Patent Publication Number: US-2020279633-A1

Title: System and method for assisting target person in behavior change and habituation

Description:
CROSS-REFERENCE TO PRIOR APPLICATION 
     This application relates to and claims the benefit of priority from Japanese Patent Application number 2019-037199, filed on Mar. 1, 2019 the entire disclosure of which is incorporated herein by reference. 
     BACKGROUND 
     The present invention relates to a novel technique for assisting a target person in behavior change and habituation by using a computer. 
     In recent years, efforts to use approaches based on a theory of behavioral science, which scientifically study human behavior, to develop services have expanded to various fields, including public policies, retail business, medical care, and environment. 
     In such efforts, a target is set, and it is considered what approach is taken to achieve the target. For example, a decision-making support technique disclosed in Patent Literature 1 is among techniques for assisting in target achievement. 
     Patent Literature 1: Japanese Patent Laid-Open No. 2018-109890 
     SUMMARY 
     For an approach toward target achievement, for example, a generally used method is issuance of a persuasive message appealing to social norms, or grant of an incentive as a system of benefiting from a certain behavior. 
     Such a method has a problem that even though effects are seen in an initial period following introduction, the effects diminish after a lapse of some time period, so that continued use of the method fails. 
     Accordingly, in some fields such as health fields, a method known as the Stages of Behavior Change Model is adopted in some cases. The “Stages of Behavior Change Model” is a method in which stages of behavior change of a target person are divided into five stages of behavior change including “pre-contemplation”, “contemplation”, preparation”, “action”, and “maintenance”, and the target person, by being given step-by-step stimulations tailored to each stage of behavior change, is encouraged to make a behavior change to shift to a higher stage of behavior change and to become habituated. 
     However, it greatly depends on knowledge and know-how of a creator of a model of the stages of behavior change whether or not the model is appropriate. In fields where the “Stages of Behavior Change Model” method is not considered, knowledge or know-how to create such a model is not necessarily available. Accordingly, in such fields, measures for encouraging behavior change tend to be designed to directly aim at a final goal although step-by-step stimulations are necessary. 
     A system according to an embodiment of the present invention analyzes behavior data including various data obtained by measuring behaviors of a plurality of target persons, and based on a result of the analysis, defines a stage indicator, which is an indicator as a criterion of a plurality of stages via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages following the stage indicator. For each pair of adjacent stages among the plurality of stages, the system identifies a stage gap, which is a gap between two stages constituting a stage pair. For each stage pair, the system identifies a gap reason/shift measure that is at least one of a gap reason, which is a reason for existence of the identified stage gap, and a shift measure, which is a measure for causing a target person belonging to a lower stage to make a behavior change to shift to a higher stage, from relationship information in which a relationship between the stage gap and the gap reason/shift measure is defined. The system executes result processing, which is processing related to the gap reason/shift measure identified for each stage pair. 
     In various fields, it can be expected that assisting a target person in behavior change and habituation is technically achieved. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an outline of a first embodiment; 
         FIG. 2  shows an example of an entire configuration of a system according to the first embodiment; 
         FIG. 3  shows an example of a structure of a field pattern table; 
         FIG. 4  shows an example of a structure of a factor table; 
         FIG. 5  shows an example of a structure of a cognitive bias table; 
         FIG. 6  shows an example of a structure of a measure table; 
         FIG. 7  shows an example of a flow of entire processing performed in the first embodiment; 
         FIG. 8  shows an example of a behavior design canvas sheet screen; 
         FIG. 9  shows an example of a customer journey map screen; 
         FIG. 10  shows an example of an entire configuration of a system according to a second embodiment; 
         FIG. 11  shows an example of a flow of entire processing performed in the second embodiment; and 
         FIG. 12  shows an example of an individual report. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the description below, “interface apparatus” may include one or more interface devices. The one or more interface devices may be at least one of the following.
         One or more I/O (Input/Output) interface devices. Each I/O (Input/Output) interface device is an interface device supporting at least one of an I/O device and a remote computer for display. An I/O interface device supporting the computer for display may be a communication interface device. At least one I/O device may be any one of user interface devices, for example, an input device such as a keyboard or a pointing device and an output device such as a display device.   One or more communication interface devices. The one or more communication interface devices may be one or more communication interface devices of a same type (for example, one or more NICs (Network Interface Cards)), or may be two or more communication interface devices of different types (for example, an NIC and an HBA (Host Bus Adapter)).       

     In the description below, “memory” may be a memory different from an NVM within an NVM drive, may include one or more memory devices, and may typically be one or more primary storage devices. At least one memory device in the memory may be a volatile memory device, or may be a non-volatile memory device. 
