Information processing method, information processing apparatus, and program

Abstract

To evaluate whether a level of difficulty of a target value is appropriate for a user.SOLUTION: An information processing method includes: a first calculation step (Step B3) of calculating, based on past performance data of action of a user, an estimated achievement value V1 indicating an estimated value to be achieved by a user with respect to a parameter related to action of the user; a second calculation step (Step B7) of calculating a first achievement provability rate E indicating the probability that the user can achieve a target value V2 on the basis of the estimated achievement value V1 calculated by the first calculation step and the target value 2 for a preset parameter; and a first correction step (Steps B9, B10) of correcting, when the first achievement probability rate E is not included in a predetermined range, the target value V2 so that the first achievement probability rate E may be included in the predetermined range.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing method, an information processing device, and a program. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there are systems that set target values ​​for activities such as exercise and learning that a user undertakes.

[0003] For example, Patent Document 1 discloses the following target value determination device. Specifically, the device holds a history of target values ​​and actual values ​​in a user's walking exercise, etc., and a history of expected values ​​for the next achievement value. Then, the device determines a target value for the user's walking exercise for the next day based on the history at a timing after the actual value for the day is determined. At that time, the device accepts an input of an expected achievement value for the next day by the user, estimates the user's psychological parameters from the accepted expected value, and determines the target value for the next day. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-104373 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the target value determined in the conventional system is a value that takes into account the expected value input by the user, and may not be a level of difficulty that is objectively appropriate for the user.

[0006] An object of the present invention is to provide an information processing method, an information processing device, and a program capable of evaluating whether or not a target value of difficulty is appropriate for a user. [Means for solving the problem]

[0007] In order to solve the above problems, the information processing method of the present invention comprises: a first calculation step of calculating an achievable estimated value indicating a value that is estimated to be achievable by the user for a parameter related to the user's behavior based on performance data of the user's behavior; a second calculation step of calculating a first achievement likelihood rate indicating the likelihood that the user will achieve the target value based on the achievable estimated value calculated in the first calculation step and a target value for the parameter that is set in advance; a first correction step of correcting the target value when the first achievement likelihood rate is not within a predetermined range so that the first achievement likelihood rate is within the predetermined range; Includes.

[0008] The information processing method of the present invention comprises the steps of: A step of identifying a similar user group to which a group of users similar to the user belongs based on a capability value for the user's behavior; an acquisition step of acquiring a second achievement likelihood rate indicating a likelihood that a group of users belonging to the similar user group identified by the identification step will be able to achieve a target value for a parameter related to a preset user behavior; a second correction step of correcting the target value when the second achievement likelihood rate is not within a predetermined range so that the second achievement likelihood rate is within the predetermined range; Including, In the obtaining step, a cumulative ratio corresponding to the target value in a cumulative histogram of performance data of a group of users belonging to the similar user group is obtained as the second achievement likelihood ratio.

[0009] The information processing device of the present invention comprises: a first calculation unit that calculates an achievable estimated value indicating a value that is estimated to be achievable by the user for a parameter related to the user's behavior based on performance data of the user's behavior; a second calculation unit that calculates a first achievement likelihood rate indicating an likelihood that the user will achieve the target value based on the achievable estimated value calculated by the first calculation unit and a target value for the parameter that is set in advance; a first correction unit that corrects the target value when the first achievement likelihood rate is not within a predetermined range so that the first achievement likelihood rate is within the predetermined range; Equipped with.

[0010] The information processing device of the present invention comprises: an identification unit that identifies a similar user group to which a group of users similar to the user belongs based on a capability value for the user's behavior; an acquisition unit that acquires a second achievement likelihood rate indicating a likelihood that a group of users belonging to the similar user group identified by the identification unit will be able to achieve a target value for a parameter related to a predetermined user behavior; a second correction unit that corrects the target value when the second achievement likelihood rate is not within a predetermined range so that the second achievement likelihood rate is within the predetermined range; Equipped with The acquisition unit acquires, as the second achievement likelihood rate, a cumulative ratio corresponding to the target value in a cumulative histogram of performance data of a group of users belonging to the similar user group.

[0011] The program of the present invention comprises: The computer of the information processing device, a first calculation unit that calculates an achievable estimated value indicating a value that is estimated to be achievable by the user for a parameter related to the user's behavior based on performance data of the user's behavior; a second calculation unit that calculates a first achievement likelihood rate indicating an likelihood that the user will achieve the target value, based on the achievable estimated value calculated by the first calculation unit and a target value for the parameter that is set in advance; a first correction unit that corrects the target value when the first achievement likelihood rate is not within a predetermined range so that the first achievement likelihood rate is within the predetermined range; Function as.

