Behavior support device, behavior support method, and behavior support program

Abstract

To propose an effective action to a user.SOLUTION: An action support device supporting an action of a user so as to achieve a purpose comprises: a storage unit; an action measurement unit; and a selection unit. The storage unit stores action pattern data in which a basic action pattern in the past action pattern of a user is associated with a derivative action pattern executed by the user following the base action pattern. The action measurement unit measures the action pattern of the user. The selection unit determines whether or not a real-time action pattern of the user measured by the action measurement unit is similar to the basic action pattern in the action pattern data stored in the storage unit. The selection unit selects the derivative action pattern associated with the basic action pattern similar to the action pattern. The selection unit also outputs a recommendation of the action based on the selected derivative action pattern via an output device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an action support device, an action support method, and an action support program. [Background technology]

[0002] There is a demand to extend the healthy lifespan of elderly people, prevent them from transitioning to nursing care, and reduce nursing care costs. Recent cohort studies have revealed that elderly people who participate a lot in society, such as working, participating in hobby clubs, and playing sports, or who are highly active in terms of the number of steps they take and the amount of time they spend walking (called social participation behavior), have a longer healthy lifespan and are less likely to develop dementia or transition to nursing care.

[0003] In view of this, for example, an information processing device has been proposed that suggests actions that are likely to be performed by a user who tends to be sedentary, based on the user's behavioral history. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2015-49825 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above-mentioned conventional technology does not take into account the situation the user is in when suggesting actions, so if an action that cannot be carried out is suggested, not only is it ineffective, but it may also lower the user's motivation and cause them to stop using the service.

[0006] Since there are many variations in an individual's lifestyle patterns, health conditions, geographical conditions, etc., it is difficult to consider all of these and suggest actions that an individual can take. In addition, there are cases where the suggested actions cannot be implemented because the timing is not right, such as suggesting a walk during work or eating after a meal, without considering the situation at the time of the action being suggested.

[0007] The present invention has been made in consideration of the above, and has as its object to provide a user with suggestions for effective actions. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems, one aspect of the present invention is a behavior support device that supports a user's behavior to achieve a goal, and is characterized by having a memory unit that stores behavior pattern data that corresponds a basic behavior pattern from the user's past behavior patterns to a derived behavior pattern performed by the user following the basic behavior pattern, a behavior measurement unit that measures the user's behavior pattern, and a selection unit that determines whether the user's real-time behavior pattern measured by the behavior measurement unit is similar to the basic behavior pattern from the behavior pattern data stored in the memory unit, selects the derived behavior pattern that corresponds to the basic behavior pattern that is similar to the behavior pattern, and outputs a recommended behavior based on the selected derived behavior pattern via an output device. Effect of the Invention

[0009] According to one aspect of the present invention, for example, it is possible to provide effective suggestions for actions to a user. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a behavior support apparatus according to a first embodiment. [Diagram 2] FIG. 4 is a diagram showing an example of the configuration of behavior (location information) data. [Diagram 3] FIG. 4 is a diagram showing an example of the configuration of user attribute information. [Figure 4] FIG. 13 is a diagram showing an example of the configuration of a derived behavior pattern effect evaluation dictionary. [Diagram 5] FIG. 4 is a diagram showing an example of the configuration of location label information. [Figure 6] FIG. 13 is a diagram showing an example of the configuration of a basic behavior pattern list. [Figure 7]FIG. 4 is a diagram showing an example of the configuration of behavior pattern data. [Figure 8] 5 is a flowchart showing an example of a process for generating behavior pattern data according to the first embodiment. [Figure 9] 1 is a flowchart showing an example of a recommendation execution process according to the first embodiment. [Figure 10] FIG. 4 is a diagram for explaining a processing example of performing a recommendation according to the first embodiment. [Figure 11] FIG. 4 is a diagram for explaining a processing example of performing a recommendation according to the first embodiment. [Figure 12] 5 is a flowchart showing an example of a process of updating behavior pattern data according to the first embodiment. [Figure 13] 11 is a diagram showing an example of a recommendation notification sent to an action support apparatus by push communication. [Figure 14] FIG. 13 is a diagram showing an example of a screen for inquiring about various data. [Figure 15] FIG. 13 is a diagram showing an example of a screen displaying the acceptance status of a recommendation. [Figure 16] FIG. 13 is a diagram showing an example of a screen displaying the acceptance status of a recommendation. [Figure 17] FIG. 13 is a diagram showing an example of a screen displaying the acceptance status of a recommendation. [Figure 18] FIG. 11 is a diagram showing an example of the configuration of a derived action pattern effect evaluation dictionary according to the second embodiment. [Figure 19] FIG. 11 is a diagram showing an example of the configuration of an action support apparatus and an action support system according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the embodiment described below, including the drawings, is merely an example and does not limit the invention according to the claims. In addition, not all of the elements and combinations thereof described in the embodiments are necessarily essential to the solution of the invention. In addition, illustrations and descriptions of configurations that are essential to the configuration of the invention but are well known may be omitted.