     In the description below, “persistent storage apparatus” includes one or more persistent storage devices. Each persistent storage device is typically a non-volatile storage device (for example, a secondary storage device), more specifically, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive). 
     In the description below, “storage apparatus” may be at least the memory of the memory and the persistent storage apparatus. 
     In the description below, “processor” includes one or more processor devices. At least one processor device is typically a microprocessor device such as a CPU (Central Processing Unit), but may be another type of processor device such as a GPU (Graphics Processing Unit). At least one processor device may be a single core or may be a multi-core. At least one processor device may be a processor core. At least one processor device may be a processor device in a broad sense, such as a hardware circuit (for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit)) that executes part or a whole of processing. 
     In the description below, an expression “xxx table” is used to describe information indicating that an output is derived from an input, and such information may be data in any structure, or may be a learning model such as a neural network that produces an output corresponding to an input. Accordingly, “xxx table” can be expressed as “xxx information”. In the description below, a structure of each table is an example, and a single table may be divided into two or more tables, and all or some of two or more tables may be a single table. 
     In the description below, an expression “kkk section” is used to describe a function, and a function may be implemented by a processor executing one or more computer programs, or may be implemented by one or more hardware circuits (for example, FPGA or ASIC). When a function is implemented by a processor executing one or more programs, the function may be at least part of the processor because predetermined processing is performed while a storage apparatus and/or an interface apparatus or the like is used as appropriate. Processing that is described by using a function as a subject of a sentence may be processing performed by the processor or an apparatus including the processor. The program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (for example, a non-transient storage medium). A description of each function is an example, and a plurality of functions may be combined into a single function, and a single function may be divided into a plurality of functions. 
     In the description below, a system assisting a target person in behavior change and habituation is referred to as “assistance system”. The “assistance system” may be one or more physical computers, may be a software-defined system implemented by at least one physical computer executing predetermined software, or may be a system implemented on a cloud stack (typically, a plurality of types of computing resources including a processor and a storage apparatus). For example, when a computer includes a display device and the computer displays information on the own display device, the computer may be the assistance system. For example, when a first computer (for example, a server) transmits output information to a remote second computer (a computer for display (for example, an administrator terminal, which will be described later)) and the computer for display displays the information (when the first computer displays information on the second computer), at least the first computer of the first computer and the second computer may be the assistance system. In other words, “displaying output information” by the assistance system may be “displaying output information on a display device included in a computer”, or may be “transmitting output information to a computer for display” by a computer (in the latter case, the output information is displayed by the computer for display). 
     In the description below, when elements of a same type are described indistinguishably, a common part of reference signs is used, and when elements of a same type are described distinguishably, reference signs are used, in some cases. For example, when stages are described indistinguishably, “stage  50 ” is used, and when stages are described distinguishably, the stages are referred to as “stage  50 A”, “stage  50 B”, and the like, in some cases. 
     In the description below, “data set” is a single set of logical electronic data viewed from a program such as an application program, and may be any of, for example, records, a file, a key-value pair, a tuple, and the like. 
     Hereinafter, several embodiments will be described. 
     First Embodiment 
       FIG. 1  shows an outline of a first embodiment. 
     An assistance system  100  includes a stage definition section  110 , a gap identification section  120 , a reason/measure identification section  130 , and a processing execution section  140 . 
     The stage definition section  110  defines a stage indicator  55 , which is an indicator as a criterion of a plurality of stages  50  via which a behavior as a target of habituation is aimed at step by step, and each of the plurality of stages  50  following the stage indicator  55 , based on behavior data  150  including various data obtained by measuring behaviors of a plurality of target persons. In an example in  FIG. 1 , five stages  50 A to  50 E are defined. The number of the stages  50  may be smaller or larger than five. 
     The gap identification section  120  identifies a stage gap, which is a gap between two stages constituting a stage pair, for each pair of adjacent stages among the plurality of stages  50 . The stage gap corresponds to, for example, a gap between a characteristic  60  of a lower stage and a characteristic  60  of a higher stage. 
     The reason/measure identification section  130  identifies a gap reason/shift measure from relationship information  160 , for each stage pair. The gap reason/shift measure is at least one of a gap reason and a shift measure. The gap reason is a reason for existence of the identified stage gap and specifically, for example, at least one of an inhibitory/stimulatory factor, which will be described later, and a cognitive bias, which will be described later. The shift measure is a measure for causing a target person belonging to a lower stage in a stage pair to make a behavior change to shift to a higher stage in the stage pair. The relationship information  160  is information in which a relationship between a stage gap and a gap reason/shift measure is defined. 
     The processing execution section  140  executes result processing, which is processing related to the gap reason/shift measure identified for each stage pair. 