[0012] The program of the present invention comprises: The computer of the information processing device, an identification unit that identifies a similar user group to which a group of users similar to the user belongs based on a capability value for the user's behavior; an acquisition unit that acquires a second achievement likelihood rate indicating a likelihood that a group of users belonging to the similar user group identified by the identification unit will be able to achieve a target value for a parameter related to a predetermined user behavior; a second correction unit that corrects the target value when the second achievement likelihood rate is not within a predetermined range so that the second achievement likelihood rate is within the predetermined range; Functioning as a The acquisition unit acquires, as the second achievement likelihood rate, a cumulative ratio corresponding to the target value in a cumulative histogram of performance data of a group of users belonging to the similar user group. Effect of the Invention

[0013] According to the present invention, it is possible to evaluate whether or not a target value of difficulty is appropriate for a user. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a block diagram showing a configuration of an information processing system. [Figure 2A] FIG. 13 is a diagram showing an example of a cumulative histogram and a cumulative ratio of a running pace. [Figure 2B] FIG. 13 is a diagram illustrating an example of a cumulative ratio of a maximum possible travel distance. [Diagram 3] 13 is a ladder chart showing the flow of a menu suggestion process. [Figure 4] 13 is a flowchart showing a flow of a target value determination process. [Diagram 5] FIG. 13 is a diagram showing an example of a proposal screen displayed on a display unit of a terminal device. [Figure 6] 13 is a flowchart showing the flow of a target value determination process according to a modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an information processing system according to an embodiment of the present invention will be described in detail with reference to the drawings.

[0016] [1. Description of configuration] [1-1. Description of the configuration of information processing system 1] FIG. 1 shows an example of the configuration of an information processing system 1 according to an embodiment of the present invention. The information processing system 1 includes an information processing device 10 and a plurality of terminal devices 20. Note that the information processing system 1 may include a plurality of information processing devices 10. The information processing device 10 and the terminal device 20 are connected via a network 30 so as to be capable of electrical communication. The network 30 may be any electric communication network, such as the Internet, a wireless LAN (Local Area Network), a wired LAN, a mobile communication network, a short-range wireless communication network, or a combination of any or all of these.

[0017] [1-2. Description of the configuration of information processing device 10] The information processing device 10 includes a control unit 11, a communication unit 12, and a storage unit 13. These components are connected to each other via a system bus 19.

[0018] The control unit 11 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and the like, and controls each unit of the information processing device 10. Specifically, the control unit 11 reads out a specified program from among the system programs and application programs stored in the storage unit 13, expands the program in the RAM, and executes various processes according to the program. For example, the control unit 11 generates a control signal for causing the display unit 24 of the terminal device 20 to display the execution result in accordance with the execution result of the application program, and transmits the control signal to the terminal device 20 via the communication unit 12.

[0019] The communication unit 12 includes a network interface card (NIC) and the like, and accesses the network 30 to perform electrical communication with external devices such as the terminal device 20.

[0020] The storage unit 13 is composed of, for example, a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read only memory (EEPROM), etc. The storage unit 13 stores system programs, application programs, etc. executed by the control unit 11, as well as data necessary for executing these programs.

[0021] The storage unit 13 also stores a performance database (DB) 131, a cumulative histogram database (DB) 132, a menu database (DB) 133, and a user group database (DB) .

[0022] The result DB 131 stores result data collected from each terminal device 20 for each user who uses the service provided by the information processing system 1. The performance data is, for example, performance data of the user's behavior in running, test results, correct answers to study questions, etc. Performance data in running is, for example, information indicating a record that the user ran 5 km in one hour.

[0023] The cumulative histogram DB 132 stores cumulative histograms relating to parameters (target parameters) of target values ​​determined in a target value determination process described later. The target parameters are, for example, running pace [min / km], maximum possible running distance [m], difficulty of study questions, and score in a unit test.

[0024] The cumulative histogram is statistical data relating to the target parameter, and is generated from, for example, public data available to the public, performance data collected from a plurality of terminal devices 20 included in the information processing system 1, and the like. For example, if the parameter of interest is test scores for a unit, a cumulative histogram may be generated for each class, grade, or school, or may be generated as a cumulative histogram for the entire country. Also, for example, if the information processing system 1 has an e-learning system and the target parameter is the correctness or incorrectness of an e-learning question, the control unit 11 generates a cumulative histogram by tallying up the answers to the e-learning questions for each e-learning student.

[0025] FIG. 2A shows an example of a cumulative histogram stored in the cumulative histogram DB 132. As shown in FIG. 2A is a cumulative histogram in which the target parameter is running pace [min / km]. In the example shown in FIG. 2A, the horizontal axis is the target parameter, which is running pace [min / km], and the vertical axis is the cumulative ratio. In the example shown in Fig. 2A, the cumulative histogram represents the cumulative number of data points from the first interval to each interval for the target parameter. The intervals are values ​​obtained by dividing the target parameter into predetermined ranges. In the example shown in FIG. 2A, the running pace [min / km] on the horizontal axis is arranged from the left in ascending order.

