BEHAVIOR ASSESSMENT SERVER AND BEHAVIOR ASSESSMENT METHOD

Abstract

To provide a behavior evaluation server capable of precisely evaluating the social participation of a user based on the activity and interaction of the user.SOLUTION: The behavior evaluation server includes a processor and a memory. The memory holds the time series location of a first user and the time series activities of the first user. The processor generates time series actions of the first user based on the time series location and the time series activity amount and calculates a first indicator indicating the activity rate that indicates the degree of activity based on the generated time series actions, and a second indicator of interaction diversity based on the generated time series actions.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a behavior evaluation server and a behavior evaluation method. [Background technology]

[0002] Background art of this technical field is International Publication No. 2018 / 100797 (Patent Document 1). This publication describes an information processing method including: "acquiring physical fitness and cognitive function measurement results, lifestyle information and social participation score by an acquisition unit; learning correlations between physical fitness values ​​and cognitive function values ​​based on the measurement results and lifestyle information and social participation score by a learning unit; and predicting the user's future physical fitness values ​​and cognitive function values ​​by a prediction unit based on the user's lifestyle information and social participation score using the learned correlations" (see Abstract). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2018 / 100797 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 predicts a user's future physical strength and cognitive function values ​​based on a social participation score, but since a user's social participation can be explained by various aspects such as the user's activity level and interactions, there is a risk that the social participation may not be evaluated accurately. Therefore, one aspect of the present invention evaluates a user's social participation with high accuracy based on the user's activity level and interactions. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, one embodiment of the present invention employs the following configuration: A behavior evaluation server includes a processor and a memory, the memory holds a time series of a location of a first user and a time series of an activity amount of the first user, and the processor generates a time series of behavior of the first user based on the time series of the location and the time series of the activity amount, and calculates a first index indicating an activity ratio indicating a degree of activity based on the generated time series of behavior, and a second index indicating interaction diversity based on the generated time series of behavior. Effect of the Invention

[0006] According to one aspect of the present invention, it is possible to evaluate a user's social participation with a high degree of accuracy based on the user's activity level and interactions.

[0007] Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an example of the configuration of a behavior evaluation system according to a first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of the configuration of a computer constituting each of an evaluation server and a client in the first embodiment. [Diagram 3] FIG. 4 is a diagram illustrating an example of a data configuration of account information in the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a data configuration of intervention service information in the first embodiment. [Diagram 5] FIG. 4 is a diagram illustrating an example of a data configuration of position information in the first embodiment. [Figure 6] FIG. 4 is a diagram illustrating an example of a data configuration of activity amount information in the first embodiment. [Figure 7] FIG. 4 is a diagram illustrating an example of a data configuration of action purpose information in the first embodiment. [Figure 8] FIG. 4 is a diagram illustrating an example of a data configuration of map information in the first embodiment. [Figure 9]FIG. 4 is a diagram illustrating an example of a data configuration of timeline information in the first embodiment. [Figure 10] FIG. 4 is a diagram illustrating an example of a data configuration of activity ratio information in the first embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of a data configuration of exchange diversity information in the first embodiment. [Figure 12] FIG. 4 is a diagram showing an example of a data configuration of exercise information in the first embodiment. [Figure 13] FIG. 4 is a diagram illustrating an example of a data configuration of behavior evaluation information in the first embodiment. [Figure 14] 4 is a diagram showing an example of a data configuration of nursing care need risk rank determination information in the first embodiment. FIG. [Figure 15] FIG. 11 is a diagram illustrating an example of a data configuration of total score rank determination information in the first embodiment. [Figure 16] 11 is a diagram illustrating an example of a data configuration of user aggregated evaluation information in the first embodiment. FIG. [Figure 17] FIG. 4 is a diagram illustrating an example of a data configuration of behavioral tendency information in the first embodiment. [Figure 18] FIG. 4 is a diagram illustrating an example of a data configuration of reference parts information in the first embodiment. [Figure 19] 4 is an example of a reference template in the first embodiment. [Figure 20] FIG. 11 is a diagram illustrating an example of a data configuration of inverse simulation result information in the first embodiment. [Figure 21] FIG. 11 is a diagram illustrating an example of a data configuration of intervention service utilization history information in the first embodiment. [Figure 22] FIG. 4 is a diagram illustrating an example of a data configuration of learning data in the first embodiment. [Figure 23] 1 is a flowchart showing an example of an evaluation process in the first embodiment. [Figure 24] 11 is a flowchart illustrating an example of a timeline counting process in the first embodiment. [Diagram 25] 13 is a flowchart showing an example of an activity ratio calculation process in the first embodiment. [Figure 26]13 is a flowchart illustrating an example of an exchange diversity calculation process according to the first embodiment. [Figure 27] 11 is a flowchart illustrating an example of a motion calculation process in the first embodiment. [Figure 28A] 11 is a flowchart showing an example of a behavior evaluation process in which a care-needing risk is used in the first embodiment. [Figure 28B] 11 is a flowchart showing an example of a behavior evaluation process in which a standard score is used in the first embodiment. [Figure 29A] 11 is a flowchart illustrating an example of an advice generating process according to the first embodiment. [Figure 29B] 11 is a flowchart showing an example of advice generation processing in which a care-needing risk is used in the first embodiment. [Figure 29C] 13 is a flowchart showing an example of an advice generating process in which a standard score is used in the first embodiment. [Diagram 30] 11 is a flowchart illustrating an example of a recommendation process in the first embodiment. [Diagram 31] 11 is a flowchart showing an example of a nursing care-requiring risk prediction model update process in the first embodiment. [Figure 32A] FIG. 13 is a diagram showing an example of an evaluation result display screen in the first embodiment. [Figure 32B] FIG. 13 is a diagram showing an example of an evaluation result display screen in the first embodiment. [Figure 32C] FIG. 13 is a diagram showing an example of an evaluation result display screen in the first embodiment. [Diagram 33] FIG. 11 is a diagram showing an example of a recommendation result display screen in the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In this embodiment, the same components are generally designated by the same reference numerals, and repeated explanations are omitted. Note that this embodiment is merely an example for realizing the present invention, and does not limit the technical scope of the present invention. EXAMPLES

[0010] 1 is a block diagram showing a configuration example of a behavior evaluation system. The behavior evaluation system includes an evaluation server 100 and one or more clients 200 connected via a network such as the Internet. The evaluation server 100 evaluates the behavior of a user having the client 200. The client 200 is a device that measures the location and activity amount of the user, such as a smartphone or a wearable device.

[0011] The evaluation server 100 includes, for example, a timeline aggregation unit 111, an activity ratio calculation unit 112, an interaction diversity calculation unit 113, an exercise calculation unit 114, a care need risk prediction unit 115, a behavior evaluation unit 116, an advice generation unit 117, a recommendation processing unit 118, a processing model update unit 119, and a standard score calculation unit 120, all of which are functional units.

[0012] The timeline counting unit 111 counts the timeline of the user's actions. The activity ratio calculation unit 112 calculates an activity ratio indicating the frequency of the user's activities. In this embodiment, the number of times the user goes out is calculated as an example of the activity ratio, but the activity ratio may be calculated using other indicators (for example, the number of times the user goes out for each purpose of the action, such as sports hobbies or cultural hobbies) in addition to or instead of the number of times the user goes out.

[0013] The interaction diversity calculation unit 113 calculates interaction diversity indicating the diversity of participating organizations and participants related to the user's activities. In this embodiment, as an example of interaction diversity, the total number of stay locations of the user and the number of stay locations for each behavioral purpose are calculated, but in addition to or instead of the number of stay locations, the number of contacts with others at the stay locations (for example, acquired by short-range wireless communication between the clients 200), as well as the frequency of contact, age, gender, and the composition ratio of the residential area, may be calculated as interaction diversity. Unless otherwise specified, hereinafter, the "number of stay locations" refers to the total number of stay locations of a certain user regardless of the behavioral purpose.

[0014] The exercise calculation unit 114 calculates exercise indicating the amount of exercise due to the user's behavior. In this embodiment, the average number of steps per day is calculated as an example of exercise, but in addition to or instead of the average number of steps, calories burned, heart rate, walking distance, walking time, moving distance, and moving time may be calculated as exercise. Unless otherwise specified, hereinafter, "average number of steps" refers to the average number of steps taken by a certain user per day. It can be said that the activity ratio, interaction diversity, and exercise are all indicators indicating the activity level of the user's behavior, indicators measuring the user's social participation, and indicators contributing to the improvement of the risk of needing nursing care.

[0015] The nursing care need risk prediction unit 115 predicts the user's nursing care need risk (one example of an index for comprehensively evaluating the user's behavior) based on the nursing care need risk prediction model. The nursing care need risk is the probability that the user will be certified as needing assistance or needing nursing care within a predetermined period (for example, within the next three years). In addition, instead of the nursing care need risk, an expected amount of the user's out-of-pocket nursing care expenses within a predetermined period may be used.