     According to the assistance system  100 , the stage indicator  55  and the plurality of stages  50  following the stage indicator  55  are defined (generated) based on the behavior data  150  on the plurality of target persons as an origin, and at least one of a gap reason and a shift measure identified from a stage gap  60  between each pair of stages among the plurality of stages  50  thus defined is associated with the stage pair. In other words, a data set derived from the behavior data  150  obtained by measuring behaviors of the plurality of target persons and a data set derived from the relationship information  160  constructed by a different method from the method used to obtain the behavior data  150  are matched by the functions  110 ,  120 , and  130  included in the assistance system  100 , more specifically, the plurality of stages  50  created by analyzing the behavior data  150  and the gap reasons/shift measures obtained from the relationship information  160  are associated with each other. Accordingly, in each of various fields, even when no one has knowledge or know-how conversant in the field, it is possible to technically assist any target person who is not habituated to a behavior as a target in becoming habituated to the behavior as the target in the field. 
     One of specific examples of such technical assistance is as follows. 
     The behavior data  150  includes a plurality of types of quantitative data  151  and a plurality of types of qualitative data  152 . The plurality of types of qualitative data  152  include, for example, target person survey data  152 B (see  FIG. 2 ), which is data indicating results of surveying the plurality of target persons (for example, respective records on a plurality of behaviors obtained by surveying the plurality of behaviors). 
     The stage definition section  110  classifies a plurality of quantitative data sets belonging to one or more types of the quantitative data  151  into a plurality of target person groups (clusters)  40 , each of which is a group of target persons, (performs clustering) from a viewpoint determined based on at least one type of the qualitative data  152 . In other words, as an example, the stage definition section  110  identifies a variable to be used in analysis from a qualitative viewpoint, and classifies target persons based on a result of analysis or predetermined quantitative data scores. The stage definition section  110  defines each of the plurality of stages  50  that follow the stage indicator  55  and into which the plurality of target person groups  40  are classified, based on a characteristic of each of the plurality of target person groups  40  and the above-mentioned survey answer data. According to the example in  FIG. 1 , the plurality of quantitative data sets are classified into five target person groups  40 A to  40 E, and the target person groups  40 A to  40 E are classified into the stages  50 A to  50 E, respectively. 
     As described above, since both a viewpoint of clustering and a viewpoint of the stage indicator  55  are determined based on at least one type of the qualitative data  152 , it can be anticipated that the plurality of stages  50  that are defined based on a result of clustering and the stage indicator  55  are appropriate to a higher degree in a field to which the assistance system  100  is applied. 
     Note that the number of target person groups  40  classified into one stage  50  may be larger than one. In other words, for the plurality of stages  50  as a result of classification of the plurality of target person groups  40 , the number of the stages  50  may be the same as, or larger than, the number of the target person groups  40 . 
     The stage indicator  55  is defined by the stage definition section  110 , based on one or more types of the qualitative data  152  and the target. Definition of the stage indicator  55  may be performed before clustering is started, or during clustering, or after clustering is finished (that is, after the plurality of target person groups  40  are determined). When the stage indicator  55  is defined before clustering is started, the clustering may be performed from a viewpoint following the stage indicator  55 . In other words, the plurality of stages  50  may be defined along data that can be visualized as results of behaviors as an axis (the “axis” here is the “stage indicator  55 ”). 
     Characteristics of the target person groups  40  may be characteristics from one or more viewpoints such as generation, gender, job category, role, basic personality, and sense of values to a specific matter of target persons. 
     For each stage pair, the gap reason is at least one of an inhibitory/stimulatory factor and a cognitive bias. The inhibitory/stimulatory factor is at least one of an inhibitory factor and a stimulatory factor. 
     “Inhibitory factor” is a factor that inhibits a behavior change to shift from a lower stage to a higher stage. The inhibitory factor is, for example, a factor obtained based on an answer of a target person who belongs to a lower stage and does not shift to a higher stage (and/or a target person who has never shifted to the higher stage), among answers indicated by the above-mentioned answer data. 
     “Stimulatory factor” is a factor that achieves at least one of stimulating a behavior change to shift from a lower stage to a higher stage and preventing a behavior change to shift from (leave) a higher stage to a lower stage. The stimulatory factor is, for example, a factor obtained based on at least one of an answer of a target person who shifts from a lower stage to a higher stage and belongs to the higher stage (and/or a target person who has shifted to the higher stage before) and an answer of a target person who has shifted from (left) a higher stage to a lower stage before, among the answers indicated by the above-mentioned answer data. 