[0026] FIG. 2B shows an example of a cumulative ratio calculated from the cumulative histogram stored in the cumulative histogram DB 132. In FIG. FIG. 2B is a graph of the cumulative ratio when the target parameter is the maximum possible running distance [m]. In the example shown in FIG. 2B, the horizontal axis is the target parameter, the maximum possible running distance [m], and the vertical axis is the cumulative ratio. The cumulative ratio shown in FIG. 2B is the ratio of the number of people who can run that distance to the total number of people. In the example shown in FIG. 2B, the maximum driving distance [m] on the horizontal axis is arranged from the left in ascending order.

[0027] In this embodiment, the cumulative ratio is the ratio of the cumulative number of data from the first interval to each interval in the target parameter to the total number of data.

[0028] In the example shown in FIG. 2A, the first section in the target parameter is the section where the running pace is 4 [min / km] or more. The cumulative ratio in the section where the running pace is from 4 [min / km] to 7 [min / km] is 0.25. The cumulative ratio in the section where the running pace is from 4 [min / km] to 8 [min / km] is 0.75. The cumulative ratio in the section where the running pace is from 4 [min / km] to 10 [min / km] is 0.95. The cumulative ratio in the section where the running pace is from 4 [min / km] to 11 [min / km] is 0.97.

[0029] In the example shown in FIG. 2B, the first section in the target parameter is the section in which the maximum possible driving distance is 45,000 [m]. The cumulative ratio in the section in which the maximum possible driving distance is from 45,000 [m] to 10,000 [m] is 0.50. The cumulative ratio in the section in which the maximum possible driving distance is from 45,000 [m] to 5,000 [m] is 0.68.

[0030] The menu DB 133 stores target values ​​in association with a predetermined menu of exercise menus, study questions, and the like for achieving the target values ​​of the target parameters. The control unit 11 transmits a proposal screen displaying a specific menu stored in the menu DB 133 to the terminal device 20, and displays the proposal screen on the terminal device 20, thereby proposing the menu to the user using the terminal device 20.

[0031] The user group DB 134 will be explained later.

[0032] [1-3. Description of the configuration of terminal device 20] The terminal device 20 may be any of a personal computer, a mobile terminal device such as a tablet, and a mobile communication device such as a smartphone.

[0033] The terminal device 20 includes a control unit 21, a communication unit 22, a storage unit 23, a display unit 24, and an operation unit 25. These components are connected to each other via a system bus 29.

[0034] The control unit 21 includes a CPU, a RAM, and the like, and controls each unit of the terminal device 20. Specifically, the control unit 21 reads out a specified program from among the system programs and application (hereinafter, referred to as app) programs stored in the storage unit 23, expands the program in the RAM, and executes various processes according to the program. For example, the control unit 21 executes an application program and connects to the information processing device 10 via the network 30. Then, the control unit 21 transmits performance data inputted at the terminal device 20 to the information processing device 10. In addition, the control unit 21 receives from the information processing device 10 a proposal screen displaying the target value determined in the target value determination process and a menu corresponding to the target value, and displays the received proposal screen on the display unit 24.

[0035] The communication unit 22 includes at least one of a wired communication module such as a NIC and a wireless communication module, and accesses the network 30 to perform electrical communication with external devices such as the information processing device 10.

[0036] The storage unit 23 is configured with, for example, a HDD, an SSD, an EEPROM, and the like. The storage unit 23 stores system programs, application programs, and other programs executed by the control unit 21, as well as data required for executing these programs. The programs may be downloaded to the storage unit 23 from an external web server via the network 30 and the communication unit 22.

[0037] The display unit 24 is configured with an LCD (Liquid Crystal Display), an EL (Electro Luminescence) display, or the like, and performs various displays according to display information instructed by the control unit 21.

[0038] The operation unit 25 has an operation input unit such as a keyboard, a touch panel, a mouse, etc. The operation unit 25 accepts an operation input by a user and outputs the operation information to the control unit 21.

[0039] [2. Description of operation] Next, the operation of the information processing system 1 will be described. FIG. 3 is a ladder chart showing the flow of a menu suggestion process for suggesting to the user the target value determined in the target value determination process and a menu for achieving the target value.

[0040] (Menu suggestion processing) First, the control unit 21 of the terminal device 20 executes an application program and accepts input of performance data on the behavior of the user who uses the terminal device 20 (step A1). In step A1, the control unit 21 accepts the input of performance data, for example, by manual input by a user via the operation unit 25. Alternatively, the control unit 21 accepts the input of performance data, for example, by acquiring data measured by a predetermined measuring means as performance data.

[0041] Next, the control unit 21 transmits the performance data received in step A1 to the information processing device 10 via the communication unit 22 (step A2). In step A2, the control unit 21 transmits the performance data to the information processing device 10 as soon as the input of the performance data is received in step A1, or periodically, or as soon as a predetermined amount of performance data is accumulated.