[0012] In the following description, the program may be installed in a device such as a computer, or may be, for example, in a program distribution server or a computer-readable (for example, non-transitory) recording medium. Also, in the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0013] In the following description, the processor may be one or more. The processor is typically a microprocessor such as a CPU (Central Processing Unit), but may be other types of processors such as a GPU (Graphics Processing Unit). The processor may be a single-core or multi-core. The processor may be a processor in a broader sense, such as a hardware circuit (for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit)) that performs part or all of the processing.

[0014] In the following description, identification numbers are used as identification information for various objects, but other types of identification information (e.g., identifiers including letters and symbols) may be used. In the following description, "#Y" represents the number Y, and for example, "XXX#Y" represents XXX identified by the number Y.

[0015] In the following description, when elements of the same type are described without distinction, reference signs (or common signs among reference signs) may be used, and when elements of the same type are described with distinction, identification numbers (or reference signs) of the elements may be used. Furthermore, the number of elements shown in each figure is an example and is not limited to the number shown in the figures.

[0016] [Embodiment 1] 1 is a diagram showing an example of the configuration of a behavior support device 1 according to embodiment 1. The behavior support device 1 is a portable general-purpose computer, such as a slate terminal such as a smartphone or a tablet, or a wearable device such as a smartwatch. The behavior support device 1 includes an auxiliary memory device 10, a main memory device 20, a processor 30, an input device 40, an output device 50, an acceleration sensor 60, and a position information sensor 70.

[0017] The primary storage device 20 is a memory that cooperates with the processor 30 when it executes processing. The input device 40 is a device that accepts user input, such as a touch panel. The output device 50 is a device that outputs various information to the user, such as a display or a speaker. The acceleration sensor 60 detects the acceleration of the action support apparatus 1. The position information sensor 70 acquires position information of the action support apparatus 1 from a GPS (Global Positioning System), a wireless base station, or the like.

[0018] The auxiliary storage device 10 is a storage or the like, and stores behavior (location information) data 11, user attribute information 12, derived behavior pattern effect evaluation dictionary 13, location label information 14, basic behavior pattern list 15, and behavior pattern data 16.

[0019] The behavior (location information) data 11 records location information indicating the user's staying location at each time for each date and day of the week, as shown in an example table configuration in FIG.

[0020] The user attribute information 12, as shown in an example table configuration in Figure 3, records the age, gender, home location (location information), work days (school days), work location (location information) (school location location information), etc. of the user who owns the action support device 1.

[0021] The derived behavior pattern effect evaluation dictionary 13 records the correspondence between the increase or decrease of each corresponding behavior (social participation behavior) in a derived behavior pattern following a basic behavior pattern and the negative and positive evaluations, as shown in the table configuration example in Figure 4. In the example of Figure 4, the derived behavior pattern effect evaluation dictionary 13 provides an interpretation for evaluating the increase or decrease of each behavior in a derived behavior pattern from the perspective of preventing a person from becoming in a state requiring care. For example, if a derived behavior pattern increases the "number of steps" from a basic behavior pattern, a positive evaluation (+1) is given from the perspective of preventing a person from becoming in a state requiring care.

[0022] In the derived behavior pattern effect evaluation dictionary 13 shown in Fig. 4, the evaluation of the change in behavior (social participation behavior) is performed in two stages, negative evaluation and positive evaluation, but it is not limited to this and may be evaluated in three or more stages according to the degree of increase or decrease. In this case, points are given for each stage, and the derived behavior pattern with the highest total point is selected.

[0023] As shown in the table configuration example in FIG. 5, the location label information 14 records a user-registered label, a POI (Point of Interest) label, a common label, etc. for each location. The user-registered label provides a label freely edited by the user for the location information of each location. The POI label provides a label of the actual name on the map for the location information of each location. The common label provides a label that generalizes the user-registered label or the POI label for the location information of each location. The common label may be a symbol or the like as long as it is information that can be identified after generalizing the location information of each location.

[0024] The basic behavior pattern list 15 records basic behavior patterns, days of the week, places of stay, etc., as shown in the table configuration example in Fig. 6. The basic behavior pattern list 15 records the correspondence between the day of the week and the places of stay, and the basic behavior pattern, which is the identification information given to this correspondence, in association with each other.

