore, the output unit 124 and the above-mentioned step S600 ( Fig.11 ) Similarly, information representing the merged service is output as service information.

[0265] In the configuration of the present modification (4), further, the memory 13 may store the third rule information ( Fig.15 , Fig.16 ) is a priority assigned to each of the multiple candidate services contained in .

[0266] Accordingly, as shown below, when the output unit 124 extracts a service group having content common to the conflicting service group, it may determine a service that avoids conflict between two or more services included in the service group based on the above-mentioned priority. Furthermore, the output unit 124 may output information indicating the determined service as service information.

[0267] Specifically, it is assumed that the output unit 124 extracts a service group having content common to the conflicting service group from the multiple sets of services determined for the first user and the second user. In this case, the output unit 124 obtains from the memory 13 the third rule information ( Fig.15 , Fig.16 ) contains a priority for each of the multiple candidate services.

[0268] The output unit 124 determines, for each of the two or more services included in the extracted service group, whether or not a user at a provider location of each service has given priority to a candidate service having content common to each service.

[0269] For example, it is assumed that a certain service included in the service group extracted by the output unit 124 is determined as a service to be provided to the first user. In other words, it is assumed that the user to whom the service is provided is the first user.

[0270] In this case, when the output unit 124 obtains from the memory 13 the priority assigned by the first user to the candidate service having content in common with the service, the output unit 124 determines that the user of the service provision destination has assigned priority to the candidate service having content in common with the service. On the other hand, when the output unit 124 does not obtain from the memory 13 the priority assigned by the first user to the candidate service having content in common with the service, the output unit 124 determines that the user of the service provision destination has not assigned priority to the candidate service having content in common with the service.

[0271] When the user of the destination of the service included in the extracted service group is the second user, the output unit 124 similarly determines whether the user of the destination of the service has given priority to the candidate service having the same content as the service.

[0272] Furthermore, it is assumed that the output unit 124 determines that, for one or more services among the two or more services included in the extracted service group, the user at the destination of each service has given priority to the candidate service whose content is common to each service. In this case, the output unit 124 determines the service that has been given the highest priority among the one or more services. Furthermore, the output unit 124 and the above-mentioned step S600 ( Fig.11 ) Similarly, information representing the determined service is output as service information.

[0273] On the other hand, it is assumed that the output unit 124 determines that none of the two or more services included in the extracted service group has been given priority by the user of the service provider destination. In this case, as described above, the output unit 124 refers to the fifth rule information ( Fig.19 ), determines whether the object avoidance method is the first method or the second method, and outputs service information representing a service for avoiding the conflict between the two or more services based on the result of the determination.

[0274] (5) In the configuration of the above-mentioned modification (1), it is assumed that the first user and the second user who are pre-registered as the subject of the service information output process are respectively subjected to the characteristic output process as the subject users ( Figure 3 ). Thereafter, when the first user and the second user exist in the same space 40, the service information output process may be performed with the first user and the second user as target users, respectively ( Fig.11 ).

[0275] However, instead of this, when the first user and the second user exist in the same space 40, step S400 ( Fig.11 ). In addition, it is sufficient to determine whether the first user and the second user exist in the same space 40 in the same manner as in the above-mentioned modification (4).

[0276] In this case, information indicating a multiple set of multiple characteristics included in the characteristic information of each of the first user and the second user acquired in step S400 may be used as characteristic information of a user existing in the space 40 where the first user and the second user exist. In addition, step S500 ( Fig.11 ) and step S600 ( Fig.11 ).

[0277] (6) In the configurations of the above-described embodiment and modified examples, the rule information storage unit 135 may further store sixth rule information defining the relationship between a plurality of element characteristics and one or more spaces 40 .

[0278] For example, in the sixth rule information, the element characteristic "fear of heat" is associated with the space ID of the space 40 where an air conditioner is frequently used by users whose content has the same characteristic as the element characteristic, "fear of heat". In addition, in the sixth rule information, the element characteristic "dislike of trouble" is associated with the space ID of the space 40 where users whose content has the same characteristic as the element characteristic, "dislike of trouble" stay for a long time.