[0042] Next, the control unit 11 of the information processing device 10 receives the performance data from the terminal device 20 via the communication unit 12. Then, the control unit 11 collects the performance data by storing the received performance data in the performance DB 131 for each user who uses the terminal device 20 (step A3). Next, the control unit 11 executes a target value determination process shown in FIG. 4 (step A4).

[0043] (Target value determination process) The control unit 11 acquires, from the result DB 131, result data of a target user who is a target user for whom a menu for achieving a target value is to be proposed (step B1).

[0044] Next, the control unit 11 determines a target parameter, which is a parameter of a target value in the user's behavior, based on the performance data acquired in step B1 (step B2). For example, the control unit 11 determines a parameter that is previously associated with performance data of the user's behavior as the target parameter. Specifically, when the control unit 11 acquires performance data in running as the performance data in step B1, the control unit 11 determines the target parameter to be the running pace [min / km] in running or the maximum possible running distance [m] in running in step B2. The control unit 11 may determine the parameters set by the target user in the terminal device 20 as the target parameters.

[0045] Next, the control unit 11 calculates an achievable estimated value V1 of the target parameter that is estimated to be attainable by the target user based on the performance data of the user's behavior acquired in step B1 (step B3). The control unit 11 functions as a first calculation unit. Step B3 is a first calculation step. In step B3, the control unit 11 calculates an achievable estimated value V1 by the following formula (1). Equation (1) V1 = M M is, for example, the average value of the top 5% in the distribution of the performance data acquired in step B1.

[0046] The control unit 11 may calculate the achievable estimated value V1 by the following formula (2). Equation (2) V1 = M(1+G) G is the growth rate per data (per implementation) calculated based on the transition of the target parameter in the performance data acquired in step B1, and is, for example, 5%. A specific example of a method for calculating G will be described below. Assume that there are two pieces of data on the user's past running distances as the user's behavioral performance data (the first running distance is 20 [km], and the second running distance is 21 [km]). In this case, if the target parameter is the maximum possible running distance [m], G is calculated using the following formula. G = (21-20) / 20 = 0.05 = 5% Also, assume that there are two pieces of past running pace data for the user as the user's behavioral performance data (the first running pace is 6 [min / km], and the second running pace is 5 [min / km]). In this case, if the target parameter is the running pace [min / km], G is calculated by the following formula. G = (6 - 5) / 6 = 0.1666 = 16.7%

[0047] Furthermore, the control unit 11 may calculate the estimated achievable value V1 by regression (for example, linear regression) or time series estimation on the performance data acquired in step B1.

[0048] In addition, assuming that the target user gradually becomes able to run at a faster pace by running for, for example, one year, the estimated achievable value of the target parameter that is estimated to be attained by the target user at present differs from the estimated achievable value of the target parameter that is estimated to be attained by the target user one year from now. In other words, the estimated achievable value changes depending on the time of calculation.

[0049] Next, the control unit 11 acquires the target value V2 for the target parameter (step B4). In step B4, the control unit 11 may obtain a recommended target value V2 that is set in advance and stored in the storage unit 13, or may obtain a target value V2 that is set by the target user in the terminal device 20.

[0050] Next, the control unit 11 acquires a first cumulative ratio, which is a cumulative ratio corresponding to the achievable estimated value V1 calculated in step B3, and a second cumulative ratio, which is a cumulative ratio corresponding to the target value V2 acquired in step B4, from the cumulative histogram stored in the cumulative histogram DB132 (step B5). For example, a case will be described in which the target parameter is the running pace [min / km], the achievable estimated value V1 is 8 [min / km], and the target value V2 is 7 [min / km]. In this case, the control unit 11 obtains 0.75 as the first cumulative ratio and 0.25 as the second cumulative ratio from the cumulative histogram shown in FIG. 2A. In addition, a case will be described in which the target parameter is the running pace [min / km], the achievable estimated value V1 is 11 [min / km], and the target value V2 is 10 [min / km]. In this case, the control unit 11 obtains 0.97 as the first cumulative ratio and 0.95 as the second cumulative ratio from the cumulative histogram shown in FIG. 2A.

[0051] Next, the control unit 11 calculates an achievement likelihood rate E (first achievement likelihood rate) indicating the likelihood that the target user will be able to achieve the target value V2 (step B6). In step B6, the control unit 11 calculates the achievement likelihood rate E by the following formula (3). Equation (3) E = Z2 / Z1 Z1: 1st cumulative ratio Z2: 2nd cumulative ratio In the above formula (3), the control unit 11 converts the second cumulative ratio acquired in step B5 by setting the first cumulative ratio acquired in step B5 to 1.0.

[0052] That is, in step B6, the control unit 11 calculates an achievement likelihood rate E (first achievement likelihood rate) indicating the likelihood that the target user will achieve the target value V2 based on the achievable estimated value V1 calculated as the first calculation unit and the preset target value V2. The control unit 11 functions as a second calculation unit. The step B6 is a second calculation step.