[0025] As shown in FIG. 7, the behavior pattern data 16 records the frequency (e.g., number of times per month) and behavior content of the basic behavior pattern, as well as the frequency, acceptance, effect, and behavior content of the derived behavior pattern for each behavior pattern. A behavior pattern is a grouping of records of the behavior (location information) data 11 including the basic behavior pattern and the derived behavior pattern. Acceptance means that the behavior (content) recommended to the user is accepted and executed by the user. The acceptance is an example of a usefulness score, and may be a cumulative score with +1 for acceptance and -1 for negative acceptance, or an acceptance rate (percentage). The effect is an example of a usefulness score, and indicates the degree to which it is useful for the user to execute the derived behavior pattern in order to achieve a purpose.

[0026] The processor 30 is an arithmetic processing device such as a CPU that executes the recommendation generation software 30s in cooperation with the primary storage device 20. The processor 30 realizes a behavior measurement unit 31, a behavior pattern extraction unit 32, a derived behavior pattern effect evaluation unit 33, a recommendation pattern selection unit 34, and a recommendation pattern execution determination unit 35 by executing the recommendation generation software 30s.

[0027] Furthermore, the processor 30 realizes an acceleration sensor control unit 36 ​​and a position information sensor control unit 37 by executing the driver. The acceleration sensor control unit 36 ​​controls the acceleration sensor 60 and acquires acceleration information of the action support apparatus 1. The position information sensor control unit 37 controls the position information sensor 70 and acquires position information of the action support apparatus 1.

[0028] The behavior measurement unit 31 measures the behavior of the user who owns the behavior support device 1 based on the acceleration information and location information of the behavior support device 1 acquired in real time, and stores the measurement results as a behavior history in the behavior (location information) data 11. At this time, the behavior measurement unit 31 judges the behavior (staying at home, movement including walking, staying outside the home, etc.), and assigns location information to stay at a location, and movement or walking to movement between locations. Whether it is movement or walking can be determined from the movement speed based on changes in the location information.

[0029] The behavior pattern extraction unit 32 generates a behavior pattern of a character string representing each record (behavior history) of the user's past behavior (location information) data 11, for example, movement with "-" and stay with location information of the stay point. If the stay point is the home (point 0), the location information is represented with "·". The behavior pattern may be, for example, "·····-11111111-···············". The behavior pattern extraction unit 32 groups (clustering, etc.) the generated behavior patterns.

[0030] In addition, the behavioral pattern extraction unit 32 calculates the similarity (behavioral similarity) between all pairs of behavioral patterns in each group. The behavioral similarity can be calculated as a similarity coefficient such as a Dice coefficient or a cost of a shortest path problem such as a Dijkstra algorithm. The behavioral similarity may also be calculated by machine learning.

[0031] The behavior pattern extraction unit 32 extracts, as a basic behavior pattern, a behavior pattern that most frequently has a calculated behavior similarity to other patterns that is equal to or greater than a threshold value. The behavior pattern extraction unit 32 also extracts, as a derived behavior pattern, a behavior pattern that has a behavior similarity to the basic behavior pattern that is equal to or greater than a threshold value.

[0032] The behavior pattern extraction unit 32 stores the extracted basic behavior pattern and its day of the week information in the basic behavior pattern list 15. By using the basic behavior pattern list 15 and the location label information 14, it is possible to grasp behavior patterns such as having lunch at a favorite cafe every Saturday (behavior pattern A) and going to work on weekdays (behavior pattern B), etc.

[0033] Moreover, the behavior pattern extraction unit 32 stores the extracted basic behavior pattern and derived behavior pattern in the behavior pattern data 16. Note that, if the same basic behavior pattern and derived behavior pattern already exist in the behavior pattern data 16, they are not stored in the behavior pattern data 16, and the existing data is maintained.

[0034] The behavior pattern data 16 has time information of stay at each point according to the storage format of the behavior pattern in the behavior content. For example, a behavior pattern stored in the behavior content in the format of "·····-11111111-···············" is a character string in which the position information of each time is arranged in chronological order, so that the position in the character string represents the time information. However, the behavior pattern data 16 does not have day of the week information. Therefore, by referring to the basic behavior pattern list 15, the day of the week information of the basic behavior pattern staying at each point can be obtained. By linking the basic behavior pattern list 15 and the behavior pattern data 16, when detecting a basic behavior pattern from the user's real-time behavior history, a recommendation that can be implemented under a specific situation can be selected taking into account the day of the week and the time. In addition, it is possible to prevent the recommendation from being implemented on an inappropriate day of the week or at an inappropriate time.