[0016] The behavior evaluation unit 116 evaluates the behavior of the user based on the user's activity ratio, interaction diversity, and exercise. The advice generation unit 117 generates advice for the user based on the user's activity ratio, interaction diversity, and exercise comparison tendency with people of the same sex and age, and self-tendency. The recommendation processing unit 118 extracts intervention services to be recommended to the user.

[0017] The processing model update unit 119 updates the nursing care need risk prediction model. The standard score calculation unit 120 calculates, for each user, a standard score indicating the position of each of the activity ratio, interaction diversity, and exercise among all users, and a total score (an example of an index for comprehensively evaluating the user's behavior) indicating the position of the overall evaluation of the activity ratio, interaction diversity, and exercise among all users.

[0018] The evaluation server 100 holds, for example, account information 131, intervention service information 132, location information 133, activity amount information 134, behavioral purpose information 135, map information 136, timeline information 137, activity ratio information 138, interaction diversity information 139, exercise information 140, behavior evaluation information 141, care need risk rank determination information 142, total score rank determination information 143, user aggregate evaluation information 144, behavior tendency information 145, reference part information 146, reference template 147, inverse simulation result information 148, intervention service usage history information 149, and learning data 150, all of which are information. Note that the client 200 may hold a part or all of the information held by the evaluation server 100.

[0019] The account information 131 includes information related to the account such as the user's attributes. The intervention service information 132 is master information of a service (hereinafter simply referred to as an intervention service) that can intervene with the user and activate the user's activity. The location information 133 indicates a time series of the user's location. The activity amount information 134 indicates a time series of the user's activity amount. The behavior purpose information 135 indicates keywords related to the behavior purpose.

[0020] Map information 136 is an example of a POI (Point Of Interest) dictionary, and indicates the location of spots. Timeline information 137 indicates a time series of user actions. Activity ratio information 138 indicates an activity ratio calculated by the activity ratio calculation unit 112. Exchange diversity information 139 indicates exchange diversity calculated by the exchange diversity calculation unit 113. Exercise information 140 indicates exercise calculated by the exercise calculation unit 114.

[0021] The behavior evaluation information 141 indicates the behavior evaluation result of the user evaluated by the behavior evaluation unit 116. The care-needing risk rank determination information 142 is information for determining the care-needing risk rank from the care-needing risk. The total score rank determination information 143 is information for determining the total score rank from the total score.

[0022] The user aggregated evaluation information 144 indicates statistics of the behavior evaluation results of all users. The behavior tendency information 145 indicates the user's tendency to compare with the same sex and age group regarding the user's risk of needing nursing care and activity (i.e., the tendency to compare with a group of users with the same attributes) and the user's own tendency. The reference part information 146 indicates parts to be inserted into a reference template 147 for the user's tendency to compare with the same sex and age group regarding the user's risk of needing nursing care and activity and the user's own tendency.

[0023] The reference template 147 is a template for generating individual advice to be presented to the user. The inverse simulation result information 148 indicates the improvement values ​​and improvement amounts of activity ratio, interaction diversity, and exercise required to improve the user's care-needing risk rank or standard score rank. The intervention service usage history information 149 indicates the user's usage history of the intervention service. The learning data 150 is learning data for generating a care-needing risk prediction model.

[0024] The client 200 includes, for example, an information transmitting / receiving unit 211 which is a functional unit. The information transmitting / receiving unit 211 transmits and receives information to and from the evaluation server 100.

[0025] In this embodiment, the behavior evaluation process and the advice generation process are executed based on at least one of the care-needing risk (absolute evaluation) and the standard score (relative evaluation). In other words, either the care-needing risk or the standard score need not be calculated (both may be calculated). When the care-needing risk is not calculated, the assessment server 100 does not need to include the care-needing risk prediction unit 115, and the information held by the assessment server 100 does not need to include items related to the care-needing risk. When the standard score is not calculated, the assessment server 100 does not need to include the standard score calculation unit 120, and the information held by the assessment server 100 does not need to include items related to the standard score and the total score.

[0026] 2 is a block diagram showing an example of the configuration of a computer constituting each of the evaluation server 100 and the client 200. The computer 300 is configured by a computer having, for example, a CPU (Central Processing Unit) 101, a memory 102, an auxiliary storage device 103, an input device 104, an output device 105, and a communication device 106.

[0027] The CPU 101 includes a processor and executes programs stored in the memory 102. The memory 102 includes a ROM (Read Only Memory), which is a non-volatile storage element, and a RAM (Random Access Memory), which is a volatile storage element. The ROM stores immutable programs (e.g., a Basic Input / Output System (BIOS)). The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the CPU 101 and data used when the programs are executed.

[0028] The auxiliary storage device 103 is a large-capacity non-volatile storage device such as a magnetic storage device (HDD (Hard Disk Drive)) or a flash memory (SSD (Solid State Drive)), and stores programs executed by the CPU 101 and data used when the programs are executed. That is, the programs are read from the auxiliary storage device 103, loaded into the memory 102, and executed by the CPU 101.

[0029] The input device 104 is a device such as a keyboard or a mouse that receives input from an operator. The output device 105 is a device such as a display device or a printer that outputs the results of program execution in a format that can be viewed by the operator.

[0030] The communication device 106 is a network interface device that controls communication with other devices in accordance with a predetermined protocol, and includes a serial interface such as a Universal Serial Bus (USB).

[0031] A part or all of the programs executed by the CPU 101 may be provided to the computer 300 from a removable medium (such as a CD-ROM or a flash memory) which is a non-transitory storage medium, or from an external computer equipped with a non-transitory storage device via a network, and may be stored in the non-volatile auxiliary storage device 103 which is a non-transitory storage medium. For this reason, the computer 300 may have an interface for reading data from the removable medium.

[0032] Each of the evaluation server 100 and the client 200 is a computer system configured on a single physical computer or on multiple logically or physically configured computers, and may operate in separate threads on the same computer, or may operate on a virtual computer constructed on multiple physical computer resources.

[0033] 1 is included in a CPU 101 of a computer 300 constituting an apparatus including the functional units. For example, the CPU 101 included in the computer 300 constituting the evaluation server 100 functions as a timeline tallying unit 111 by operating according to a timeline tallying program loaded into a memory 102 included in the computer 300, and functions as an activity ratio calculation unit 112 by operating according to an activity ratio calculation program loaded into the memory 102.

[0034] In addition, some or all of the functions of the functional units included in the CPU 101 of the computer 300 constituting each device included in the behavioral evaluation system may be realized by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array).

[0035] 1 is stored in the auxiliary storage device 103 of the computer 300 constituting the device. Note that some or all of the information stored in the auxiliary storage device 103 of the computer 300 constituting the device included in the behavior evaluation system may be stored in the memory 102 of the computer 300, or may be stored in a database connected to the device and external to the behavior evaluation system.

[0036] In this embodiment, the information used by the behavior evaluation system does not depend on the data structure and may be expressed in any data structure. In this embodiment, the information is expressed in a table format, but the information can be stored in a data structure appropriately selected from, for example, a list, a database, or a queue.

[0037] Although not shown in the figure, the computer 300 constituting the client 200 is equipped with a sensor (e.g., a GPS (Global Positioning System)) that measures the user's location information, and a sensor that measures the user's activity level, such as the number of steps, heart rate, or calories burned.

[0038] 3 is a diagram showing an example of the data configuration of account information 131. The account information 131 includes, for example, a user ID column 1311, a name column 1312, a date of birth column 1313, a gender column 1314, and an address column 1315. The user ID column 1311 holds a user ID for identifying a user. The name column 1312 holds information indicating the name of the user.

[0039] A date of birth column 1313, a gender column 1314, and an address column 1315 hold information indicating the user's date of birth, gender, and address, respectively, and all of this information is an example of user attribute information.

[0040] 4 is a diagram showing an example of a data configuration of the intervention service information 132. The intervention service information 132 holds, for example, an intervention service ID column 1321, a name column 1322, a summary column 1323, a type column 1324, an event frequency column 1325, a venue column 1326, and a recommended amount of exercise column 1327.

[0041] An intervention service ID for identifying an intervention service is stored in an intervention service ID column 1321. A name column 1322, a summary column 1323, and a type column 1324 respectively store information indicating the name, summary, and type of the intervention service.

[0042] The frequency of holding field 1325 holds information indicating the frequency of holding the intervention service, and the frequency of holding indicates an expected improvement value of the activity ratio. The location of holding field 1326 holds information indicating the location and number of locations of the intervention service, and the number of locations indicates an expected improvement value of the interaction diversity. The recommended amount of exercise field 1327 holds information indicating a recommended amount of exercise per session when the user participates in the intervention service, and the recommended amount of exercise indicates an expected improvement value of the exercise.