     The “cognitive bias” corresponds to a higher-order concept of the inhibitory/stimulatory factor (at least one of the inhibitory factor and the stimulatory factor) and is a bias defined based on expertise in behavioral science. The cognitive bias is a concept identical or similar to, or broader than, so-called “nudge”. 
     The relationship information  160  includes a factor table  161 , which is an example of first information in which a relationship between a stage gap and an inhibitory/stimulatory factor is defined, a cognitive bias table  162 , which is an example of second information in which a relationship between an inhibitory/stimulatory factor and a cognitive bias is defined, and a measure table  163 , which is an example of third information in which a relationship between a cognitive bias and a shift measure is defined (each of the tables  161  to  163  will be described later). 
     The reason/measure identification section  130  performs (a) to (c) below, for each stage pair: 
     (a) Identifying an inhibitory/stimulatory factor  70  corresponding to a stage gap between the stage pair from the factor table  161 ;
 
(b) Identifying a cognitive bias  80  corresponding to the identified inhibitory/stimulatory factor  70  from the cognitive bias table  162 ; and
 
(c) Identifying a shift measure  90  corresponding to the identified cognitive bias  80  from the measure table  163 .
 
     According to the procedure of (a) to (c), which is an example, advantages as follows can be anticipated. Specifically, in the relationship information  160 , an inhibitory/stimulatory factor and a shift measure are associated with each other via a cognitive bias. A cognitive bias is a systematic bias in human thinking or decision, including non-rationality of humans, based on expertise in behavioral science and, in other words, can also be said to be an actual nature of an inhibitory/stimulatory factor. If the actual nature of an inhibitory/stimulatory factor is known, it can be thought that an appropriate shift measure can be easily determined. Accordingly, for each stage pair, a shift measure can be anticipated that is appropriate for a stage gap between two stages constituting the stage pair. 
     Note that the reason/measure identification section  130  may perform at least one of the following processing, instead of the above-described (a) to (c).
         For at least one stage pair, (b) and (c) may be transposed. For example, for at least one stage pair, the reason/measure identification section  130  identifies a cognitive bias for each of the stages by using a characteristic of each stage as a key and, based on the cognitive bias for each of the stages, identifies an inhibitory/stimulatory factor for the stage pair. In such a case, in the relationship information  160 , the cognitive bias table  162 , which is an example of the second information in which a relationship between an inhibitory/stimulatory factor and a cognitive bias is defined, may include information in which a relationship between an inhibitory/stimulatory factor and a combination of a cognitive bias for a lower stage and a cognitive bias for a higher stage is defined.   For at least one stage pair, the reason/measure identification section  130  may identify one of an inhibitory/stimulatory factor and a cognitive bias as a gap reason from a stage gap, and may identify a shift measure without identifying the other as a gap reason.   For at least one stage pair, the reason/measure identification section  130  may identify a shift measure from a stage gap without identifying any of an inhibitory/stimulatory factor and a cognitive bias as a gap reason. In such a case, the relationship information  160  may include information in which a relationship between a stage gap and a shift measure is defined, instead of, or in addition to, at least one of the tables  161  to  163 .       

     Hereinafter, the present embodiment will be described in detail. 
       FIG. 2  shows an example of an entire configuration of a system according to the first embodiment. 
     A client system  240 , a storage server  250 , an administrator terminal  210 , and an assistance server  200  are coupled to a communication network (for example, the Internet)  290 . At least the assistance server  200  of the storage server  250 , the administrator terminal  210 , and the assistance server  200  is a constituent component of the assistance system  100 . 
     The client system  240  includes one or more client apparatuses  241 . For the one or more client apparatuses  241 , a client terminal  241 A such as a personal computer may be adopted, or various sensor devices  241 B may be adopted. The client system  240  transmits at least one type of data obtained by measurement about a plurality of target persons to the storage server  250 . The at least one type of data, or the data processed, is at least part of the behavior data  150 . 
     The storage server  250  stores the behavior data  150 . The behavior data  150  includes activity data  151 A as an example of the quantitative data  151 , and includes profile data  152 A and target person survey data  152 B as examples of the qualitative data  152 . The activity data  151 A is quantitative data obtained by measuring various activities of the plurality of target persons and may include, for example, human body measures, sensor data, mechanical data, PC operation logs, communicational information such as e-mails and SNS (Social Networking Service), image information (still images or moving images) shot by a camera such as a security camera, history information such as a purchase histories and work histories, management information created for work management, work-time schedule information, audio data, locating information (for example, information indicating positions located by using infrared, Wi-Fi®, UWB (Ultra Wide Band)®, or the like), and like data. The activity data  151 A may include two or more types of the quantitative data  151 . The profile data  152 A includes, for each of the plurality of target persons, a data set indicating a profile (for example, age, gender, and the like) of the target person. The target person survey data  152 B includes, for each of the plurality of target persons, a data set indicating a result of a survey (for example, a plurality of behaviors) (for example, respective track records of the plurality of behaviors), such as a data set indicating a result of an questionnaire (a plurality of questions) (respective answers to the plurality of questions) or a data set indicating a result of an interview (a plurality of questions) (respective answers to the plurality of questions). 