[0035] The derived behavior pattern effect evaluation unit 33 evaluates the effect of the derived behavior pattern on the basic behavior pattern extracted by the behavior pattern extraction unit 32, using the derived behavior pattern effect evaluation dictionary 13. For example, if the derived behavior pattern is a behavior pattern in which the "number of steps" increases from the basic behavior pattern, it is evaluated as a "plus" (+1), and if it is a behavior pattern in which the "number of steps" decreases, it is evaluated as a "minus" (-1). This evaluation is performed for each derived behavior pattern for all behaviors listed in the derived behavior pattern effect evaluation dictionary 13, and the sign of the counted result of "+1" or "-1" (or the counted result itself) is regarded as the effect of the derived behavior pattern.

[0036] The derived behavior pattern effect evaluation section 33 stores the evaluation result of the effect of the derived behavior pattern in the behavior pattern data 16.

[0037] The recommendation pattern selection unit 34, like the behavioral pattern extraction unit 32, generates a behavioral pattern of character strings representing, for example, movement with "-", home (point 0) with "·", and a stay with the location information of the stay point, from the real-time behavioral history measured and stored in the behavioral (location information) data 11.

[0038] In addition, the recommendation pattern selection unit 34 calculates the behavioral similarity between the behavioral pattern of the real-time behavior history and the basic behavioral pattern stored in the behavioral pattern data 16. When calculating the behavioral similarity, a time condition, such as the time difference between the behavioral patterns being within a predetermined time, is also taken into consideration.

[0039] The recommendation pattern selection unit 34 selects a derived behavior pattern with a positive acceptance rate, a top β% (β is a predetermined number) and an effect of "+" from among derived behavior patterns of basic behavior patterns with behavior similarity in the top α% (α is a predetermined number). At this time, the recommendation pattern selection unit 34 selects a derived behavior pattern whose day of the week (basic behavior pattern list 15 (FIG. 6)) of the behavior pattern corresponding to the basic behavior pattern matches the day of the week of the real-time history. If the day of the week of the behavior pattern corresponding to the basic behavior pattern does not match the day of the week of the real-time history, for example, there may be a problem in which it is detected that the user is working on Sunday, which is a holiday. Then, the recommendation pattern selection unit 34 recommends the selected derived behavior pattern as a recommended pattern to the user via the output device 50.

[0040] The recommendation pattern execution determination unit 35 performs a similarity search of behavioral patterns based on the actual behavioral history after the recommendation by the recommendation pattern selection unit 34, and searches for behavioral patterns similar to the recommendation pattern implemented by the recommendation pattern selection unit 34. The recommendation pattern execution determination unit 35 generates, from the actual behavioral history stored in the behavior (location information) data 11, a character string pattern that represents, for example, movement after the recommendation by "-", home (point 0) by "·", and a stay by the location information of the stay point, in the same way as the behavioral pattern extraction unit 32. When a behavioral pattern having a behavioral similarity with the recommendation pattern selected by the recommendation pattern selection unit 34 of γ or more (γ is a predetermined number) is hit by the similarity search, the recommendation pattern execution determination unit 35 adds "+1" to the acceptability of the corresponding recommendation pattern.

[0041] (Behavior Pattern Data Generation Process According to the First Embodiment) 8 is a flowchart showing an example of a process for generating behavior pattern data according to embodiment 1. The process for generating behavior pattern data is executed at a predetermined cycle or at the timing of a user instruction.

[0042] First, in step S11, the behavior pattern extraction unit 32 reads each record (behavior history), location information, and acceleration information of the behavior (location information) data 11. Next, in step S12, the behavior pattern extraction unit 32 generates and groups character string patterns representing each behavior history read in step S11.

[0043] Next, in step S13, the behavior pattern extraction unit 32 calculates the similarity (behavior similarity) between all pairs of patterns in each group grouped in step S12. Next, in step S14, the behavior pattern extraction unit 32 extracts the pattern that most frequently has a behavior similarity to other patterns calculated in step S13 that is equal to or greater than a threshold as a basic behavior pattern of the group. Next, in step S15, the behavior pattern extraction unit 32 extracts the pattern that has a behavior similarity to the basic behavior pattern that is equal to or greater than a threshold as a derived behavior pattern.

[0044] Next, in step S16, the derived behavior pattern effect evaluation unit 33 evaluates the effect of the derived behavior pattern on the basic behavior pattern extracted by the behavior pattern extraction unit 32, using the derived behavior pattern effect evaluation dictionary 13. Next, in step S17, the derived behavior pattern effect evaluation unit 33 stores the evaluation results of the extracted basic behavior pattern, derived behavior pattern, and the effect of the derived behavior pattern in the behavior pattern data 16.