[0043] 5 is a diagram showing an example of the data configuration of the location information 133. The location information 133 includes, for example, a user ID column 1331, a time stamp column 1332, a point column 1333, a stay / movement category column 1334, and a means of transportation column 1335. The user ID column 1331 holds a user ID. The time stamp column 1332 holds information indicating the time when the user was at the location. Note that the sampling interval of each user's location indicated by the time stamp column 1332 (i.e., the sampling interval at which each client 200 acquires location information) may be constant or may not be constant depending on the implementation of the client 200 (for example, location information is not acquired when the battery of the client 200 is in power saving mode, etc.).

[0044] The points column 1333 holds information indicating the location where the user was at the relevant time. The stay / movement category column 1334 holds a stay / movement category indicating whether the user was staying at the relevant location at the relevant time or moving around the location. The transportation means column 1335 holds information indicating the transportation means when the stay / movement category is "movement" (when the stay / movement category is "stay", "N / A" (Not / Applicable) is stored).

[0045] 6 is a diagram showing an example of a data configuration of the activity amount information 134. The activity amount information 134 includes, for example, a user ID column 1341, a start time column 1342, an end time column 1343, and a step count column 1344. The user ID column 1341 holds a user ID. The start time column 1342 and the end time column 1343 hold information indicating the start time and end time, respectively, of the measurement of the activity amount indicated by the record.

[0046] The step count field 1344 holds information indicating the number of steps in the behavior indicated by the record. Note that the number of steps is an example of the amount of activity, and the activity amount information 134 may store information indicating other amounts of activity (e.g., heart rate or calories burned) instead of or in addition to the number of steps.

[0047] 7 is a diagram showing an example of the data configuration of the action purpose information 135. The action purpose information 135 includes, for example, an action purpose column 1351 and a keyword column 1352. The action purpose column 1351 holds information indicating an action purpose. The keyword column 1352 holds information indicating a keyword related to the action purpose.

[0048] 8 is a diagram showing an example of the data configuration of the map information 136. The map information 136 includes, for example, a spot ID column 1361, a spot type column 1362, a spot name column 1363, and a coordinate column 1364. The spot ID column 1361 holds a spot ID for identifying a spot. The spot type column 1362 holds information indicating the type of the spot. The spot name column 1363 holds information indicating the name of the spot. The coordinate column 1364 holds information indicating the coordinates of a representative position of the spot.

[0049] 9 is a diagram showing an example of the data configuration of the timeline information 137. The timeline information 137 includes, for example, a user ID column 1371, a start time column 1372, an end time column 1373, a coordinate column 1374, a spot type column 1375, a spot name column 1376, an activity purpose column 1377, a stay / travel category column 1378, a means of transportation column 1379, and a step count column 13710.

[0050] The user ID column 1371 holds a user ID. The start time column 1372 and end time column 1373 hold information indicating the start time and end time, respectively, of the activity indicated by the record. The coordinate column 1374, spot type column 1375, spot name column 1376, and activity purpose column 1377 hold information indicating the coordinates where the user stayed, the type of spot, the name of the spot, and the activity purpose of the spot, respectively, when the stay / movement category is "stay" (however, when the stay / movement category is "movement", "N / A" is stored in these columns).

[0051] The stay / movement category column 1378 holds the stay / movement category. The transportation means column 1379 holds information indicating the transportation means when the stay / movement category is "movement" (when the stay / movement category is "stay", "N / A" is stored). The number of steps column 13710 holds information indicating the number of steps in the behavior indicated by the record. Note that the timeline information 137 may store information indicating other activity amounts (e.g., heart rate or calories burned) instead of or in addition to the number of steps.

[0052] The user's behavior at a spot indicated by a record in which the stay / movement category in the timeline information 137 is "stay" is included in the activity. On the other hand, the user's behavior (movement by a means of transportation) indicated by a record in which the stay / movement category in the timeline information 137 is "movement" may or may not be included in the activity (hereinafter, movement is not included in the activity).

[0053] FIG. 10 is a diagram showing an example of the data configuration of the activity ratio information 138. The activity ratio information 138 includes, for example, a user ID column 1381, a counting year / month / week column 1382, and a number of times going out column 1383. The user ID column 1381 holds a user ID. The counting year / month / week column 1382 holds information indicating the week for which the number of times going out is counted. In the example of FIG. 10, the number of times going out for each user is counted on a weekly basis, but the time unit of counting is not limited to weeks. This also applies to the time unit of counting for other information (however, it is desirable that the time unit of counting is the same for all information). The number of times going out column 1383 holds information indicating the number of times the user went out in the week.

[0054] 11 is a diagram showing an example of a data configuration of the interaction diversity information 139. The interaction diversity information 139 includes, for example, a user ID column 1391, a counting year / month / week column 1392, a number of places stayed column 1393, an athletic hobby column 1394, a humanities hobby column 1395, an employment column 1396, a volunteer column 1397, and an other column 1398.

[0055] The user ID column 1391 holds a user ID. The counting year / month / week column 1392 holds information indicating the week for which the interaction diversity is counted. The number of places visited column 1393 holds information indicating the total number of places visited by the user for the activity of the week. The athletic hobby column 1394, the liberal arts hobby column 1395, the work column 1396, the volunteer column 1397, and the other column 1398 hold the number of places visited by the user for the activity of the week for each purpose of activity.

[0056] 12 is a diagram showing an example of the data configuration of the exercise information 140. The exercise information 140 includes, for example, a user ID column 1401, a counting year / month / week column 1402, and an average number of steps column 1403. The user ID column 1401 holds a user ID. The counting year / month / week column 1402 holds information indicating the week for which the interaction diversity is counted. The average number of steps column 1403 holds information indicating the average number of steps per day for the user's activity in the week.

[0057] 13 is a diagram showing an example of the data configuration of the behavior evaluation information 141. The behavior evaluation information 141 holds, for example, a compilation year / month / week column 1411, a user ID column 1412, a care-needing risk column 1413, a care-needing risk rank column 1414, a number of times going out deviation value column 1415, a number of places stayed deviation value column 1416, an average number of steps deviation value column 1417, a total score column 1418, and a total score rank column 1419.

[0058] The aggregation year / month / week column 1411 holds information indicating the week for which the behavior evaluation is aggregated. The user ID column 1412 holds the user ID. The care-needing risk column 1413 holds information indicating the care-needing risk of the user. The care-needing risk rank column 1414 holds information indicating the care-needing risk rank (class) of the user indicated by the record among all users.

[0059] The number of times going out standard deviation column 1415, the number of places stayed standard deviation column 1416, and the average number of steps standard deviation column 1417 respectively hold information indicating the standard deviation of the number of times the user went out in the week, the standard deviation of the number of places stayed in the week, and the standard deviation of the average number of steps (per day) in the week for the user, with the entire user population as the population.

[0060] The total score column 1418 holds information indicating the total score. The total score is calculated, for example, by a standard deviation (for example, a weighted average value of these standard deviations) obtained by combining the standard deviations of the number of times the user went out during the week, the number of places the user stayed, and the average number of steps taken (per day). The total score rank column 1419 holds information indicating the rank (class) of the total score of the user indicated by the record among all users.

[0061] 14 is a diagram showing an example of the data configuration of the care need risk rank determination information 142. The care need risk rank determination information 142 includes, for example, a care need risk section column 1421 and a care need risk rank column 1422. Depending on the correspondence between the section indicated by the care need risk section column 1421 and the care need risk rank indicated by the care need risk rank column 1422, it is possible to determine the care need risk rank from the value of the care need risk.

[0062] 15 is a diagram showing an example of the data configuration of the total score rank determination information 143. The total score rank determination information 143 includes, for example, a total score interval column 1431 and a total score rank column 1432. The total score rank can be determined from the total score value according to the correspondence between the interval indicated by the total score interval column 1431 and the total score rank indicated by the total score rank column 1432.

[0063] 14 and 15, the nursing care risk rank and the total score rank are defined as four ranks from A to D (A being the best rank and D being the worst rank), but the number of ranks is not limited to four (it is sufficient if there are multiple ranks). Furthermore, the thresholds (upper and lower limits) of each section for determining the rank are not limited to the examples of FIG. 14 and FIG. 15.

[0064] 16 is a diagram showing an example of the data configuration of user aggregated evaluation information 144. The user aggregated evaluation information 144 includes, for example, an aggregated year / month / week column 1441, a gender column 1442, an age class column 1443, a number of times going out column 1444, a number of places stayed column 1445, an average number of steps column 1446, and a care-needing risk column 1447. The aggregated year / month / week column 1441 holds information indicating the week for which the user aggregated evaluation is to be aggregated. The gender column 1442 holds information indicating the gender. The age class column 1443 holds information indicating the age class.

[0065] In the example of Fig. 16, users are grouped by gender and age class, but other attribute information for grouping users may be used in addition to or in addition to gender and age class. Also, in the example of Fig. 16, age classes are grouped in 5-year increments, but they may be grouped in any age increments.