     The administrator terminal  210  may be an information processing terminal (for example, a personal computer) functioning as a client (for example, an input-output console) of the assistance server  200 . The administrator terminal  210  is used by an administrator. “Administrator” may be an administrator of the assistance server  200 , or a manager who manages the plurality of target persons. The administrator may be a target person. 
     The assistance server  200  includes an interface apparatus  261 , a storage apparatus  262 , and a processor  263  coupled to the interface apparatus  261  and the storage apparatus  262 . The interface apparatus  261  is coupled to the communication network  290 . 
     The storage apparatus  262  stores, for example, the relationship information  160  and one or more programs (not shown) for implementing the stage definition section  110 , the gap identification section  120 , the reason/measure identification section  130 , and a display section  140 X by being executed by the processor  263 . The display section  140 X is an example of the processing execution section  140 . The relationship information  160  includes, for example, a field table  164  in addition to the factor table  161 , the cognitive bias table  162 , and the measure table  163  mentioned above. 
       FIG. 3  shows an example of a structure of the field table  164 . 
     The field table  164  retains information related to a field pattern. “Field” here corresponds to a collective term of various elements belonging to a “field” to which the assistance system  100  is applied. Examples of the elements belonging to a field include a business affair, an office, a public site, a living environment, and the like to which the assistance system  100  is applied. “Field pattern” is a pattern of such an element. 
     For example, the field table  164  includes an entry for each field pattern. For example, each entry retains information such as a field pattern ID  301 , a business type  302 , an application site  303 , a target person type  304 , and a data type list  305 . Hereinafter, one field pattern will be taken as an example (“field pattern of interest” in the description regarding  FIG. 3 ). 
     The field pattern ID  301  indicates an ID of a field pattern of interest. The information  302  to  305  is an example of information indicating constituent elements of the field pattern of interest. The business type  302  indicates a type of a business of application. The application site  303  indicates a site of application (an example of a site belonging to the business). The target person type  304  indicates a type of a target person of application (for example, a type derived from a profile, such as a job category). The data type list  305  indicates a list of types of data that can be used for objects of analysis in the behavior data  150 . 
       FIG. 4  shows an example of a structure of the factor table  161 . 
     For example, the factor table  161  includes an entry for each inhibitory/stimulatory factor. For example, each entry retains information such as a factor ID  401 , a factor type  402 , a factor content  403 , a gap element ID list  404 , and a field pattern ID list  405 . Hereinafter, one inhibitory/stimulatory factor will be taken as an example (“inhibitory/stimulatory factor of interest” in the description regarding  FIG. 4 ). 
     The factor ID  401  indicates an ID of an inhibitory/stimulatory factor of interest. The factor type  402  indicates which one of an inhibitory factor and a stimulatory factor the inhibitory/stimulatory factor of interest is. The factor content  403  indicates a content of the inhibitory/stimulatory factor of interest. The gap element ID list  404  indicates a list of IDs of gap elements applicable to the inhibitory/stimulatory factor of interest. The field pattern ID list  405  indicates a list of IDs of field patterns corresponding to the inhibitory/stimulatory factor of interest. 
     “Gap element” here is each of one or more elements included in a stage gap. For example, in each stage pair, each of two respective characteristics  60  (see  FIG. 1 ) of two stages  50  included in the stage pair may have one or more characteristic elements. Each characteristic element may be a set of a characteristic item and a characteristic value of the item. The two characteristics  60  may have one or more common characteristic items. Accordingly, for each same characteristic item, it can be obtained, as a result of comparison between the two characteristics  60 , whether or not the characteristic values are the same and, when the characteristic values are not the same, what difference lies. A stage gap may correspond to a set of characteristic elements having differences between the two characteristics  60 , and a gap element may correspond to a characteristic element having a difference. A specific example is as follows. Specifically, characteristic elements (“characteristic item: characteristic value”) that are present in each stage can be “time: ‘unpunctual’/‘punctual’”, “meal: ‘eat’/‘not eat’”, “oversleeping: ‘wake late’/‘wake on time’”, and the like, and an inhibitory/stimulatory factor can be obtained by comparing the characteristic values of each characteristic item. Note that one or some characteristic values may be a qualitative value (for example, an answer to a question). For example, two or more identical or similar answers to a same question may be regarded as the same answers. An inhibitory/stimulatory factor in the present embodiment may be a patterned factor. 