[0045] (Recommendation Execution Process According to the First Embodiment) 9 is a flowchart showing an example of a recommendation implementation process according to embodiment 1. The recommendation implementation process is executed in real time.

[0046] First, in step S21, the recommendation pattern selection unit 34 reads the behavior history, location information, and acceleration information measured in real time and stored in the behavior (location information) data 11. Next, in step S22, the recommendation pattern selection unit 34 generates a behavior pattern of a character string representing the behavior history read in step S21.

[0047] Next, in step S23, the recommendation pattern selection unit 34 performs a similarity search of the basic behavior patterns stored in the behavior pattern data 16 to search for a basic behavior pattern similar to the behavior pattern of the real-time behavior history.

[0048] Next, in step S24, the recommended pattern selection unit 34 selects derived behavior patterns with positive acceptability, top β% (β is a predetermined number), and effect of "+" from derived behavior patterns of basic behavior patterns with a time difference within a predetermined time and behavior similarity in the top α%. At this time, the recommended pattern selection unit 34 selects derived behavior patterns whose day of the week of the behavior pattern corresponding to the basic behavior pattern (basic behavior pattern list 15 (FIG. 6)) matches the day of the week of the real-time history.

[0049] Next, in step S25, the recommendation pattern selection unit 34 sets the derived behavior pattern selected in step S24 as a recommendation pattern, and recommends the recommendation pattern to the user via the output device 50.

[0050] 10 and 11 are diagrams for explaining a processing example of performing a recommendation according to the first embodiment.

[0051] FIG. 10(a) shows a behavior pattern (behavior pattern A) in the behavior history of "Leaving home (point 0) at 11:00, walking to point 1 for about 30 minutes, staying at point 1 for 90 minutes, and returning home after walking for about 30 minutes." While behavior pattern A appears frequently, assume that behavior pattern A', which stops at point 2 in the past as a derived behavior pattern of behavior pattern A, exists as shown in FIG. 10(b). When predicting or detecting the situation in which behavior pattern A is being executed, recommending a visit to point 2, as shown in FIG. 10(c), can be expected to increase the number of points stayed (visited) and walking time. By making a recommendation that has been realized in the past under a situation similar to that of the past, it is possible to implement a recommendation that is more likely to be realized.

[0052] FIG. 11(a) shows a behavior pattern (behavior pattern B) in the behavior history of "commuting (round trip) by approximately 15 minutes (e.g., train)." While behavior pattern B appears frequently, assume that behavior pattern B', which is a derived behavior pattern of behavior pattern B in the past, in which the user walked home without taking the train on the return journey, exists as shown in FIG. 11(b). When a situation in which behavior pattern B is being performed is predicted or detected, as shown in FIG. 11(c), if behavior pattern B', which is to walk home on the return journey, is recommended, an increase in walking time can be expected. The recommendation of behavior pattern B' is performed only when external information such as the probability of precipitation and temperature obtained from a weather forecast corresponds to a situation in which behavior pattern B' can be performed without any problems, and thus the recommendation can be performed under a situation in which it is feasible.

[0053] (Action Pattern Data Update Process According to the First Embodiment) 12 is a flowchart showing an example of a behavior pattern data update process according to embodiment 1. The behavior pattern data update process is executed at a predetermined cycle or at the timing of a user instruction.

[0054] First, in step S31, the recommendation pattern execution determination unit 35 performs a similarity search of behavior patterns based on actual behavior history after the recommendation by the recommendation pattern selection unit 34 is implemented, and searches for patterns similar to the recommendation pattern implemented by the recommendation pattern selection unit 34.

[0055] Next, in step S32, the recommendation pattern execution determination unit 35 determines whether or not a behavior pattern having a behavior similarity of γ or more with the recommendation pattern implemented by the recommendation pattern selection unit 34 exists among the patterns based on the actual behavior history after the recommendation is implemented. If a behavior pattern having a behavior similarity of γ or more with the recommendation pattern implemented by the recommendation pattern selection unit 34 exists among the patterns based on the actual behavior history after the recommendation is implemented (step S32 YES), the recommendation pattern execution determination unit 35 proceeds to step S33, and if not (step S32 NO), ends the behavior pattern data update process.

[0056] In step S33, the recommendation pattern execution determination unit 35 updates the behavior pattern data 16 by, for example, incrementing the acceptance degree of the corresponding recommendation pattern implemented by the recommendation pattern selection unit 34 by "+1."

[0057] (Example of the screen display of the behavior support device 1) Next, various screen displays displayed on the screen of the output device 50 of the action support apparatus 1 will be described with reference to FIGS.