[0066] The number of times going out column 1444, number of places stayed column 1445, average number of steps column 1446, and care need risk column 1447 respectively hold statistical information on the number of times going out, number of places stayed, average number of steps, and care need risk of a user of the relevant gender and age group in the relevant week (for example, statistical quantities such as the average, minimum, and maximum of the number of times going out, number of places stayed, average number of steps, and care need risk of a user of the relevant gender and age group in the relevant week, as well as statistical quantities for classifying into the first quartile, second quartile, third quartile, etc.).

[0067] 17 is a diagram showing an example of the data configuration of the behavioral tendency information 145. The behavioral tendency information 145 includes, for example, a user ID column 1451, a counting year / month / week column 1452, a care-needing risk column 1453, a number of times going out column 1454, a number of places stayed column 1455, and an average number of steps column 1456. The user ID column 1451 holds a user ID, and the counting year / month / week column 1452 holds information indicating the week for which the behavioral tendency is to be counted.

[0068] The nursing care risk column 1453, the number of times going out column 1454, the number of places stayed column 1455, and the average number of steps column 1456 respectively hold information indicating the comparative tendency of the user in terms of the nursing care risk, the number of times going out, the number of places stayed, and the average number of steps for the week in question with the same sex and age (same age class), and the user's own tendency (for example, the comparative tendency with the week prior to the week in question).

[0069] 18 is a diagram showing an example of a data configuration of the reference parts information 146. The reference parts information 146 includes, for example, a trend column 1461 and a parts column 1462. The trend column 1461 holds columns showing whether the tendencies of the same sex and age for the risk of needing care, the number of outings, the number of places of stay (total), and the average number of steps are good or bad (A is the best risk of needing care and D is the worst, and the higher the value for the number of outings, the number of places of stay (total), and the average number of steps, the better), and columns showing whether the self-term tendencies for the risk of needing care, the number of outings, the number of places of stay (total), and the average number of steps are improving or worsening.

[0070] The parts column 1462 holds information indicating parts to be inserted into the text of the reference template 147 for each tendency of the same sex and age group and for each personal tendency regarding the risk of needing care, number of times going out, number of places stayed (total), and average number of steps.

[0071] 19 is an example of reference template 147. Reference template 147 indicates text to be displayed as individual advice. Individual advice is generated by inserting parts extracted from reference parts information 146 into each of (1) to (8) of reference template 147.

[0072] 20 is a diagram showing an example of a data configuration of the inverse simulation result information 148. The inverse simulation result information 148 includes, for example, a user ID column 1481, a counting year / month / week column 1482, an improvement value of the number of outings column 1483, an improvement range of the number of outings column 1484, an improvement value of the number of places of stay column 1485, an improvement range of the number of places of stay column 1486, an improvement value of the average number of steps column 1487, and an improvement range of the average number of steps column 1488.

[0073] The user ID column 1481 holds a user ID. The aggregation year / month / week column 1482 holds information indicating the week to be aggregated for the inverse simulation. The improvement value of the number of times going out column 1483, the improvement range of the number of times going out column 1484, the improvement value of the number of places of stay column 1485, the improvement range of the number of places of stay column 1486, the improvement value of the average number of steps column 1487, and the improvement range of the average number of steps column 1488 hold information indicating the improvement value and improvement range of the number of times going out, the number of places of stay, and the average number of steps for the user to improve the care need risk rank for the week.

[0074] 21 is a diagram showing an example of a data configuration of the intervention service usage history information 149. The intervention service usage history information 149 includes, for example, a user ID column 1491 and an intervention service reference ID column 1492. The user ID column 1491 holds a user ID. The intervention service reference ID column 1492 holds a flag indicating the usage history of the user of the intervention service indicated by each intervention service ID.

[0075] For example, if a user has used a certain intervention service, a "1" is stored in the column corresponding to the intervention service ID of the user, if a certain intervention service has been recommended to the user but has not been used, a "0" is stored in the column corresponding to the intervention service ID of the user, and if a certain intervention service has not been recommended to the user and has not been used, a "-" is stored in the column corresponding to the intervention service ID of the user. Note that the intervention service reference ID column 1492 may store the number of times the user has used the intervention service.

[0076] 22 is a diagram showing an example of the data configuration of the learning data 150. The learning data 150 includes, for example, a user ID field 1501, an age field 1502, a gender field 1503, a residential area field 1504, a number of times going out field 1505, a number of places stayed field 1506, an athletic hobby field 1507, a cultural hobby field 1508, an employment field 1509, a volunteer field 15010, a miscellaneous field 15011, an average number of steps field 15012, and a field 15013 showing whether or not the user has been certified as needing nursing care within the past T years.

[0077] The user ID column 1501 holds the user ID of the user for learning the nursing care risk prediction model. The age column 1502, gender column 1503, and residential area column 1504 hold information indicating the attributes (age, gender, and residential area) of the user in the reference week of the nursing care risk prediction model learning. The number of times going out column 1505, the number of places stayed column 1506, and the average number of steps column 15012 hold information indicating the number of times going out (activity ratio), the number of places stayed (interaction diversity), and the average number of steps (amount of exercise) of the user in the reference week of the nursing care risk prediction model learning, respectively.

[0078] The athletic hobby column 1507, cultural hobby column 1508, work column 1509, volunteer column 15010, and other column 15011 hold the number of places the user stayed in for the user's activities in the reference week of the nursing care risk prediction model learning, for each purpose of action. The nursing care certification within T years column 15013 holds information indicating whether the user was certified as needing nursing care within T years (e.g., 3 years) from the reference week of the nursing care risk prediction model learning.

[0079] In the example of Figure 22, the information indicated in the age column 1502, gender column 1503, residential area column 1504, number of times going out column 1505, number of places stayed column 1506, athletic hobbies column 1507, cultural hobbies column 1508, employment column 1509, volunteer column 15010, other column 15011, and average number of steps column 15012 are values ​​to be substituted for independent variables in the risk prediction model, and the information indicated in the column indicating whether or not nursing care is required within T years 15013 is a value to be substituted for dependent variables in the risk prediction model.

[0080] The following describes the evaluation process by the evaluation server 100. Before the evaluation process starts, the following information is registered in advance: account information 131, intervention service information 132, location information 133, activity amount information 134, behavioral purpose information 135, map information 136, care need risk rank determination information 142, total score rank determination information 143, reference part information 146, and reference template 147.

[0081] For example, the account information 131 is registered according to an input from a user to each client 200, and the position information 133 and the activity amount information 134 are registered according to information measured by each client 200. In addition, for example, the intervention service information 132, the map information 136, the care need risk rank determination information 142, the total score rank determination information 143, the reference parts information 146, and the reference template 147 are registered based on an input from an administrator of the evaluation server 100, for example.

[0082] 23 is a flowchart showing an example of the evaluation process. The process described below is executed, for example, for each user registered in the account information 131. The timeline counting unit 111 executes the timeline counting process (S100).

[0083] The activity proportion calculation unit 112 executes an activity proportion calculation process (S200). The exchange diversity calculation unit 113 executes an exchange diversity calculation process (S300). The motion calculation unit 114 executes a motion calculation process (S400). Note that the order of execution of the activity proportion calculation process in step S200, the exchange diversity calculation process in step S300, and the motion calculation process in step S400 does not matter, and they may be executed in parallel.

[0084] The behavior evaluation unit 116 executes a behavior evaluation process (S500). The advice generation unit 117 executes an advice generation process (S600). The recommendation processing unit 118 executes a recommendation process (S700), and the evaluation process ends. The recommendation processing unit 118 may receive a recommendation request from the client 200 and execute the recommendation process only for the user who uses the client 200. Details of each process described in FIG. 23 will be described later with reference to FIGS. 24 to 30.

[0085] FIG. 24 is a flowchart showing an example of the timeline tallying process in step S100. The timeline tallying unit 111 refers to the location information 133 to identify the time at which the stay / movement category of the target user of the timeline tallying process changes. The timeline tallying unit 111 handles a time period in which the target user's stay / movement category "stay" is continuous as one stay segment, and a time period in which the target user's stay / movement category "movement" is continuous as one movement segment. The timeline tallying unit 111 determines the identified time as the end time of the stay segment or movement segment that ends at the identified time, and the start time of the stay segment or movement segment that starts from the identified time (S101). That is, the time identified in step S101 is the time at which the movement segment and the stay segment switch.

[0086] The timeline tallying unit 111 refers to the location information 133 to identify a point in the section of stay of the target user (for example, the average position of all points in the section of stay, or a point at the middle time of the section of stay, etc.), and refers to the map information 136 to extract the spot type and spot name of the spot having the coordinates closest to the identified point (S102). Note that the timeline tallying unit 111 may receive the spot type and spot name from the client 200 (for example, the client 200 accepts input from the user), in which case the process of step S102 is omitted.

