A task-based estimation system for task execution inhibition function with a user interface (UI) that induces intuitive operation

Abstract

To cause a task execution function and a task suppression function of a user to be estimated in more detail only by executing a simple test.SOLUTION: A function estimation device includes: a presentation unit for presenting a plurality of tasks to a user for executing an operation to a user interface for inducing the user to give an incorrect answer; an acquisition unit for acquiring operation data indicating the user's operation result for the plurality of tasks presented by the presentation unit; and a prediction unit for predicting indexes indicating the user's task execution function and task suppression function on the basis of the user's operation data acquired by the acquisition unit. The user interface includes: an interrogative sentence; a correct answer option that the user is supposed to select for the interrogative sentence; and an incorrect answer option to remind the user of a correct answer to the interrogative sentence by at least one element of a hue, ornament, expression, term, and arrangement indicated by contents of the interrogative sentence or contents of the correct answer option.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a function estimation device, and in particular to a function estimation device that estimates a task execution function and a task inhibition function of the prefrontal cortex of the brain. [Background technology]

[0002] It is known that dementia tests (such as the Hasegawa Scale) are necessary to determine whether or not someone has dementia, or to check the degree of dementia progression. In addition, it is known that impairment of task execution and task inhibition functions in the prefrontal cortex of the brain tends to be manifested in Alzheimer's disease, which is one of the causes of dementia.

[0003] Task executive function is the ability to deal with the current situation, derive appropriate thoughts or actions, and promote decision-making regarding those thoughts or actions when performing a task. Task inhibition function is the ability to consciously suppress inappropriate thoughts or actions in the current situation when performing a task. In order to estimate these task executive and task inhibition functions, it was necessary to conduct psychological tests (e.g., go-nogo tasks, Stroop tests, etc.). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] “Dementia self-diagnosis test”, [online], Dementia Prevention Association, [searched October 19, 2020], Internet<URL:http: / / www.ninchi-k.com / print_test.html> Summary of the Invention [Problem to be solved by the invention]

[0005] Among tests that suggest a visit to a dementia specialist by having the target user answer questions that can test multiple abilities, questions that can test whether the above-mentioned task execution function and task inhibition function are normal have been disclosed (see Non-Patent Document 1). The test adds up the scores of multiple questions that the target user answers correctly, and suggests a visit to a specialist if the total score does not reach a predetermined standard score. Therefore, the test includes one question about the task execution function and task inhibition function, but does not suggest the detailed state of the target user's task execution function and task inhibition function.

[0006] In addition, conducting a psychological test to estimate the task execution function and task inhibition function of a target user requires the presence of an expert, execution of a predetermined procedure, sufficient time to take the test, etc., and therefore conducting such a test is not easy. Therefore, there is a demand for a system that can estimate the task execution function and task inhibition function of a target user in more detail by simply having the target user take a simple test.

[0007] Therefore, an object of the present disclosure is to estimate a target user's task execution function and task inhibition function in more detail by simply conducting a simple test. [Means for solving the problem]

[0008] A function estimation device according to one aspect of the present disclosure includes a presentation unit that presents to a user a plurality of tasks that involve operations on a user interface that induces the user to give an incorrect answer, an acquisition unit that acquires operation data indicating results of the user's operations on the plurality of tasks presented by the presentation unit, and an index that represents a user's task execution function and a task inhibition function based on the user's operation data acquired by the acquisition unit. Estimation do Estimationand a unit, wherein the user interface includes a question, a correct answer option that the user should originally select for the question, and an incorrect answer option that reminds the user of the correct answer to the question by at least one element of color, decoration, expression, terminology, and arrangement indicated by the content of the question or the content of the correct answer option.

[0009] In this function estimation device, the user interface displays incorrect answer options that evoke the correct answer to the question together with the correct answer option. Therefore, the user interface can induce the user to give an incorrect answer, and therefore the user's operation data can be significantly and closely related to the task execution function and task inhibition function. Estimation The department is to obtain more detailed indicators representing a user's task execution function and task inhibition function based on the user's operation data. Estimation Therefore, by simply conducting a simple test, the user's task execution function and task inhibition function can be estimated in more detail. Effect of the Invention

[0010] According to the present disclosure, a target user's task execution function and task inhibition function can be estimated in more detail by simply conducting a simple test. [Brief description of the drawings]