[0058] FIG. 13 is a diagram showing an example of a recommendation notification by push communication notified to the action support apparatus 1. In step S25 of the recommendation implementation process (FIG. 9), the contents of the recommendation are output in chronological order by push notification on the display screen 50A of the output device 50 of the action support apparatus 1 as shown in FIG. 13. For example, push notification 51A1 has contents recommending the optimal action to be subsequently performed when a certain action pattern of the user is predicted, such as "Action pattern A of staying at point 1 has been detected. Why don't you extend your trip to point 2?", and corresponds to FIG. 10(c). Also, for example, push notification 51A2 has contents saying "Action pattern B of staying at point n has been detected. Why don't you walk home today?", and corresponds to FIG. 11(c).

[0059] Fig. 14 is a diagram showing an example of a screen for inquiring about various data. When the behavior data display button 52A1 is tapped, behavior (location information) data 11 (Fig. 2) is displayed in the display pane 52A of the output device 50 of the behavior support apparatus 1. When the registered behavior pattern display button 52A2 is tapped, a basic behavior pattern list 15 (Fig. 6) is displayed in the display pane 52A. When the recommendation acceptance status display button 52A3 is tapped, a recommendation acceptance status list (Fig. 15) is displayed in the display pane 52A. When the label edit button 52A4 is tapped, location label information 14 (Fig. 5) is displayed in an editable manner in the display pane 52A.

[0060] In the example shown in Fig. 15, the recommendation acceptance status includes the date of recommendation, the recommendation content, and the result (whether the recommendation was accepted or not) displayed for each recommendation. The recommendation acceptance status list is not limited to the example shown in Fig. 15, and may display the acceptance rate for each day in a calendar format as in Fig. 16. Alternatively, the number of recommendations, the number of acceptances, and the number of acceptances / number of recommendations = acceptance rate may be displayed for each recommendation content as in Fig. 17.

[0061] According to the above-described first embodiment, by referring to the user's past actions, it is possible to recommend highly feasible behavioral patterns that have been performed by the user under similar circumstances in the past. In addition, by learning the user's receptivity to recommendations, it is possible to take into account feasibility factors that are not apparent in the user's situation or actions (e.g., wanting to go home immediately after work because one is tired, or being highly motivated to take a walk after eating out). As a result, by effectively visualizing the social participation actions of users such as elderly people, it is possible to encourage behavioral change in users through intervention services via the action support device 1, realize promotion of social participation actions, and ultimately suppress the elderly from becoming dependent on care, thereby maintaining the soundness of the nursing care insurance system.

[0062] [Embodiment 2] In the first embodiment, the action support device 1 recommends actions that will prevent the user from becoming dependent on care. In contrast, in the second embodiment, the action support device 1 recommends actions that will lead to the user's successful diet. In the second embodiment, the action support device 1 stores a derived action pattern effect evaluation dictionary 13C in the auxiliary storage device 10 instead of the derived action pattern effect evaluation dictionary 13.

[0063] Fig. 18 is a diagram showing an example of the configuration of the derived behavior pattern effect evaluation dictionary 13C according to the second embodiment. The derived behavior pattern effect evaluation dictionary 13C is different from the derived behavior pattern effect evaluation dictionary 13 of the first embodiment in that it provides an interpretation for evaluating the increase or decrease of each behavior of a derived behavior pattern from the viewpoint of succeeding in dieting. Referring to Fig. 18, for example, if a derived behavior pattern causes "dinner time" to be after 8 p.m. with respect to a basic behavior pattern, it is understood that a negative evaluation (-1) is given from the viewpoint of succeeding in dieting.

[0064] The evaluation of the same action may change depending on the purpose of the action support device 1. By adopting an appropriate derived action pattern effect evaluation dictionary in line with the purpose of the action support device 1, the disclosed technology can be applied to all fields, not limited to the healthcare field such as nursing care and dieting in the first embodiment.

[0065] [Embodiment 3] In the first embodiment, the action support device 1 recommends an action to a user by using the action pattern data 16 closed in each action support device 1. In contrast, in the third embodiment, the action support device 1 recommends an action by using the action pattern data 16 of other action support devices 1 as well. Fig. 19 is a diagram showing an example of the configuration of the action support device 1 and the action support system S according to the third embodiment. In Fig. 19, illustration is omitted as appropriate.

[0066] The action support system S includes a server 100 and action support devices 1 of multiple users connected via a network. To share action pattern data 16, the multiple action support devices 1 transmit the position label information 14 of each action support device 1 together with the action pattern data 16 of each action support device 1 to the server 100. Alternatively, the multiple action support devices 1 convert the position information included in the action pattern of the action pattern data 16 of each action support device 1 into a common label using the position label information 14 and then transmit the converted information.