[0087] The timeline counting unit 111 uses the extracted spot type and spot name as a search key to extract a behavior purpose from the behavior purpose information 135 (S103). Specifically, the timeline counting unit 111 identifies a behavior purpose in the behavior purpose information 135 that corresponds to a keyword included in the extracted spot type and spot name.

[0088] The timeline counting unit 111 acquires the number of steps in each stay section and each movement section of the target user from the activity amount information 134 (S104). The timeline counting unit 111 registers the start time and end time identified in step S101, the spot type and spot name identified in step S102, the purpose of the action identified in step S103, the number of steps identified in step S104, the point (coordinates) of the spot, the stay movement / category ("stay" for a stay section, "movement" for a movement section), and the means of transportation indicated by the location information 133, for each stay section and movement section identified in step S101, in the timeline information 137, and ends the timeline counting process.

[0089] Alternatively, each client 200 may generate a timeline for each user. In this case, the evaluation server 100 registers the timeline for each user received from each client 200 in the timeline information 137, and the timeline aggregation process in step S100 is omitted.

[0090] 25 is a flowchart showing an example of the activity ratio calculation process in step S200. The activity ratio calculation unit 112 sets the aggregation year, month, and week as the aggregation period (S201). The activity ratio calculation unit 112 refers to the timeline information 137 and counts the number of times the target user of the activity ratio calculation process goes out during the aggregation period (S202).

[0091] Specifically, for example, the activity ratio calculation unit 112 regards the target user's departure from home as going out. Note that, for example, the activity ratio calculation unit 112 refers to the location information 133 and determines the most frequent value of the target user's stay location during a predetermined time period (e.g., 12:00 a.m. to 4:00 a.m.) within a predetermined period (e.g., within the last month) as the home coordinates of the target user. In addition, the client 200 may transmit the home coordinates input by the target user to the evaluation server 100, so that the activity ratio calculation unit 112 acquires the home coordinates of the target user.

[0092] In addition, when the target user goes out multiple times on the same day, it is preferable that the activity ratio calculation unit 112 counts it as one outing in order to prevent the user from staying indoors. On the other hand, the activity ratio calculation unit 112 may count the total number of times the target user goes out (that is, when the target user goes out multiple times on the same day, the multiple outings are counted).

[0093] The activity ratio calculation unit 112 registers the total number of times that the target user went out per week, counted in step S202, in the activity ratio information 138 (S203), and ends the activity ratio calculation process.

[0094] 26 is a flowchart showing an example of the interaction diversity calculation process in step S300. The interaction diversity calculation unit 113 sets the aggregation year, month, and week as the aggregation period (S301). The interaction diversity calculation unit 113 refers to the timeline information 137 and counts the number of spots (total number of stay locations) where the target user of the interaction diversity calculation process stayed during the aggregation period and the number of each behavior purpose corresponding to the spots (number of stay locations for each behavior purpose) (S302).

[0095] In addition, when the target user stays at the same spot multiple times during the collection period, it is preferable that the interaction diversity calculation unit 113 counts it as one stay location (because it is highly likely that the "diversity" of the interaction is not improved whether the target user stays there once or multiple times). In addition, the interaction diversity calculation unit 113 may count the target user's stay at the same spot multiple times during the collection period as multiple stay locations.

[0096] The interaction diversity calculation unit 113 registers the total number of stay locations for the target user for the aggregation year, month, and week counted in step S303 and the number of stay locations for each behavioral purpose in the interaction diversity information 139 (S303), and terminates the interaction diversity calculation process.

[0097] 27 is a flowchart showing an example of the exercise calculation process in step S400. The exercise calculation unit 114 sets the collection year, month, and week as the collection period (S401). The exercise calculation unit 114 refers to the timeline information 137, counts the number of steps of the target user of the exercise calculation process during the collection period, and calculates the average number of steps per day of the target user during the collection period (S402).

[0098] In step S402, the exercise calculation unit 114 may count only the number of steps for which the stay / movement category of the target user in the aggregation period is “stay”, or may also count the number of steps for which the stay / movement category is “movement” (i.e., at least exercise during a “stay” is counted).

[0099] The exercise calculation unit 114 registers the average number of steps per day for the target user in the collection period calculated in step S402 in the exercise information 140 (S403), and ends the exercise calculation process.

[0100] Fig. 28A is a flowchart showing an example of the behavior evaluation process in step S500. A care-needing risk is used for the behavior evaluation in Fig. 28A. The care-needing risk prediction unit 115 calculates the care-needing risk of the target user of the behavior evaluation process by using a care-needing risk prediction model (S501).

[0101] The nursing care need risk prediction model is stored in advance, for example, in the memory 102 or the auxiliary storage device 103 of the evaluation server 100. The nursing care need risk y is expressed, for example, as y=f (user attribute, number of times going out, number of places stayed, average number of steps) (f is the nursing care need risk prediction model). Examples of user attributes included in the independent variables of f include age, sex, and / or residential area. Also, examples of the number of places stayed included in the independent variables of f include the number of places stayed for each purpose of action. For example, the nursing care need risk is higher with age, higher for women than for men, and tends to be lower with the number of times going out, number of places stayed, and average number of steps.

[0102] The nursing care risk prediction model may be expressed as a multiple regression model. For example, the nursing care risk y is expressed as y=(b0, b1, . . . , b n ) (age, sex, residential area, number of times going out, number of places to stay for sports, number of places to stay for cultural and sports, , average number of steps) T It is expressed as +ε.

[0103] The behavior evaluation unit 116 refers to the care risk rank determination information 142 to determine a care risk rank corresponding to the care risk of the target user, and registers the care risk and care risk rank of the target user for the aggregation period in the behavior evaluation information 141 (S502).

[0104] The behavior evaluation unit 116 calculates statistics (average, minimum, maximum, and quartile) of each indicator (number of times going out, number of places stayed, and average number of steps) for the combination of age class and gender to which the target user belongs during the target period, registers the calculated statistics in the user aggregated evaluation information 144 (S503), and terminates the behavior evaluation process in which the risk of needing care is used.

[0105] Fig. 28B is a flowchart showing an example of the behavior evaluation process in step S500. Standard scores are used for the behavior evaluation in Fig. 28B. The standard score calculation unit 120 calculates the standard score (S511).

[0106] Specifically, for example, the standard score calculation unit 120 calculates the standard scores for the activity ratio, interaction diversity, and exercise, based on the standard scores for the number of times the target user went out, the number of places where the user stayed, and the average number of steps taken during the target period, when the entire user population is used. Note that the standard score calculation unit 120 may use other quantities (e.g., z-scores) indicating the position in the overall distribution instead of the standard scores when calculating the standard scores.

[0107] The standard score calculation unit 120 calculates the total score (S512). Specifically, for example, the standard score calculation unit 120 calculates a weighted average (for example, weights are given in advance) of the standard scores (deviation values) calculated in step S511 as the total score.

[0108] The behavior evaluation unit 116 refers to the overall score rank determination information 143 to determine the overall score rank corresponding to the overall score of the target user, and registers the standard scores, overall word count, and overall score rank of the target user for the aggregation period in the behavior evaluation information 141 (S513).

[0109] The behavior evaluation unit 116 calculates statistics (average, minimum, maximum, and quartiles) of each indicator (number of times going out, number of places stayed, and average number of steps) for the combination of age class and gender to which the target user belongs during the target period, registers the calculated statistics in the user aggregated evaluation information 144 (S514), and terminates the behavior evaluation process in which the standard score is used.

[0110] In addition, the behavior evaluation process of both Fig. 28A and Fig. 28B may be executed, or the behavior evaluation process of either Fig. 28A or Fig. 28B may be executed. When the behavior evaluation process of both Fig. 28A and Fig. 28B is executed, the process of either step S503 or step S514 is omitted.

[0111] Fig. 29A, Fig. 29B, and Fig. 29C are flowcharts showing an example of advice generation processing. Fig. 29B is a continuation process of Fig. 29A, and since the risk of needing care is used for advice generation in Fig. 29B, the processing of Fig. 28A must be executed in order to execute the processing of Fig. 29B. Fig. 29C is a continuation process of Fig. 29A, and since the standard score is used for advice generation in Fig. 29C, the processing of Fig. 28B must be executed in order to execute the processing of Fig. 29C. In addition, both the behavior evaluation processing of Fig. 29A and Fig. 29B may be executed, or the behavior evaluation processing of either Fig. 29A or Fig. 29B may be executed.

[0112] The advice generation unit 117 calculates the behavioral tendencies of the target user of the advice generation process (comparison tendencies with users of the same sex and age and personal tendencies for each of the risk of needing care, number of times going out, number of places stayed, and average number of steps) and registers the behavioral tendencies in the behavioral tendency information 145 (S601).

[0113] For example, in step S601, the advice generation unit 117 determines whether the risk of needing care, number of times going out, number of places stayed, and average number of steps for the target user indicated by the behavior evaluation information 141 during the aggregation period are greater than or equal to the risk of needing care, number of times going out, number of places stayed, and average number of steps for the same sex and age (same age class) of the target user indicated by the user aggregation evaluation information 144 during the aggregation period.