     According to the factor table  161  as described above, an inhibitory/stimulatory factor corresponding to a field pattern and a set of gap elements can be obtained. For example, for the inhibitory/stimulatory factor of interest to be identified, at least all of conditions below need to be satisfied. Conditions for identifying an inhibitory/stimulatory factor may be arbitrarily designed.
         At least one (for example, all or a majority) of one or more gap elements obtained by comparing characteristics  60  matches with all, or one or some, of gap element IDs included in the gap element ID list  404  corresponding to the inhibitory/stimulatory factor of interest.   An ID of a field pattern to which the assistance system  100  is applied matches with any one of field pattern IDs included in the field pattern ID list  405  corresponding to the inhibitory/stimulatory factor of interest.       

       FIG. 5  shows an example of a structure of the cognitive bias table  162 . 
     For example, the cognitive bias table  162  includes an entry for each cognitive bias. For example, each entry retains information such as a cognitive bias ID  501 , a cognitive bias content  502 , and a factor ID list  503 . Hereinafter, one cognitive bias will be taken as an example (“cognitive bias of interest” in the description regarding  FIG. 5 ). 
     The cognitive bias ID  501  indicates an ID of a cognitive bias of interest. The cognitive bias content  502  indicates a content of the cognitive bias of interest. The factor ID list  503  indicates a list of IDs of inhibitory/stimulatory factors belonging to the cognitive bias of interest. 
       FIG. 6  shows an example of a structure of the measure table  163 . 
     For example, the measure table  163  includes an entry for each measure pattern. For example, each entry retains information such as a measure pattern ID  601 , a measure pattern content  602 , a cognitive bias ID list  603 , and a touchpoint ID list  604 . “Measure pattern” is a pattern of a shift measure. Hereinafter, one measure pattern will be taken as an example (“measure pattern of interest” in the description regarding  FIG. 6 ). 
     The measure pattern ID  601  indicates an ID of a measure pattern of interest. The measure pattern content  602  indicates a content (for example, a label) of the measure pattern of interest. The cognitive bias ID list  603  indicates a list of IDs of cognitive biases for which it is preferable that at least one shift measure belonging to the measure pattern of interest be executed. The touchpoint ID list  604  indicates a list of IDs of touchpoints of shift measures belonging to the measure pattern of interest. In the present embodiment, “touchpoint” means a contact point between a shift measure and a target person. 
     Note that an entry corresponding to the measure pattern of interest may further retain information indicating one or more shift measures belonging to the measure pattern of interest. In such a case, the one or more shift measures belonging to the measure pattern of interest may be identified from the entry. 
     As described above, the tables shown in  FIGS. 3 to 6  are examples of information included in the relationship information  160 . With respect to the relationship information  160 , the following may be adopted instead of, or in addition to, at least part of the foregoing.
         An inhibitory/stimulatory factor (pattern) may be defined from a result of analysis of the target person survey data  152 B.   A table for identifying one or more inhibitory/stimulatory factors by using an ID of a cognitive bias as a key may be prepared.   A plurality of inhibitory/stimulatory factors may be prepared beforehand for each field pattern, and the cognitive bias table  162  may be common regardless of field pattern.       

       FIG. 7  shows an example of a flow of entire processing performed in the first embodiment. Note that in the description below, as an example of the plurality of stages  50 , a plurality of stages of behavior change according to the Stages of Behavior Change Model will be adopted. Specifically, it is assumed that the five stages  50 A to  50 E illustrated in  FIG. 1  are the behavior change stage  50 A belonging to the pre-contemplation period, the behavior change stage  50 B belonging to the contemplation period, the behavior change stage  50 C belonging to the preparation period, the behavior change stage  50 D belonging to the action period, and the behavior change stage  50 E belonging to the maintenance period. 
     The stage definition section  110  performs stage definition (S 701 ). The stage definition includes the above-described clustering, defining the stage indicator  55 , and defining each of the stages  50 A to  50 E. With respect to S 701 , for example, the following may be adopted. In S 701 , a definition of the stage indicator  55  and a definition of each of the stages  50 A to  50 E are determined.