[0067] The server 100 accumulates the position label information 14 and the behavior pattern data 16 received from each behavior support device 1. Then, each behavior support device 1 detects a behavior pattern when a behavior pattern of a real-time behavior history detected by the device itself is similar to a basic behavior pattern stored in the behavior pattern data 16 on the device itself or on the server 100. Then, an appropriate derived behavior pattern is selected from derived behavior patterns corresponding to the basic behavior pattern stored in the behavior pattern data 16 on the device itself or on the server 100, and the appropriate derived behavior pattern is recommended to the user as a recommended pattern.

[0068] Each behavior support device 1 can interpret the location information of other behavior support devices 1 using the common label in the location label information of the other behavior support device 1 as a clue, and use the behavior pattern data 16 of the other behavior support devices 1 as information that is meaningful to the own device as well.

[0069] Note that the plurality of action support apparatuses 1 may directly exchange and share the position label information 14 and the action pattern data 16 among themselves, without going through the server 100.

[0070] The present invention is not limited to the above-described embodiment, and includes various modified examples. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to those having all the configurations described. In addition, it is also possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and to add the configuration of another embodiment to the configuration of one embodiment, as long as there is no contradiction. In addition, it is possible to add, delete, replace, integrate, or distribute a part of the configuration of each embodiment. In addition, the configurations and processes shown in the embodiments can be appropriately distributed, integrated, or replaced based on processing efficiency or implementation efficiency. [Explanation of symbols]

[0071] 1: behavior support device, 10: auxiliary memory device, 11: behavior (location information) data, 12: user attribute information, 13, 13C: derived behavior pattern effect evaluation dictionary, 14: location label information, 15: basic behavior pattern list, 16: behavior pattern data, 20: main memory device, 30: processor, 31: behavior measurement unit, 32: behavior pattern extraction unit, 33: derived behavior pattern effect evaluation unit, 34: recommended pattern selection unit, 35: recommended pattern execution determination unit, 100: server

Claims

1. An action support device for supporting a user's action, comprising: a storage unit that stores behavior pattern data in which a basic behavior pattern among the user's past behavior patterns is associated with a derived behavior pattern executed by the user subsequent to the basic behavior pattern; a behavior measurement unit that measures a behavior pattern of the user; a selection unit that determines whether or not the real-time behavior pattern of the user measured by the behavior measurement unit is similar to the basic behavior pattern of the behavior pattern data stored in the storage unit, selects the derived behavior pattern corresponding to the basic behavior pattern similar to the real-time behavior pattern, and outputs a recommendation of a behavior based on the selected derived behavior pattern via an output device, the storage unit stores an acceptability score indicating a degree to which a recommended behavior based on the derived behavior pattern is output to the user and the recommended behavior is accepted and executed by the user in association with the derived behavior pattern; The behavior support device, wherein the selection unit selects the derived behavior pattern based on the acceptability score.

2. The action support device according to claim 1 , an execution determination unit that refers to a history of the user's behavioral patterns measured by the behavior measurement unit, determines whether the recommendation for the behavior has been accepted by the user and the behavior has been performed, and updates the acceptability score in accordance with the determination result; The action support device further comprises:

3. An action support device for supporting a user's action, comprising: a storage unit that stores behavior pattern data in which a basic behavior pattern among the user's past behavior patterns is associated with a derived behavior pattern executed by the user subsequent to the basic behavior pattern; a behavior measurement unit that measures a behavior pattern of the user; a selection unit that determines whether or not the real-time behavior pattern of the user measured by the behavior measurement unit is similar to the basic behavior pattern of the behavior pattern data stored in the storage unit, selects the derived behavior pattern corresponding to the basic behavior pattern similar to the real-time behavior pattern, and outputs a recommendation of a behavior based on the selected derived behavior pattern via an output device, the storage unit stores a usefulness score indicating a degree of usefulness of the derived behavior pattern with respect to achievement of a purpose when the derived behavior pattern is executed by the user in association with the derived behavior pattern; The action support device, wherein the selection unit selects the derived action pattern based on the usefulness score.

4. The action support device according to claim 3, the storage unit further stores an evaluation dictionary for evaluating the derived behavior pattern executed subsequent to the basic behavior pattern in accordance with the purpose; The action support device, wherein the usefulness score is determined based on the evaluation dictionary.

5. The action support device according to claim 4, The purpose is to prevent the user from transitioning to a care-requiring state, The action support device, wherein the evaluation dictionary is a dictionary for evaluating whether or not the derived action pattern is useful for preventing the user from transitioning to a care-requiring state.