[0053] For example, when the achievable estimated value V1 is 8 [min / km] and the target value V2 is 7 [min / km], that is, when the first cumulative ratio is 0.75 and the second cumulative ratio is 0.25, the achievement likelihood rate E is 33.3%. Furthermore, when the achievable estimated value V1 is 11 [min / km] and the target value V2 is 10 [min / km], that is, when the first cumulative ratio is 0.97 and the second cumulative ratio is 0.95, the achievement likelihood rate E is 97.9%. In this way, changing the running pace from the estimated achievable value V1 of 8 [min / km] to the target value V2 of 7 [min / km] has a lower probability of achievement E and is more difficult for the target user than changing from the estimated achievable value V1 of 11 [min / km] to the target value V2 of 10 [min / km].

[0054] Next, the control unit 11 judges whether the achievement likelihood rate E calculated in step B6 is within a predetermined range (E1 to E2) (step B7). The predetermined range (E1 to E2) is set in advance, and may be, for example, a recommended range stored in the storage unit 13, or may be a range set by the target user in the terminal device 20 and desired by the target user. The predetermined range (E1 to E2) is a variable range. If the achievement likelihood rate E is not within the predetermined range (E1 to E2) (step B7; NO), the control unit 11 determines whether the achievement likelihood rate E is closer to the minimum value E1 than the maximum value E2 in the predetermined range (step B8).

[0055] If the achievement likelihood rate E is closer to the minimum value E1 than the maximum value E2 in the predetermined range (step B8; YES), the control unit 11 corrects the target value V2 so that the achievement likelihood rate E becomes the minimum value E1 in the predetermined range (step B9). For example, a case will be described in which the achievable estimated value V1 is 8 [min / km], the target value V2 is 7 [min / km], the achievement likelihood rate E is 33.3%, and the predetermined range is 50% (minimum value E1) to 60% (maximum value E2). In this case, the target value V2 of 7 [min / km] is a high degree of difficulty relative to the predetermined range. In this case, in step B9, the control unit 11 corrects the target value V2 so that the achievement likelihood rate E becomes 50%.

[0056] On the other hand, if the achievement likelihood rate E is closer to the maximum value E2 than the minimum value E1 in the specified range (step B8; NO), the control unit 11 corrects the target value V2 so that the achievement likelihood rate E becomes the maximum value E2 in the specified range (step B10). For example, a case will be described in which the achievable estimated value V1 is 11 [min / km], the target value V2 is 10 [min / km], the achievement likelihood rate E is 97.9%, and the predetermined range is 50% (minimum value E1) to 60% (maximum value E2). In this case, the target value V2 of 10 [min / km] is an easy level of difficulty relative to the predetermined range. In this case, in step B10, the control unit 11 corrects the target value V2 so that the achievement likelihood rate E becomes 60%.

[0057] That is, in steps B9 and B10, if the achievement likelihood rate E (first achievement likelihood rate) is not within a predetermined range, the control unit 11 corrects the target value V2 so that the first achievement likelihood rate falls within the predetermined range. The control unit 11 functions as a first correction unit. The steps B9 and B10 are the first correction steps.

[0058] Next, the control unit 11 determines the target value V2 corrected in step B9 or step B10 as the final target value (step B11), and ends the target value determination process.

[0059] If the achievement likelihood rate E is within a predetermined range (E1 to E2) (step B7; YES), the control unit 11 advances the process to step B11. In this case, in step B11, the control unit 11 determines the target value V2 acquired in step B4 as the final target value.

[0060] 3, the control unit 11 acquires a menu associated with the final target value determined in the target value determination process in step A4 from the menu DB 133. Then, the control unit 11 transmits a proposal screen displaying the final target value and the acquired menu to the terminal device 20 (step A5). FIG. 5 shows an example of a proposal screen that displays a target value and a menu item associated with the target value.

[0061] Next, the control unit 21 of the terminal device 20 displays the proposal screen received from the information processing device 10 on the display unit 24, thereby proposing the target value and a menu corresponding to the target value to the target user (step A6), and terminates the menu proposal process.

[0062] (Modification) Next, an information processing system 1 according to a modification of the above embodiment will be described. The following mainly describes the differences from the above embodiment.

[0063] The storage unit 13 in the modified example stores a user group DB 134 . The user group DB 134 stores user group information obtained by classifying a plurality of users who use the services provided by the information processing system 1 based on ability values ​​for the actions of the users. The ability value for the user's behavior is, for example, the completion time in running data for a full marathon, the estimated completion time if the user were to run a full marathon, etc.

[0064] The user groups in the user group DB 134 are classified into users whose distance is within a predetermined range based on, for example, the inter-vector distance of the index values ​​in the performance data of the latest user behavior. The inter-vector distance may be, for example, Euclidean distance, cosine similarity, or the like. Alternatively, if the user behavior performance data is data related to test questions, the user groups in the user group DB 134 are classified into users whose scores (grades) for each subject field are within a predetermined range based on the balance of the scores.