[0279] Accordingly, as described below, the sixth rule information may be used to limit a plurality of characteristics of the target user used to determine the first element and the second element according to the space 40 (environment) in which the target user currently exists.

[0280] Specifically, in step S501 ( Fig.12 ), the acquisition unit 121 acquires not only the second rule information but also the sixth rule information from the rule information storage unit 135. In addition, the acquisition unit 121 acquires information (hereinafter referred to as environment information) indicating the space 40 where the target user currently exists.

[0281] Specifically, the acquisition unit 121 refers to the comparison data including the user ID of the target user stored in the user information storage unit 132, and acquires the sensor information including the detection information about the space 40 where the target user exists from the sensor information storage unit 134. The output unit 124 acquires the sensor information including the most recent detection date and time among the acquired sensor information as information indicating the behavior of the target user in the space 40 where the target user currently exists (hereinafter, behavior information). The acquisition unit 121 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 behavior information is arranged as the environmental information.

[0282] The acquisition unit 121 further acquires one or more element characteristics corresponding to the space 40 indicated by the environmental information based on the sixth rule information.

[0283] Thereafter, the decision unit 123 performs step S501 ( Fig.12 ), replace step S400 ( Fig.11 ), and determining the first element based on one or more characteristics among the multiple characteristics that are common to one or more element characteristics acquired by the acquisition unit 121 from the sixth rule information.

[0284] Similarly, the determination unit 123 performs the following steps in step S502 ( Fig.12 ), replace step S400 ( Fig.11 ), and determining the second element based on one or more characteristics among the multiple characteristics that are common to one or more element characteristics acquired by the acquisition unit 121 from the sixth rule information.

[0285] (7) In the above-mentioned embodiment and the configuration of the modified example, step S600 ( Fig.11 ), the output unit 124 may also output, together with the service information, request information for requesting a reply to information indicating whether to use the service indicated by the service information, to the output device 6 used by the target user. Furthermore, when information indicating that the service is not to be used is replied from the output device 6, the service determination processing ( Fig.12 ), and re-execute step S600 ( Fig.11 ).

[0286] In addition, when re-executing the service decision process ( Fig.12 ) in step S502 ( Fig.12 ), the decision unit 123 may also determine, as the second characteristic, a characteristic whose strength is second only to the second characteristic determined in the previous service decision process among the multiple characteristics included in the characteristic information. Thus, in the service decision process at the time of re-execution, the service determined as the service to be provided to the target user may be as different as possible from the service determined in the previous service decision process.

[0287] (8) In the configuration of the above-mentioned modification example (7), step S600 ( Fig.11 ), the output unit 124 may also output information (permission request information) requesting a reply to information indicating whether to allow the output of subsequent service information to the output device 6 used by the target user. Furthermore, during the period until information indicating that the output of subsequent service information is not allowed is replied from the output device 6, if information indicating that the service is not to be used is replied from the output device 6, the service decision processing ( Fig.12 ), and re-execute step S600 ( Fig.11 ). In this case, when information indicating that the output of subsequent service information is not permitted is replied from the output device 6, the service information output process may be terminated.

[0288] (9) In the configuration of the above-mentioned embodiment and the modified example, information indicating a plurality of services (hereinafter, utilization services) that the user has utilized in the past may be stored in the user information storage unit 132. Furthermore, as described below, the output unit 124 may output information suggesting that the target user stop utilizing one or more utilization services that the target user has utilized in the past to the output device 6 used by the target user.

[0289] Specifically, in step S600 ( Fig.11 ), the output unit 124 obtains information indicating a plurality of services used by the target user in the past from the user information storage unit 132. The output unit 124 refers to the third rule information ( Fig.15 , Fig.16 ), calculate the number of utilization services (hereinafter, target utilization services) having the same content as the candidate service among the plurality of utilization services, the candidate service and step S501 ( Fig.12 ) establishes a correspondence with the first element determined in ).