6. An action support device for supporting a user's action, comprising: a storage unit that stores behavior pattern data in which a basic behavior pattern among the user's past behavior patterns is associated with a derived behavior pattern executed by the user subsequent to the basic behavior pattern; a behavior measurement unit that measures a behavior pattern of the user; a selection unit that determines whether or not the real-time behavior pattern of the user measured by the behavior measurement unit is similar to the basic behavior pattern of the behavior pattern data stored in the storage unit, selects the derived behavior pattern corresponding to the basic behavior pattern similar to the real-time behavior pattern, and outputs a recommendation of a behavior based on the selected derived behavior pattern via an output device, an extraction unit for grouping the past behavior patterns of the user measured by the behavior measurement unit, extracting, within each group, a behavior pattern whose similarity to the most other behavior patterns satisfies a predetermined condition as the basic behavior pattern, extracting the other behavior patterns whose similarity to the basic behavior pattern satisfies a predetermined condition as the derived behavior pattern, and storing, in the storage unit, the behavior pattern data in which the extracted basic behavior patterns and the derived behavior patterns are associated with each other; The action support device further comprises:

7. The action support device according to any one of claims 1, 3, and 6, The selection unit is 11. A behavior support device, comprising: a behavior support apparatus, which searches for the behavior pattern data stored in the storage unit of the apparatus itself as well as the behavior pattern data of other behavior support apparatuses to select the derived behavior pattern.

8. The action support device according to any one of claims 1, 3, and 6, The selection unit is The derived behavior pattern executable by the user is selected based on a situation including time information or location information indicated by the real-time behavior pattern of the user measured by the behavior measurement unit.

13. An action support device comprising:

9. A behavior support method executed by a behavior support device that supports a user's behavior, comprising: The action support device includes: a storage unit that stores behavior pattern data in which a basic behavior pattern among the user's past behavior patterns is associated with a derived behavior pattern executed by the user subsequent to the basic behavior pattern; The action support device, Measuring the behavioral patterns of the user; determining whether the measured real-time behavior pattern of the user is similar to the basic behavior pattern of the behavior pattern data stored in the storage unit; Selecting the derived behavior pattern corresponding to the basic behavior pattern similar to the behavior pattern; A recommendation of an action based on the selected derived action pattern is output to the user. Each process has the storage unit stores an acceptability score indicating a degree to which a recommended behavior based on the derived behavior pattern is output to the user and the recommended behavior is accepted and executed by the user in association with the derived behavior pattern; The behavior support device selects the derived behavior pattern based on the acceptability score. The action support method further comprises the steps of:

10. A behavior support method executed by a behavior support device that supports a user's behavior, comprising: The action support device includes: a storage unit that stores behavior pattern data in which a basic behavior pattern among the user's past behavior patterns is associated with a derived behavior pattern executed by the user subsequent to the basic behavior pattern; The action support device, Measuring the behavioral patterns of the user; determining whether the measured real-time behavior pattern of the user is similar to the basic behavior pattern of the behavior pattern data stored in the storage unit; Selecting the derived behavior pattern corresponding to the basic behavior pattern similar to the behavior pattern; A recommendation of an action based on the selected derived action pattern is output to the user. Each process has the storage unit stores a usefulness score indicating a degree of usefulness of the derived behavior pattern with respect to achievement of a purpose when the derived behavior pattern is executed by the user in association with the derived behavior pattern; The behavior support device selects the derived behavior pattern based on the usefulness score. The action support method further comprises the steps of:

11. A behavior support method executed by a behavior support device that supports a user's behavior, comprising: The action support device includes: a storage unit that stores behavior pattern data in which a basic behavior pattern among the user's past behavior patterns is associated with a derived behavior pattern executed by the user subsequent to the basic behavior pattern; The action support device, Measuring the behavioral patterns of the user; determining whether the measured real-time behavior pattern of the user is similar to the basic behavior pattern of the behavior pattern data stored in the storage unit; Selecting the derived behavior pattern corresponding to the basic behavior pattern similar to the behavior pattern; A recommendation of an action based on the selected derived action pattern is output to the user. Each process has The action support device, grouping the measured past behavioral patterns of the user, and extracting, from each group, a behavioral pattern that satisfies a predetermined condition in terms of similarity with the most other behavioral patterns as the basic behavioral pattern; extracting the other behavior patterns whose similarity to the basic behavior pattern satisfies a predetermined condition as the derived behavior pattern; The behavior pattern data in which the extracted basic behavior pattern and the derived behavior pattern are associated with each other is stored in the storage unit. The action support method further comprises the steps of:

12. A behavior support program for causing a computer to function as the behavior support device according to any one of claims 1 to 8.

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