         With respect to each stage  50  and each stage pair, a condition for identifying that the stage or the stage pair is applicable is predetermined for each qualitative data item (each data item in at least one type of the qualitative data  152 ) that can be adopted as a viewpoint of the stage indicator  55 , in other words, for each stage indicator candidate (each candidate for the stage indicator  55 ).   A viewpoint (for example, a viewpoint derived from at least one type of the qualitative data  152 ) that can be adopted in clustering may be predetermined for each field pattern.   Clustering is performed, for example, for each viewpoint group (one or more viewpoints) corresponding to a specified field pattern. An arbitrary stage indicator candidate (a candidate for the axis) may be selected before clustering is started, and clustering may be performed based on the stage indicator candidate.   The stage definition section  110  performs condition determination as to whether or not a plurality of target person groups  40  obtained as a result of any clustering can be classified into the stages  50 A to  50 E such that a condition corresponding to an arbitrary stage indicator candidate is satisfied. When a result of the condition determination is true, the stage indicator candidate is defined (determined) as the stage indicator  55 . When a result of the condition determination is false, the stage definition section  110  may select an arbitrary stage indicator from among stage indicator candidates yet to be selected, and condition determination as described above may be performed with respect to the stage indicator candidate. In other words, the stage definition section  110  may iterate selection of a stage indicator candidate and condition determination as described above until a result of the condition determination that is true is obtained.       

     The gap identification section  120  performs gap identification (S 702 ). Specifically, for example, the gap identification section  120  identifies characteristics  60 A to  60 E corresponding to the stages  50 A to  50 E, respectively. For each stage  50 , the characteristic  60  may be a characteristic of one or more target person groups  40  classified into the stage, and the characteristic may include a plurality of characteristic items and a plurality of characteristic values as described above. At least one characteristic item may be a question obtained from the answer data in the target person survey data  152 B, and a characteristic value corresponding to the characteristic item may be an answer to the question. 
     The reason/measure identification section  130  performs reason/measure identification (S 703 ). A specific example of the reason/measure identification is as described above. 
     The display section  140 X performs display (S 704 ). Specifically, as an example of the result processing, which is processing related to a gap reason/shift measure identified for each stage pair (for example, an inhibitory/stimulatory factor, a cognitive bias, and a measure pattern (and a shift measure belonging to the measure pattern) identified for each stage pair), the display section  140 X performs processing of outputting information related to the identified gap reason and shift measure (which may include information related to the defined stages  50 A to  50 E). Although the output is a display (output of information for display) in the present embodiment, another type of output such as printing may be made instead of, or in addition to, to the display. 
     In the present embodiment, for example, the display section  140 X displays at least one of a behavior design canvas sheet screen and a customer journey map screen, as a screen of the information related to the identified gap reason and shift measure. 
       FIG. 8  shows an example of the behavior design canvas sheet screen. 
     A behavior design canvas sheet screen  800  is, for example, a GUI (Graphical User Interface). The screen  800  displays a behavior design canvas sheet. “Behavior design canvas sheet” is a sheet that shows, with respect to a stage pair specified by a user among the stages  50 A to  50 E, an identified gap reason, an identified shift measure, a touchpoint of the shift measure, and a label of data at least available with respect to the stage pair. 
     The behavior design canvas sheet screen  800  includes, for example, a stage area  810 , a factor area  820 , an idea area  840 , a touchpoint area  850 , and a data area  860 . The rectangular areas  820  to  860  that are extensible in a horizontal direction (an example of a first direction) are arranged in a vertical direction (an example of a second direction orthogonal to the first direction). 
     The stages  50 A to  50 E are displayed in the stage area  810 . A stage pair specified among the stages  50 A to  50 E (“stage pair of interest” in the description regarding  FIG. 8 ) is highlighted. Thus, an administrator (an example of a user) can recognize information on which stage pair is displayed in the behavior design canvas sheet screen  800 . 
     One or more factor objects  821  are displayed in the factor area  820 . Each factor object  821  indicates a label (or a content) of an inhibitory/stimulatory factor identified with respect to the stage pair of interest. 
     One or more measure objects  841  are displayed in the idea area  840 . Each measure object  841  indicates a shift measure belonging to any of one or more measure patterns identified with respect to the stage pair of interest, and a cognitive bias, if any, used in the shift measure. 
     One or more touchpoint objects  851  are displayed in the touchpoint area  850 . Each touchpoint object  851  indicates a label (or a content) of a touchpoint. 
     The data area  860  shows one or more data objects  861 . Each data object  861  indicates a label of a data type available or usable with respect to the stage pair of interest. 
     At least one of the objects  821 ,  841 ,  851 , and  861  (and/or images of the plurality of stages displayed in the stage area  810 ) may be GUI parts. For example, when any one touchpoint object  851  is specified by the administrator, the display section  140 X may highlights a measure object  841  of a shift measure applicable to a measure pattern to which a touchpoint corresponding to the object  851  belongs. Highlighting may be relative highlighting, for example, by changing a form of displaying the measure object  841 , or making non-applicable measure objects  841  of the shift measure undisplayed. 