[0290] When the number of target utilization services is equal to or larger than a predetermined number, the output unit 124 outputs information advising the target user to stop utilization of the target utilization services together with the service information to the output device 6 used by the target user.

[0291] Alternatively, information indicating a plurality of utilization services and information indicating the utilization frequency of each of the plurality of utilization services may be associated with each other and stored in the user information storage unit 132 .

[0292] Accordingly, in step S600 ( Fig.11 ), the output unit 124 may also obtain information indicating a plurality of utilization services that the target user has utilized in the past and information indicating the utilization frequency of each of the plurality of utilization services from the user information storage unit 132. Furthermore, the output unit 124 may also calculate the number of the target utilization services in the same manner as described above.

[0293] In this case, when the number of objects utilizing services is greater than a given number, the output unit 124 may also output, together with the service information, information suggesting that use of the object utilization service with the lowest utilization frequency among the objects utilizing services greater than a given number be stopped, and information on the service represented by the utilization service information, to the output device 6 used by the object user.

[0294] (10) In the above-mentioned embodiment and the configuration of the modified example, step S500 ( Fig.11 ), the decision unit 123 may not execute Fig.12 The service decision process shown in step S400 ( Fig.11) to determine the service provided to the target user based on the multiple characteristics contained in the characteristic information of the target user obtained in ). This structure can be implemented as follows, for example.

[0295] Specifically, the rule information storage unit 135 stores seventh rule information that associates one or more element characteristic groups composed of a plurality of element characteristics with one or more candidate services. Fig.11 ), the decision unit 123 refers to the seventh rule information and determines the candidate service associated with the element characteristic group including a plurality of element characteristics as the service to be provided to the target user, wherein the plurality of element characteristics are consistent with the content of step S400 ( Fig.11 ) are common to multiple characteristics contained in the characteristic information of the target user obtained.

[0296] In addition, the present disclosure may arbitrarily combine the above-mentioned embodiment and modifications (1) to (10).

[0297] Industrial Applicability

[0298] The present disclosure is useful in providing services corresponding to a plurality of characteristics of a user estimated from the user's operations or actions on a device.

Claims

1. An information processing method is an information processing method executed by a computer. In the information processing method, acquiring action information indicating at least one of a machine operation and an action of a user, Based on the action information, multiple characteristics of the user are estimated, Based on the plurality of characteristics, determining a service to provide to the user, Service information representing the service is output.

2. The information processing method according to claim 1, wherein: In the decision of said service, determining a first element for determining the service based on the plurality of characteristics, determining a second element associated with the first element based on the plurality of characteristics, The service is determined based on the first element and the second element.

3. The information processing method according to claim 2, wherein: Furthermore, information defining a plurality of element characteristics and a relationship between one or more first candidate elements and one or more second candidate elements is obtained. In determining the first element, For each of the one or more first candidate elements, a first number indicating the number of characteristics common to the content and the element characteristics among the plurality of characteristics is calculated; determining a first candidate element having the largest first number from among the one or more first candidate elements, and determining the determined first candidate element as the first element, In determining the second element, For each of the one or more second candidate elements, a second number indicating the number of characteristics common to the content and the element characteristics among the plurality of characteristics is calculated; determining a second candidate element with the largest second number from among the one or more second candidate elements, and determining the determined second candidate element as the second element, In the decision of said service, Obtaining rule information defining a relationship between one or more first elements, one or more second elements, and a plurality of candidate services, A candidate service corresponding to the first element and the second element is identified from among the plurality of candidate services, and the identified candidate service is determined as the service.

4. The information processing method according to claim 2, wherein: Further, based on the action information, the strength of each of the plurality of characteristics is calculated, Furthermore, information defining a plurality of element characteristics and a relationship between one or more first candidate elements and one or more second candidate elements is obtained. In determining the first element, determining a first characteristic having the greatest strength among the plurality of characteristics, determining a first candidate element corresponding to an element characteristic having content in common with the first characteristic from among the one or more first candidate elements, and determining the determined first candidate element as the first element, In determining the second element, determining a characteristic second only to the first characteristic in intensity among the plurality of characteristics as a second characteristic, determining a second candidate element corresponding to an element characteristic having content common to the second characteristic from among the one or more second candidate elements, and determining the determined second candidate element as the second element, In the decision of said service, Obtaining rule information defining a relationship between one or more first elements, one or more second elements, and a plurality of candidate services, A candidate service corresponding to the first element and the second element is identified from among the plurality of candidate services, and the identified candidate service is determined as the service.