[0114] Specifically, for example, if the advice generation unit 117 determines that the risk of needing care of the target user for the aggregation period indicated by the behavior evaluation information 141 is equal to or higher than the risk of needing care of the target user of the same sex and age (same age class) for the aggregation period indicated by the user aggregate evaluation information 144, it determines that the target user's tendency to compare the risk of needing care with users of the same sex and age is "bad", and if it determines that this is not the case, it determines that the target user's tendency to compare the risk of needing care with users of the same sex and age is "good".

[0115] Also, for example, when the advice generating unit 117 determines that the number of times the target user goes out during the aggregation period indicated by the behavior evaluation information 141 is equal to or greater than the number of times the target user goes out during the aggregation period of the same sex and age (same age class) indicated by the user aggregate evaluation information 144, it determines that the target user's tendency to compare the number of times he goes out with others of the same sex and age is "good", and when it determines that this is not the case, it determines that the target user's tendency to compare the number of times he goes out with others of the same sex and age is "bad". The method of calculating the tendency to compare the number of places stayed and the average number of steps with others of the same sex and age is the same as the method of calculating the tendency to compare the number of times he goes out with others of the same sex and age.

[0116] Note that the advice generating unit 117 may use each deviation value of the target user for the collection period indicated by the behavior evaluation information 141 to determine the tendency to compare with users of the same sex and age group, instead of comparing the average value between the target user and users of the same sex and age group.

[0117] Also, for example, if the advice generation unit 117 determines that the target user's risk of needing care for the current aggregation period indicated by the behavior evaluation information 141 is equal to or higher than the target user's risk of needing care for the previous aggregation period, it determines that the target user's self-tendency of needing care risk is “worsening,” and if it determines that this is not the case, it determines that the target user's self-tendency of needing care risk is “improving.”

[0118] Also, for example, when the advice generating unit 117 determines that the number of times the target user went out during the current counting period indicated by the behavior evaluation information 141 is equal to or greater than the number of times the target user went out during the previous counting period, it determines that the target user's self-tendency of the number of times he went out is "improved", and when it determines that this is not the case, it determines that the target user's self-tendency of the number of times he went out is "deteriorated". The method of calculating the self-tendency of the number of staying places and the average number of steps is the same as the method of calculating the self-tendency of the number of times he went out.

[0119] In addition, instead of comparing the indicators of the target user's current collection period with those of the previous collection period, the advice generation unit 117 may use the slope of the regression coefficient of the indicators for a specified period including the current collection period to determine whether the target user's self-tendency has improved or worsened.

[0120] The advice generating unit 117 extracts parts from the reference parts information 146 using the behavioral tendency of the target user calculated in step S601 as a key (S602). Specifically, in the example of the reference parts information 146 in Fig. 18, if the target user's tendency of needing nursing care risk compared with the same sex and age is "good", "relatively active" is extracted as part (1), and "can lower the risk of needing nursing care" is extracted as part (2). In this way, the advice generating unit 117 extracts parts from the reference parts information 146 that correspond to the same sex and age tendency and the self tendency of the target user's risk of needing nursing care, number of times going out, number of places stayed, and average number of steps.

[0121] The advice generating unit 117 generates an individual advice text by inserting each of the extracted parts into the corresponding position in the text of the reference template 147 (S603). Note that when the care-needing risk prediction is not performed (when the behavior evaluation process of FIG. 28A is not performed), the process regarding the care-needing risk is omitted in steps S601 to S603.

[0122] The process in Fig. 29B will be described. Following step S603, the advice generating unit 117 calculates the difference between the upper limit value of the care need risk section that is one rank higher than the care need risk rank to which the target user belongs and the current care need risk value of the target user (S611).

[0123] 14, if the target user's care need risk is 30, the care need risk rank is B, and the upper limit of the care need risk section for rank A, which is one rank better than care need risk rank B, is 20, so the difference calculated in step S611 is 30-20=10. Note that when the target user's care need risk rank is A (the best rank), the processing in FIG. 29B may be omitted.

[0124] The care-needing risk prediction unit 115 executes the processes of steps S612, S613, and S614 described below for each of the indexes (number of times going out, number of places stayed, and average number of steps) of the target user.

[0125] The care need risk prediction unit 115 sets the current values ​​of indicators other than the target indicator in the loop to the care need risk prediction model, and performs an inverse simulation to calculate the improvement value (improved value) of the target indicator and the improvement range (from the current value) required to obtain a care need risk rank that is one level better (S612).

[0126] Specifically, for example, if the target indicator in the loop is the number of times going out and the risk of needing care is y=f (user attributes, number of times going out, number of places stayed, average number of steps), the care need risk prediction unit 115 sets the current values ​​of the user attributes, number of places stayed, and average number of steps, which are indicators other than the target indicator in the loop, in the care need risk prediction model, and identifies the improvement value and improvement range of the number of times going out to obtain a care need risk that is one rank better than the current care need risk rank.

[0127] The care-needing risk prediction unit 115 rounds up the improvement value and improvement range according to the data type of the target index within the range of the domain of the target index (S613). For example, if the current value of the target index, the number of times going out, is 3 times, the improvement value calculated in step S613 is 4.8 times (i.e., the improvement range is 4.8-3=1.8 times), the domain of the number of times going out is 0 to 7 inclusive, and the data type of the number of times going out is an integer, the care-needing risk prediction unit 115 rounds up the improvement value to 5 times (i.e., the improvement range is 2 times). Note that the domain and data type of each index (number of times going out, number of places stayed, and average number of steps) are predetermined.

[0128] The care-needing risk prediction unit 115 stores the improvement value (rounded up as necessary) and the improvement range of the target index of the target user in the collection period in the inverse simulation result information 148 (S614).

[0129] In addition, if the improvement value of the index calculated in step S614 exceeds the domain, the advice display screen described below may display, for example, the upper limit of the domain instead of the improvement value, or a message such as "The rank will not change, but if you make improvements, you can reduce the risk of needing care. Let's start with what you can do."

[0130] Although the care-needing risk prediction section 115 executes an inverse simulation for each target index in step S613, it may execute an inverse simulation by combining a plurality of target indexes (for example, by mathematical programming).

[0131] The process in Fig. 29C will be described. Following step S603, the advice generating unit 117 calculates the difference between the lower limit of a total score interval that is one rank higher than the total score rank to which the target user belongs and the current value of the total score of the target user (S621).

[0132] 15, if the target user's total score rank is 55, the total score rank is B, and the lower limit of the total score interval for rank A, which is one rank higher than total score rank B, is 60, so the difference calculated in step S621 is 60-55=5. Note that if the target user's total score rank is A (the best rank), the process in FIG. 29C may be omitted.

[0133] The standard score calculation unit 120 executes the processes of steps S622, S623, and S624 described below for each index (number of times going out, number of places stayed, and average number of steps) of the target user.

[0134] The standard score calculation unit 120 sets the current values ​​of the indicators other than the target indicator in the loop in the calculation formula for the overall score, and performs an inverse simulation to calculate the improvement value (improved value) of the target indicator and the improvement range (from the current value) required to obtain a one-level better overall score rank (S622).

[0135] Specifically, for example, if the target indicator in the loop is the number of times going out, and the total score is given as a weighted average of the number of times going out, the number of places stayed, and the average number of steps, the standard score calculation unit 120 sets the current values ​​of the number of places stayed and the average number of steps, which are indicators other than the target indicator in the loop, in a calculation formula for the weighted average, calculates a standard deviation of the number of times going out to achieve a care risk rank that is one level better than the current care risk rank, and identifies the improvement value and improvement range of the number of times going out required to achieve the calculated standard deviation.

[0136] The standard score calculation unit 120 rounds up the improvement value and improvement range according to the data type of the target index within the range of the domain of the target index (S623). The standard score calculation unit 120 stores the improvement value and improvement range (rounded up as necessary) of the target index of the target user in the aggregation period in the inverse simulation result information 148 (S624).

[0137] In addition, if the improvement value of the index calculated in step S624 exceeds the definition domain, the advice display screen described below may display, for example, the upper limit of the definition domain instead of the improvement value, or a message such as "The rank will not change, but if you improve it, you can reduce the risk of needing care. Let's start with what you can do."

[0138] Although the standard score calculation section 120 executes an inverse simulation for each target index in step S622, the standard score calculation section 120 may execute an inverse simulation for a combination of a plurality of target indexes (eg, by mathematical programming).

[0139] 30 is a flowchart showing an example of the recommendation process in step S700. The recommendation processing unit 118 identifies the behavioral performance estimate value (usage record of each intervention service) of the target user by matching the venue indicated by the intervention service information 132 with the location of the target user of the recommendation process indicated by the location information 133, and stores the intervention service recommendation history for the target user indicated by the intervention service usage history information 149 in the identified behavioral performance estimate value (S701).