       FIG. 9  shows an example of the customer journey map screen. 
     A customer journey map screen  900  is, for example, a GUI (Graphical User Interface). The screen  900  displays a customer journey map. “Customer journey map” is a sheet that shows a panoramic view of a plurality of stages, a gap reason identified with respect to each stage pair, and a shift measure identified with respect to each stage pair. 
     In any of the screens  800  and  900 , at least part of the displayed information may be information identified from at least part of the behavior data  150  and the relationship information  160 . Accordingly, of the information displayed in the screen  800  or  900 , an information item that is not mentioned in the description of the behavior data  150  and the relationship information  160  may actually be included in any one of the behavior data  150  and the relationship information  160 , or may be included in other information than the behavior data  150  and the relationship information  160 . 
     The foregoing is the description of the first embodiment. 
     Note that after S 701  to S 703  are performed (for example, after S 704  is performed thereafter), measurement (verification) of an effect of S 701  to S 703 , that is, effect measurement such as comparison between the behavior data  150  before and after the displayed shift measure is conducted may be performed. Based on a result of the effect measurement, information update may be performed on information (typically, the relationship information  160 ) to be referred to in the processing in S 701  to S 703  by, for example, the stage definition section  110 . The series of processing that is S 701  to S 703 →effect measurement→information update as described above is iterated, whereby optimization of the relationship information  160  can be anticipated, and resultantly, optimization of matching between the behavior data  150  and the relationship information  160  (optimization of matching between the definitions of the plurality of stages  50  and the plurality of gap reasons/shift measures) can be anticipated. 
     Second Embodiment 
     A second embodiment will be described. In the description below, differences from the first embodiment will mainly be described, and a description of common points with the first embodiment will be omitted or simplified. 
     In the second embodiment, selection and execution of a shift measure are performed instead of (or in addition to) display of the information related to a gap reason/shift measure (for example, a behavior design canvas sheet and a customer journey map). 
       FIG. 10  shows an example of an entire configuration of a system according to the second embodiment. 
     In the assistance server  200 , a measure selection section  140 Y and a measure execution section  140 Z are implemented instead of (or in addition to) the display section  140 X. The measure selection section  140 Y and the measure execution section  140 Z are examples of the processing execution section  140 . The measure selection section  140 Y selects a shift measure to be executed from among shift measures identified by the reason/measure identification section  130 . The measure execution section  140 Z executes the shift measure selected by the measure selection section  140 Y. 
       FIG. 11  shows an example of a flow of entire processing performed in the second embodiment. 
     Processing similar to S 701  to S 703  is performed (S 1101  to S 1103 ). 
     Thereafter, the measure selection section  140 Y performs measure selection (S 1104 ). Specifically, for example, with respect to each of one or more target persons among a plurality of target persons, the measure selection section  140 Y selects a shift measure applicable to the target person from among shift measures identified by the reason/measure identification section  130  with respect to a stage pair including a stage to which the target person belongs and a next higher stage. Each of the “one or more target persons” may be a target person who is arbitrarily selected, or may be a target person on which profile-related information or the like satisfies a predetermined condition. The selected shift measure may be any one of the following.
         In iteration of a series of processing that is S 1101  to S 1103 →effect measurement→information update, the stage definition section  110  assigns, for each shift measure (or measure pattern) executed, a score according to a degree of an effect measured about the shift measure. The measure selection section  140 Y selects a shift measure assigned a relatively high score (for example, a highest score) among the shift measures identified by the reason/measure identification section  130 .   For each of the one or more target persons, the measure selection section  140 Y selects a shift measure that is suitable to the profile-related information or the like on the target person, among the shift measures identified by the reason/measure identification section  130 . In such a case, for example, in the measure table  163 , information related to a target person (for example, profile-related information on a target person) may be associated with each shift measure.       

     The measure execution section  140 Z performs measure execution (S 1105 ). Specifically, the measure execution section  140 Z executes the shift measure selected in S 1104 . 
     An example of the shift measure execution is output (for example, printing) of an individual report to a target person. 
       FIG. 12  shows an example of the individual report. 
     An individual report  1200  is a report made for a target person individually and is, for example, a report that shows information according to a gap reason and a shift measure applicable to the target person, among gap reasons and shift measures identified with respect to each stage pair. 
     According to the example in  FIG. 12 , the individual report  1200  is a “claim for reimbursement of traveling expenses” report on an individual employee, regarding accelerated submission by employees of a claim for reimbursement of traveling expenses. 
     Although several embodiments have been described hereinabove, the embodiments are given for illustrative purposes to describe the present invention, and are not intended to limit the scope of the invention to the embodiments. The present invention can also be implemented in other various forms.