5. The information processing method according to claim 3 or 4, wherein: The one or more first elements are any one of a service field to which the plurality of candidate services belong, a type of the plurality of candidate services, an execution timing of the plurality of candidate services, and an execution method of the plurality of candidate services. The one or more second elements are any one of the service field, the type, the execution timing, and the execution method that is different from the one or more first elements.

6. The information processing method according to claim 2, wherein: Further, based on the action information, the strength of each of the plurality of characteristics is calculated, Furthermore, information defining a relationship between one or more first characteristics, one or more automatic controls of devices, one or more second characteristics, and one or more detailed controls included in the automatic controls of each device is acquired. In determining the first element, From among the one or more first characteristics, determine a first characteristic having content common to the plurality of characteristics; determining the automatic control of a device corresponding to the determined first characteristic from the automatic controls of the one or more devices, and determining the determined automatic control of the device as the first element; In determining the second element, determining, from among the one or more second characteristics, a second characteristic that corresponds to automatic control of the device determined as the first element and has content common to the plurality of characteristics; determining a detailed control corresponding to the determined second characteristic from among the one or more detailed controls, and determining the determined detailed control as the second element, In the decision of said service, An automatic control service that performs the detailed control indicated by the second element and included in the automatic control of the device indicated by the first element is determined as the service.

7. The information processing method according to claim 3, wherein: The users include a first user and a second user, When the first user and the second user exist in the same environment, acquiring the action information and estimating the plurality of characteristics for each of the first user and the second user; determining the first element and determining the second element by using a multiple set of the plurality of characteristics estimated for the first user and the second user as the plurality of characteristics estimated for the user, The service is determined and the service information is output.

8. The information processing method according to claim 4, wherein: The users include a first user and a second user, When the first user and the second user exist in the same environment, acquiring the action information, estimating the plurality of characteristics, calculating the strength of each of the plurality of characteristics, determining the first element, determining the second element, and determining the service for each of the first user and the second user, Furthermore, information defining a relationship between one or more conflicting service groups representing two or more conflicting services and a plurality of avoidance methods for avoiding the conflict is obtained. The plurality of avoidance methods include a first method of selecting a conflicting service from the two or more conflicting services, and a second method of merging the two or more conflicting services. When the multi-sets of services determined for the first user and the second user respectively include a service group having content common to a conflicting service group, extracting the service group from the multi-set, determining whether a target avoidance method is the first method or the second method, the target avoidance method being an avoidance method corresponding to a conflicting service group having content common to the service group, In the case where the object avoidance method is the first method, acquiring, for each of the two or more services included in the service group, the strength of the first characteristic of a user at a destination of each service, selecting one service from the two or more services based on the strengths of the first characteristics acquired for the two or more services, and outputting information indicating the one service as the service information, In the case where the object avoidance method is the second method, acquiring, for each of the two or more services, the strength of the second characteristic of a user at a destination of provision of each service, Based on the strengths of the second characteristic acquired for each of the two or more services, the two or more services are merged, and information indicating the merged service is output as the service information.

9. The information processing method according to claim 8, wherein: In the case where the object avoidance method is the first method, In the selection of the one service, a service that obtains the greatest strength of the first characteristic among the two or more services is selected as the one service.

10. The information processing method according to claim 8, wherein: In the case where the object avoidance method is the second method, In the merger of two or more services, calculating a difference between the strengths of any two of the second characteristics among the strengths of the second characteristics acquired for the two or more services, When the difference is less than a given value, the parameters causing conflict between the two or more services are averaged so as to set the service obtained by merging the two or more services as the merged service. When the difference is greater than or equal to the given value, a service that obtains the greatest strength of the second characteristic among the two or more services is set as the combined service.