[0140] In addition, in step S701, the client 200 may transmit the usage record value of the intervention service input by the target user to the evaluation server 100, and the recommendation processing unit 118 may store the received usage record value of the intervention service in the intervention service usage history information 149.

[0141] The recommendation processing unit 118 receives information indicating the improvement index selected by the user from the client 200, identifies an intervention service of a social participation type related to the selected improvement index from the intervention service information 132 (for example, by referring to the event frequency column 1325, the event location column 1326, and the target exercise amount column 1327), and filters the identified intervention service, for example, by collaborative filtering (filtering that identifies intervention services used by users who have a similar intervention service usage history to the target user and that have not been used by the target user) (S702).

[0142] The recommendation processing unit 118 identifies an intervention service that has not been recommended to the target user in the past from the intervention services filtered in step S702 by referring to the intervention service usage history information 149, and determines the identified intervention service as a recommendation candidate intervention service (S703).

[0143] The recommendation processing unit 118 refers to the account information 131 and filters the intervention services of the recommendation candidates by user attributes (e.g., age class, gender, and residential area) (identifying, among the intervention services of the recommendation candidates, intervention services used by users whose user attributes are similar to those of the target user) (S704).

[0144] The recommendation processing unit 118 extracts the expected improvement value of the improvement index by the intervening service extracted by the filtering in step S704 from the intervening service information 132 (S705), and ends the recommendation processing.

[0145] 31 is a flowchart showing an example of a nursing care need risk prediction model update process. Before the nursing care need risk prediction model update process starts, the learning data 150 is registered in advance. The nursing care need risk prediction model update process may be executed periodically, may be executed according to an instruction from a user of the evaluation server 100, or may be executed when there is a high possibility that a deviation has occurred between the data trends at the time of learning and the time of inference, such as every time the number of records in the learning data 150 increases by a predetermined number.

[0146] The values ​​of the independent variables of the learning data 150 may be accumulated whenever the account information 131 is updated, the activity ratio calculation process, the interaction diversity calculation process, and the motion information calculation process are executed. The value of the dependent variable of the learning data 150, the presence or absence of nursing care certification within T years column 15013, is, for example, "present" when the evaluation server 100 receives a notice from the client 200 indicating that nursing care certification has been made within T years, and is "not present" when the evaluation server 100 does not receive the notice within T years.

[0147] The care-needing risk prediction unit 115 executes re-learning of the care-needing risk prediction model using the latest learning data 150 (S801). Specifically, for example, the care-needing risk prediction unit 115 learns the regression coefficients and error terms of the care-needing risk prediction model using the values ​​of independent variables and dependent variables in the latest learning data 150. The care-needing risk prediction unit 115 deploys the re-learned care-needing risk prediction model and ends the care-needing risk prediction model update process.

[0148] Fig. 32A, Fig. 32B, and Fig. 32C are diagrams showing an example of an evaluation result display screen. Fig. 33 is a diagram showing an example of a recommendation result display screen. The evaluation result display screen and the recommendation result display screen may be displayed on the output device 105 of the evaluation server 100, for example, or may be displayed on the output device 105 of the client 200 based on information transmitted from the evaluation server 100 to the client 200.

[0149] The evaluation result display screen includes, for example, an activity status display area 1001 for the period to be aggregated, a number of outings history display area 1002, a number of places stayed history display area 1003, an average number of steps history display area 1004, an individual advice text display area 1005, an improvement indicator display area 1006, and a recommendation button 1007.

[0150] The activity status display area 1001 for the period to be aggregated displays, for example, the target user's care risk rank or overall score rank ("You B most recently" in FIG. 32A) indicated by the behavioral assessment information 141, the number of times the target user has gone out indicated by the activity ratio information 138, the total number of places the target user has stayed indicated by the interaction diversity information 139, and the average number of steps taken by the target user indicated by the exercise information 140, during the period to be aggregated (for example, the most recent year, month, and week).

[0151] Furthermore, in the aggregation target period activity status display area 1001, for example, a radar chart is displayed which compares the number of times going out indicated by the activity ratio information 138, the total number of places stayed indicated by the interaction diversity information 139, and the average number of steps indicated by the exercise information 140 with the average number of times going out, the total number of places stayed, and the average number of steps of the target user of the same sex and age indicated by the user aggregation evaluation information 144. Note that the apex of each triangle constituting the radar chart indicates the quartile of the average number of times going out, the total number of places stayed, and the average number of steps of the target user of the same sex and age indicated by the user aggregation evaluation information 144.

[0152] The outing frequency history display area 1002 displays, for example, the history of the number of times the target user went out in each of the current tally period and multiple past tally periods, as indicated by the activity ratio information 138. The stay location number history display area 1003 displays, for example, the total number of stay locations and the number of stay locations for each behavioral purpose, as indicated by the interaction diversity information 139, as indicated by the current tally period and multiple past tally periods. The average step count history display area 1004 displays, for example, the target user's average step count history in each of the current tally period and multiple past tally periods, as indicated by the exercise information 140.

[0153] The individual advice text generated in step S603 is displayed in an individual advice text display area 1005. The improvement index display area 1006 displays the improvement values ​​and improvement ranges of the number of times going out, the number of places stayed, and the average number of steps, which are indicated by the inverse simulation result information 148. When a recommendation button 1007 is selected, the display transitions to a recommendation result display screen 1008.

[0154] The recommendation result display screen 1008 displays information indicating the intervention service extracted in step S704, and information indicating the expected value of the improvement index by the intervention service extracted in step S705.

[0155] As described above, the behavior evaluation system of this embodiment calculates the activity ratio and interaction diversity indicating the degree of the user's social participation, as well as an index indicating the user's exercise, based on the user's location and activity amount measured by the sensor of the client 200, and calculates the care-needing risk and / or the total score based on these indexes. In this way, the behavior evaluation system can objectively calculate an index measuring the user's participation in social activities, and calculate the care-needing risk and / or the total score based on the indexes, without imposing a large burden on the user.

[0156] The behavioral evaluation system of this embodiment also generates advice text based on the user's level of social participation and trends (tendencies in comparison with others, such as trends with people of the same sex and age, and trends over time, such as self-tendencies). Furthermore, the behavioral evaluation system presents the user with improvement values ​​and improvement ranges of indicators for improving the nursing care risk rank and / or total score rank. The behavioral evaluation system also recommends intervention services that contribute to actions for improving the user's nursing care risk and / or total score, based on the user's attributes and behavioral records. By the behavioral evaluation system performing these processes, the user can specifically understand preventive actions that are useful to him / herself, and behavioral changes in the user are expected.

[0157] In the above example, the evaluation server 100 calculates the risk of needing nursing care, as well as the standard score and the total score, using the activity ratio, interaction diversity, and exercise, but the risk of needing nursing care, as well as the standard score and the total score, may also be calculated using at least one of the activity ratio, interaction diversity, and exercise.

[0158] The present invention is not limited to the above-mentioned embodiment, and various modifications are included. For example, the above-mentioned embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to those having all the configurations described. It is also possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0159] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0160] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0161] 100 evaluation server, 101 CPU 101, 102 memory, 103 auxiliary storage device, 104 input device, 105 output device, 106 communication device, 111 timeline aggregation unit, 112 activity ratio calculation unit, 113 exchange diversity calculation unit, 114 exercise calculation unit, 115 nursing care risk prediction unit, 116 behavior evaluation unit, 117 advice generation unit, 118 recommendation processing unit, 119 processing model update unit, 120 standard score calculation unit, 132 intervention service information, 133 location information, 134 activity amount information, 137 timeline information, 138 activity ratio information, 139 exchange diversity information, 140 exercise information, 151 behavior evaluation information, 142 nursing care risk rank determination information, 143 total score rank determination information, 145 behavior tendency information, 146 reference part information, 148 Inverse simulation result information, 149 Intervention service usage history information, 150 Learning data, 200 Client

Claims

1. A behavior evaluation server, A processor and a memory are included. The memory holds a time series of a position of a first user and a time series of an activity amount of the first user; The processor, generating a time series of a behavior of the first user based on the time series of the location and the time series of the activity amount; Calculating a first index indicating the number of times the first user has gone out based on the time series of the locations indicated by the generated time series of the behavior, and a second index indicating the number of places where the first user has stayed based on the time series of the locations indicated by the generated time series of the behavior, Calculating a third index indicating an amount of exercise including an average number of steps at a place where the first user stays based on the generated time series of the behavior; The memory includes: a care-needing risk prediction model that outputs a care-needing risk when a value including a user attribute, the first index, the second index, and the third index is input; and a user attribute of the first user; the processor calculates a care-needing risk of the first user as a fourth index that comprehensively evaluates the behavior of the first user based on the user attributes of the first user, the first index, the second index, the third index, and the care-needing risk prediction model; The memory holds care-needing risk rank information indicating a correspondence between the care-needing risk and a care-needing risk rank, The processor, Identifying a care-needing risk rank from the care-needing risk of the first user by referring to the care-needing risk rank information; a behavioral evaluation server that calculates an amount of improvement of each of the first index, the second index, and the third index, based on a care risk corresponding to a care risk rank in the care risk rank information that is a predetermined level better than the identified care risk rank, the first index, the second index, the third index, and the care risk prediction model, in order to improve the care risk rank of the first user to a care risk rank that is a predetermined level better.