[0065] Alternatively, the user groups in the user group DB 134 are classified into users whose transitions are within a predetermined range based on the distance between vector transitions of index values ​​in the performance data of the user's behavior and the distance between vector transitions of records. The distance between vector transitions is, for example, the Bhattacharyya distance. In this case, the user groups are classified by the K-means method, the K-medoids method, or the like. Alternatively, if the performance data of user behavior is data related to test questions, user groups in the user group DB 134 are classified into users whose scores (grades) for each subject area change within a predetermined range.

[0066] The user group DB 134 also stores cumulative histograms of target parameters for users belonging to each user group. The cumulative histograms are generated from performance data of the actions of the users belonging to each user group.

[0067] FIG. 5 shows a flowchart of the target value determination process of this modified example.

[0068] First, the control unit 11 of the information processing device 10 executes steps C1 to C3 similar to the target value determination processing steps B1, B2, and B4 of the above embodiment.

[0069] Next, the control unit 11 refers to the user group DB 134 and identifies a similar user group, which is a group including users having ability values ​​similar to the ability values ​​for the target user's behavior, based on the performance data acquired in step C1 (step C4). That is, the control unit 11 identifies a similar user group to which a group of users similar to the target user belongs, based on the ability value for the behavior of the target user. The control unit 11 functions as an identification unit. The step C4 is an identification step.

[0070] Next, the control unit 11 acquires a cumulative histogram for the target parameter of the similar user group identified in step C4 from the user group DB 134 (step C5).

[0071] Next, the control unit 11 acquires a cumulative ratio corresponding to the target value V2 acquired in step C3 from the cumulative histogram of the similar group acquired in step C5. Then, the control unit 11 sets the acquired cumulative ratio as an achievement likelihood ratio GE (second achievement likelihood ratio) indicating the likelihood that the user group belonging to the similar user group will be able to achieve the target value V2 (step C6). That is, the control unit 11 acquires an achievement likelihood rate GE (second achievement likelihood rate) indicating the likelihood that a group of users belonging to the similar user group identified by the identification unit can achieve a preset target value V2 for the target parameter. The control unit 11 functions as an acquisition unit. The step C6 is an acquisition step.

[0072] Next, the control unit 11 executes steps C7 to C11 similar to steps B7 to B11 of the target value determination process of the above embodiment, and ends the target value determination process of this modified example. That is, in steps C9 and C10, if the achievement likelihood rate GE (second achievement likelihood rate) is not within a predetermined range, the control unit 11 corrects the target value V2 so that the second achievement likelihood rate is within the predetermined range. The control unit 11 functions as a second correction unit. The steps C9 and C10 are second correction steps.

[0073] In addition, in the target value determination process step C1 of this modified example, the control unit 11 may not acquire performance data of the target user's behavior. In this case, in step C2, the control unit 11 determines a target parameter, which is a parameter of the target value, based on a designation operation by the target user on the terminal device 20. In this case, in step C4, the control unit 11 may acquire a capability value for the target user's behavior from the terminal device 20, and determine a similar user group based on data correlated with the capability value. The data correlated with the capability value is data different from the user group information stored in the user group DB 134. Alternatively, in step C4, the control unit 11 may determine that the group with the largest number of users in the user group information stored in the user group DB 134 is the similar user group.

[0074] By proposing a menu to the target user in the above-mentioned menu suggestion process, the target user can easily grasp the target difficulty value appropriate for the target user and the menu for achieving the target value. Therefore, it is possible to prevent the target user from becoming discouraged or bored due to the target value being too difficult or too easy for the target user, which contributes to maintaining the motivation of the target user. In addition, the target user can grasp the target value V2 set based on the performance data of his / her own behavior, or the target value V2 set based on the performance data of the behavior of a group of users who belong to a user group similar to the user, thereby improving the user's sense of self-efficacy and increasing the user's motivation.

[0075] [3. Effects] As described above, the information processing method executed by the information processing device 10 according to this embodiment includes a first calculation step (step B3) of calculating an achievable estimate value V1 indicating a value that the user is estimated to be achievable for a parameter related to the user's behavior based on actual data of the user's behavior, a second calculation step (step B7) of calculating a first achievement likelihood rate E indicating the likelihood that the user will be able to achieve the target value V2 based on the achievable estimate value V1 calculated by the first calculation step and a target value V2 for a preset parameter, and a first correction step (steps B9, B10) of correcting the target value V2 if the first achievement likelihood rate E is not within a predetermined range so that the first achievement likelihood rate E is within the predetermined range. Therefore, by calculating the first achievement likelihood rate E indicating the likelihood that the user will be able to achieve the target value V2, it is possible to evaluate whether the target value V2 has an appropriate level of difficulty for the user. In addition, the target value V2 can be corrected so that the first achievement likelihood rate E is within a predetermined range (E1 to E2), in other words, so that the difficulty level is appropriate for the user. That is, the target value V2 can be set to a difficulty level appropriate for the user.