11. The information processing method according to any one of claims 8 to 10, wherein: When the service group is extracted, further, obtaining the priority assigned by one or more of the first user and the second user to each of the plurality of candidate services, determining, for each of the two or more services, whether a user at a provider destination of each service has given the priority to a candidate service included in the plurality of candidate services and having content common to each service, When it is determined that one or more services among the two or more services are given the priority, a service given the highest priority among the one or more services is identified, and the service information indicating the identified service is output.

12. The information processing method according to claim 3 or 4, wherein: Further, obtaining information defining the relationship between the plurality of element characteristics and one or more environments, Further, obtaining environment information representing the environment in which the user currently exists, Further, one or more element characteristics corresponding to the environment represented by the environmental information among the one or more environments are obtained from the plurality of element characteristics, In determining the first element and the second element, In place of the plurality of characteristics, one or more characteristics among the plurality of characteristics having content common to the one or more element characteristics are used.

13. The information processing method according to claim 4, wherein: In the output of the service information, outputting, together with the service information, request information for requesting a reply to information indicating whether to utilize the service indicated by the service information, to an output device used by the user, When information indicating that the service is not to be used is returned from the output device, the determination of the first element, the determination of the second element, the decision of the service, and the output of the service information are re-executed; In determining the second characteristic in determining the second element during re-execution, a characteristic second in intensity to the second characteristic among the plurality of characteristics is determined as the second characteristic.

14. The information processing method according to claim 13, wherein: In the output of the service information, Further, the permission request information for requesting a reply to the information indicating whether to permit the output of the service information in the future is output to the output device, During the period until information indicating that the output of the service information is not permitted in the future is replied from the output device, in the event that information indicating that the service is not to be utilized is replied from the output device, the determination of the first element, the determination of the second element, the decision of the service, and the output of the service information are re-executed.

15. The information processing method according to claim 3 or 4, wherein: Furthermore, information indicating a plurality of utilization services that the user has utilized in the past is obtained, In the output of the service information, further, calculating the number of target utilization services, wherein the target utilization services are utilization services having content common to the candidate service associated with the first element specified in the rule information among the plurality of utilization services; When the number of the target utilization service is equal to or larger than a predetermined number, information suggesting that utilization of the target utilization service be stopped is output to an output device used by the user together with the service information.

16. The information processing method according to claim 3 or 4, wherein: Furthermore, information indicating a plurality of utilization services used by the user in the past and the utilization frequency of each of the plurality of utilization services is obtained. In the output of the service information, further, calculating the number of target utilization services, wherein the target utilization services are utilization services having content common to the candidate service associated with the first element specified in the rule information among the plurality of utilization services; When the number of objects utilizing the service is greater than a given number, information on the recommendation to stop utilizing the service with the lowest utilization frequency among the objects utilizing the service greater than the given number, and utilizing the service represented by the service information, is output to an output device used by the user together with the service information.

17. An information processing device comprising: an acquisition unit that acquires action information indicating at least one of a machine operation and an action of a user; an estimating unit that estimates a plurality of characteristics of the user based on the action information; a decision unit that decides a service to provide to the user based on the plurality of characteristics; and The output unit outputs service information indicating the service.

18. The information processing device according to claim 17, wherein: The determination unit determines a first element for determining the service based on the plurality of characteristics, The determination unit determines a second element associated with the first element based on the plurality of characteristics, The determination unit determines the service based on the first element and the second element. 19.A program that enables a computer to function. The program causes the computer to function as follows: an acquisition unit that acquires action information indicating at least one of a machine operation and an action of a user; an estimating unit that estimates a plurality of characteristics of the user based on the action information; a decision unit that decides a service to provide to the user based on the plurality of characteristics; and The output unit outputs service information indicating the service.

20. The program according to claim 19, wherein: The determination unit determines a first element for determining the service based on the plurality of characteristics, The determination unit determines a second element associated with the first element based on the plurality of characteristics, The determination unit determines the service based on the first element and the second element.

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