2. A behavioral evaluation server according to claim 1, The processor generates data for displaying the first index, the second index, and the third index, as well as an amount of improvement of each of the first index, the second index, and the third index.

3. A behavioral evaluation server according to claim 1, The memory holds learning data indicating the user attributes, the first indicator, the second indicator, the third indicator, and whether or not a nursing care certification has been made within a predetermined period of time for each of a plurality of users including the first user, The processor updates the nursing care need risk prediction model based on the learning data.

4. A behavior evaluation server, A processor and a memory are included. The memory holds a time series of a position of a first user and a time series of an activity amount of the first user; The processor, generating a time series of a behavior of the first user based on the time series of the location and the time series of the activity amount; Calculating a first index indicating the number of times the first user has gone out based on the time series of the locations indicated by the generated time series of the behavior, and a second index indicating the number of places where the first user has stayed based on the time series of the locations indicated by the generated time series of the behavior, Calculating a third index indicating an amount of exercise including an average number of steps at a place where the first user stays based on the generated time series of the behavior; The memory holds the first index, the second index, and the third index for each of a plurality of users including the first user; The processor, Calculating standard scores indicating a ranking among the plurality of users for each of the first index, the second index, and the third index of a first user included in the plurality of users; calculating a total score of the first user by combining the standard scores of the first index, the second index, and the third index, as a fourth index for comprehensively evaluating the behavior of the first user; the memory holds total score rank information indicating a correspondence between the total score and a total score rank; The processor, Identifying a total score rank from the total score of the first user by referring to the total score rank information; A behavioral evaluation server that calculates an improvement amount of each of the first index, the second index, and the third index based on an overall score corresponding to an overall score rank in the overall score rank information that is a predetermined level better than the identified overall score rank, the first index, the second index, and the third index, in order to improve the overall score rank of the first user to an overall score rank that is a predetermined level better.

5. A behavioral evaluation server according to claim 4, The processor generates data for displaying the first index, the second index, and the third index, as well as an amount of improvement of each of the first index, the second index, and the third index.

6. A behavior evaluation server, comprising: A processor and a memory are included. The memory holds a time series of a position of a first user and a time series of an activity amount of the first user; The processor, generating a time series of a behavior of the first user based on the time series of the location and the time series of the activity amount; Calculating a first index indicating the number of times the first user has gone out based on the time series of the locations indicated by the generated time series of the behavior, and a second index indicating the number of places where the first user has stayed based on the time series of the locations indicated by the generated time series of the behavior, Calculating a third index indicating an amount of exercise including an average number of steps at a place where the first user stays based on the generated time series of the behavior; The memory includes: User attributes of a plurality of users including the first user; Intervention service usage history information indicating an intervention service used by each of the plurality of users; and intervention service information indicating an improvement amount of the first index, the second index, and / or the third index when each of the intervention services is used; The processor, determining an intervention service to be recommended to the first user from intervention services used by each of the plurality of users having the same user attribute as the first user, the intervention service usage history information indicating the intervention service usage history information; a behavior evaluation server that refers to the intervention service information and extracts an improvement amount of the first index, the second index, and / or the third index when the determined intervention service is used.

7. A behavioral evaluation server according to claim 1, 4, or 6, The memory includes: Holding user attributes, the first index, the second index, and the third index of each of a plurality of users including the first user; The processor, Calculating a self tendency indicating a tendency of a change in the time series of the first index, the second index, and the third index of the first user based on the generated time series of the behavior; Calculating a same attribute comparison tendency indicating a tendency of each of the most recent first index, second index, and third index of the first user compared with a group of users having a common user attribute with the first user among the plurality of users; A behavior evaluation server that generates advice to be presented to the first user based on the calculated self-tendencies and same-attribute comparison tendencies of each of the first user's first indicator, second indicator, and third indicator.

8. A behavioral evaluation server according to claim 1, 4, or 6, The time series of the first user's locations indicates whether the first user is moving or staying at each location; The time series of the activity amount of the first user indicates a time series of the number of steps of the first user. Behavioral assessment server.

9. A behavior evaluation method using a behavior evaluation server, comprising: The behavior evaluation server includes a processor and a memory, The memory holds a time series of a position of a first user and a time series of an activity amount of the first user; The behavior evaluation method includes: The processor generates a time series of a behavior of the first user based on the time series of the positions and the time series of the activity amount; Calculating a first index indicating the number of times the first user has gone out based on the time series of the locations indicated by the generated time series of the behavior, and a second index indicating the number of places where the first user has stayed based on the time series of the locations indicated by the generated time series of the behavior, The processor calculates a third index indicating an amount of exercise including an average number of steps at a location where the first user stays based on the generated time series of the behavior; The memory includes: a care-needing risk prediction model that outputs a care-needing risk when a value including a user attribute, the first index, the second index, and the third index is input; and a user attribute of the first user; The behavior evaluation method includes the steps of: calculating a care-needing risk of the first user as a fourth index that comprehensively evaluates the behavior of the first user based on a user attribute of the first user, the first index, the second index, the third index, and the care-needing risk prediction model; the memory holds care-requiring risk rank information indicating a correspondence between the care-requiring risk and a care-requiring risk rank; The behavior evaluation method includes: The processor refers to the care need risk rank information to identify a care need risk rank from the care need risk of the first user; A behavioral evaluation method in which the processor calculates an amount of improvement of each of the first index, the second index, and the third index, based on a care risk corresponding to a care risk rank in the care risk rank information that is a predetermined level better than the identified care risk rank, the first index, the second index, the third index, and the care risk prediction model, in order to improve the care risk rank of the first user to a care risk rank that is a predetermined level better.

10. A behavior evaluation method using a behavior evaluation server, comprising: The behavior evaluation server includes a processor and a memory, The memory holds a time series of a position of a first user and a time series of an activity amount of the first user; The behavior evaluation method includes: The processor generates a time series of a behavior of the first user based on the time series of the positions and the time series of the activity amount; The processor calculates a first index indicating a number of times the first user has gone out based on the time series of the location indicated by the generated time series of the behavior, and a second index indicating a number of places where the first user has stayed based on the time series of the location indicated by the generated time series of the behavior, The processor calculates a third index indicating an amount of exercise including an average number of steps at a location where the first user stays based on the generated time series of the behavior; The memory holds the first index, the second index, and the third index for each of a plurality of users including the first user; The behavior evaluation method includes: The processor calculates standard scores indicating a ranking among the plurality of users for each of the first index, the second index, and the third index of a first user included in the plurality of users; The processor calculates a total score of the first user by totaling the standard scores of the first index, the second index, and the third index, as a fourth index for comprehensively evaluating the behavior of the first user; the memory holds total score rank information indicating a correspondence between the total score and a total score rank; The behavior evaluation method includes: The processor refers to the overall score rank information to identify an overall score rank from the overall score of the first user; A behavioral evaluation method in which the processor calculates an amount of improvement of each of the first index, the second index, and the third index, based on an overall score corresponding to an overall score rank in the overall score rank information that is a predetermined level better than the identified overall score rank, the first index, the second index, and the third index, in order to improve the overall score rank of the first user to an overall score rank that is a predetermined level better.

11. A behavior evaluation method using a behavior evaluation server, comprising: The behavior evaluation server includes a processor and a memory, The memory holds a time series of a position of a first user and a time series of an activity amount of the first user; The behavior evaluation method includes: The processor generates a time series of a behavior of the first user based on the time series of the positions and the time series of the activity amount; The processor calculates a first index indicating a number of times the first user has gone out based on the time series of the location indicated by the generated time series of the behavior, and a second index indicating a number of places where the first user has stayed based on the time series of the location indicated by the generated time series of the behavior, The processor calculates a third index indicating an amount of exercise including an average number of steps at a location where the first user stays based on the generated time series of the behavior; The memory includes: User attributes of a plurality of users including the first user; Intervention service usage history information indicating an intervention service used by each of the plurality of users; and intervention service information indicating an improvement amount of the first index, the second index, and / or the third index when each of the intervention services is used; The behavior evaluation method includes: The processor determines an intervention service to be recommended to the first user from intervention services used by each of the plurality of users having the same user attribute as the first user, the intervention service usage history information indicating the intervention service usage history information; A behavior evaluation method in which the processor refers to the intervention service information and extracts the amount of improvement of the first index, the second index, and / or the third index when the determined intervention service is used.

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