[0076] Furthermore, in the information processing method executed by the information processing device 10 according to this embodiment, in the first correction step, if the first achievement likelihood rate E is closer to the minimum value E1 than the maximum value E2 in a specified range (E1 to E2), the target value V2 is corrected so that the first achievement likelihood rate E becomes the minimum value E1, and if the first achievement likelihood rate E is closer to the maximum value E2 than the minimum value E1, the target value V2 is corrected so that the first achievement likelihood rate E becomes the maximum value E2. Therefore, the target value V2 can be corrected so as to have a level of difficulty appropriate for the user.

[0077] In addition, in the information processing method executed by the information processing device 10 according to the present embodiment, the achievable estimated value V1 is calculated by the following formula (1) or the following formula (2). Formula (1): V1=M Formula (2): V1=M(1+G) V1: Achievable estimate M: The average value within a given range in the distribution of the user's performance data G: Growth rate per data calculated based on the parameter transition in the user's performance data Therefore, an appropriate achievable estimated value V1 can be calculated based on the user's performance data.

[0078] In addition, in the information processing method executed by the information processing device 10 according to the present embodiment, the first achievement likelihood rate E is calculated by the following formula (3). Formula (3): E=Z2 / Z1 E: First achievement probability rate Z1: The cumulative ratio corresponding to the achievable estimate in the cumulative histogram of statistical data Z2: The cumulative ratio corresponding to the target value in the cumulative histogram of statistical data Therefore, for example, changing the running pace from the estimated achievable value V1 of 8 [min / km] to the target value V2 of 7 [min / km] has a lower achievement probability E and is more difficult for the target user than changing from the estimated achievable value V1 of 11 [min / km] to the target value V2 of 10 [min / km]. In this way, the estimated achievable value V1 can be calculated taking into account the difficulty in the numerical variation.

[0079] Furthermore, the information processing method executed by the information processing device 10 according to this embodiment includes a specification step (step C4) of identifying a similar user group to which a group of users similar to the user belongs based on an ability value for the user's behavior, an acquisition step (step C6) of acquiring a second achievement likelihood rate GE indicating the likelihood that a group of users belonging to the similar user group identified by the specification step will be able to achieve a target value V2 for a parameter related to a preset user's behavior, and a second correction step (steps C9, C10) of correcting the target value V2 so that the second achievement likelihood rate GE falls within a predetermined range if the second achievement likelihood rate GE is not within a predetermined range, and in the acquisition step, a cumulative ratio corresponding to the target value V2 in a cumulative histogram of performance data of a group of users belonging to the similar user group is acquired as the second achievement likelihood rate GE. Therefore, by acquiring the second achievement likelihood rate GE, which indicates the likelihood that the group of users belonging to the similar user group will be able to achieve the target value V2, it is possible to evaluate whether the target value V2 is at an appropriate level of difficulty for the user. In addition, the target value V2 can be corrected so that the second achievement expected rate GE falls within a predetermined range (E1 to E2), in other words, so that the difficulty level is appropriate for the user. That is, the target value V2 can be set to a difficulty level appropriate for the user.

[0080] Furthermore, in the information processing method executed by the information processing device 10 according to this embodiment, in the second correction step, if the second achievement likelihood rate GE is closer to the minimum value E1 than the maximum value E2 in a specified range (E1 to E2), the target value V2 is corrected so that the second achievement likelihood rate GE becomes the minimum value E1, and if the second achievement likelihood rate GE is closer to the maximum value E2 than the minimum value E1, the target value V2 is corrected so that the second achievement likelihood rate GE becomes the maximum value E2. Therefore, the target value V2 can be corrected so as to have a level of difficulty appropriate for the user.

[0081] Although the present invention has been specifically described above based on an embodiment thereof, the present invention is not limited to the above embodiment and can be modified without departing from the spirit of the present invention.

[0082] For example, in the above embodiment, the information processing device 10 and the terminal device 20 are separate devices, but the information processing device 10 and the terminal device 20 may be integrated to function as an information processing device.

[0083] In the above description, an example has been disclosed in which a HDD, SSD, EEPROM, etc. are used as a computer-readable medium storing a program for executing each process, but the present invention is not limited to this example. As another computer-readable medium, a portable recording medium such as a CD-ROM can also be used. In addition, a carrier wave can be used as a medium for providing program data via a communication line. [Explanation of symbols]

[0084] 1. Information Processing Systems 10. Information processing device 11 control unit (first calculation unit, second calculation unit, first correction unit, identification unit, acquisition unit, second correction unit) 12 Communications Department 13 Storage section 131 Performance Database 132 Cumulative Histogram Database 133 Menu Database 134 User Group Database 19 System Bus 20 Terminal Equipment 21 Control section 22 Communications Department 23 Memory section 24 Display section 25 Control section 29 System Bus 30 Network

Claims

1. a first calculation step of calculating an achievable estimated value indicating a value that is estimated to be achievable by the user for a parameter related to the user's behavior based on performance data of the user's behavior; a second calculation step of calculating a first achievement likelihood rate indicating an likelihood that the user will achieve the target value based on the achievable estimated value calculated in the first calculation step and a preset target value for the parameter; a first correction step of correcting the target value when the first achievement likelihood rate is not within a predetermined range so that the first achievement likelihood rate is within the predetermined range; An information processing method comprising:

2. 2. The information processing method of claim 1, wherein, in the first correction step, if the first achievement likelihood rate is closer to a minimum value than a maximum value in the specified range, the target value is corrected so that the first achievement likelihood rate becomes the minimum value, and if the first achievement likelihood rate is closer to the maximum value than the minimum value, the target value is corrected so that the first achievement likelihood rate becomes the maximum value.

3. The information processing method according to claim 1 , wherein the achievable estimate value is calculated by the following formula (1) or the following formula (2). Formula (1): V1=M Formula (2): V1=M(1+G) V1: Estimated achievable value M: The average value within a predetermined range in the distribution of the performance data of the user's behavior G: Growth rate per data calculated based on the parameter transition in the performance data of the user's behavior

4. The information processing method according to claim 1 , wherein the first achievement likelihood rate is calculated by the following formula (3): Formula (3): E=Z2 / Z1 E: First achievement probability rate Z1: The cumulative ratio corresponding to the achievable estimate in the cumulative histogram of statistical data Z2: The cumulative ratio corresponding to the target value in the cumulative histogram of statistical data

5. A step of identifying a similar user group to which a group of users similar to the user belongs based on a capability value for the user's behavior; an acquisition step of acquiring a second achievement likelihood rate indicating a likelihood that a group of users belonging to the similar user group identified by the identification step will be able to achieve a target value for a parameter related to a preset user behavior; a second correction step of correcting the target value when the second achievement likelihood rate is not within a predetermined range so that the second achievement likelihood rate is within the predetermined range; Including, An information processing method, in the obtaining step, a cumulative ratio corresponding to the target value in a cumulative histogram of performance data of a group of users belonging to the similar user group is obtained as the second achievement likelihood rate.

6. 6. The information processing method of claim 5, wherein in the second correction step, when the second achievement likelihood rate is closer to a minimum value than a maximum value in the specified range, the target value is corrected so that the second achievement likelihood rate becomes the minimum value, and when the second achievement likelihood rate is closer to the maximum value than the minimum value, the target value is corrected so that the second achievement likelihood rate becomes the maximum value.

7. a first calculation unit that calculates an achievable estimated value indicating a value that is estimated to be achievable by the user for a parameter related to the user's behavior based on performance data of the user's behavior; a second calculation unit that calculates a first achievement likelihood rate indicating an likelihood that the user will achieve the target value based on the achievable estimated value calculated by the first calculation unit and a preset target value for the parameter; a first correction unit that corrects the target value when the first achievement likelihood rate is not within a predetermined range so that the first achievement likelihood rate is within the predetermined range; An information processing device comprising:

8. an identification unit that identifies a similar user group to which a group of users similar to the user belongs based on a capability value for the user's behavior; an acquisition unit that acquires a second achievement likelihood rate indicating a likelihood that a group of users belonging to the similar user group identified by the identification unit will be able to achieve a target value for a parameter related to a predetermined user behavior; a second correction unit that corrects the target value when the second achievement likelihood rate is not within a predetermined range so that the second achievement likelihood rate is within the predetermined range; Equipped with The information processing device, wherein the acquisition unit acquires, as the second achievement likelihood rate, a cumulative ratio corresponding to the target value in a cumulative histogram of performance data of a group of users belonging to the similar user group.

9. The computer of the information processing device, a first calculation unit that calculates an achievable estimated value indicating a value that is estimated to be achievable by the user for a parameter related to the user's behavior based on performance data of the user's behavior; a second calculation unit that calculates a first achievement likelihood rate indicating an likelihood that the user will achieve the target value based on the achievable estimated value calculated by the first calculation unit and a target value for the parameter that is set in advance; a first correction unit that corrects the target value when the first achievement likelihood rate is not within a predetermined range so that the first achievement likelihood rate is within the predetermined range; A program that functions as a

10. The computer of the information processing device, an identification unit that identifies a similar user group to which a group of users similar to the user belongs based on a capability value for the user's behavior; an acquisition unit that acquires a second achievement likelihood rate indicating a likelihood that a group of users belonging to the similar user group identified by the identification unit will be able to achieve a target value for a parameter related to a predetermined behavior of the users; a second correction unit that corrects the target value when the second achievement likelihood rate is not within a predetermined range so that the second achievement likelihood rate is within the predetermined range; Function as a The acquisition unit acquires, as the second achievement likelihood rate, a cumulative ratio corresponding to the target value in a cumulative histogram of performance data of a group of users belonging to the similar user group.

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