[0011] [Figure 1] 1 is a functional block diagram of a function estimation device according to an embodiment. [Diagram 2] 1A is a diagram showing an example of a screen presenting a triggered user interface with distinctive terms and sizes in the options, and FIG. 1B is a diagram showing an example of a screen presenting a triggered user interface with distinctive terms and decorations in the options. [Diagram 3] FIG. 13 is a diagram showing an example of operation data of a triggered user interface. [Figure 4]FIG. 1A is a diagram showing an example of the test results of a psychology test, and FIG. 1B is a diagram showing indices representing task executive function and task inhibitory function derived from the test results of the psychology test. [Diagram 5] FIG. 11 is a flow diagram showing a process for constructing an estimation model. [Figure 6] FIG. 11 is a flow diagram showing an acquisition process of operation data. [Figure 7] FIG. 11 is a flow diagram showing a process of estimating a user's task execution function and task suppression function using an estimation model. [Figure 8] FIG. 2 is a diagram illustrating an example of a hardware configuration of a function estimation device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The present invention will be described with reference to the accompanying drawings. Whenever possible, the same parts are given the same reference numerals and duplicated explanations are omitted. The dimensional ratios of the drawings do not necessarily correspond to those of the description.

[0013] A configuration of a function estimation device according to an embodiment will be described with reference to FIG. 1. The function estimation device 1 shown in FIG. 1 estimates indicators representing task execution functions and task inhibition functions. Estimation do Estimation We constructed a model and derived indicators that represent the user's task execution and task inhibition functions. Estimation It is a device that

[0014] The function estimation device 1 is a device that can be operated by a user. Examples of the function estimation device 1 include a smartphone and a tablet terminal. For example, an application (hereinafter, referred to as an "app") that can be operated by a user is installed in the function estimation device 1. The type of the app is not particularly limited, and examples thereof include an SNS (social network service) app, a communication app, a video playback app, and a camera function app.

[0015] The function estimation device 1 includes a reception and display unit 5, a presentation unit 10, a calculation unit 15, an acquisition unit 20, and a learning unit 30. EstimationThe reception display unit 5 displays applications, tasks, and a user interface (described later) of the function estimation device 1, and receives input operations by a user (for example, user operations in a user interface (described later) presented by the presentation unit 10).

[0016] The storage unit 90 is a functional unit that stores information. The storage unit 90 functions as a database that stores data related to a user interface and tasks presented by the presentation unit 10 (described later). The storage unit 90 stores data calculated by the calculation unit 15 (described later). The storage unit 90 may store a combination of data from the presentation unit 10 and data obtained by the calculation unit 15. The calculation unit 15, the acquisition unit 20, the learning unit 30, and Estimation The unit 40 is configured to be able to acquire information from the storage unit 90. Note that the storage unit 90 may be provided outside the function estimation device 1.

[0017] The presentation unit 10 presents a plurality of tasks operable on the user interface to the user via the reception display unit 5. A task is an assignment or process that imposes an operation on the function estimation device 1 on the user who handles the function estimation device 1. The user interface presented by the presentation unit 10 is displayed, for example, as a pop-up on the reception display unit 5 of the function estimation device 1, and is visually recognized by the user and operated by the user via the reception display unit 5. For example, the user taps on the reception display unit 5 at an operation location on the user interface presented by the presentation unit 10 to operate the user interface.

[0018] The presentation unit 10 presents to the user a plurality of tasks for performing operations on a triggering user interface (hereinafter, referred to as a "triggering UI"), which is a user interface that triggers the user to give an incorrect answer. By presenting the triggering UI by the presentation unit 10, it is sensed whether or not a naturally induced response can be suppressed through the reception display unit 5.

[0019] For example, after the user launches an app that estimates a brain state (hereinafter, referred to as an estimation app), the presentation unit 10 presents to the user via the reception display unit 5 a plurality of tasks for performing operations on a triggering UI. The launch of the estimation app refers to, for example, the user touching an icon of the estimation app displayed on the reception display unit 5 in the function estimation device 1, causing the estimation app to start operating. The number of the plurality of tasks is a predetermined number.

[0020] As shown in FIG. 2, the trigger UI 80 presents to the user, for example, a question 81, a correct answer option 82 which is an answer to the question 81 and which the user should originally select, and an incorrect answer option 83 which is an answer to the question 81 and which the user should not originally select.

[0021] The question 81 in the trigger UI 80 is, for example, a question or fact-checking sentence regarding at least one of color, decoration, expression, terminology, and layout. The correct answer option 82 is, for example, a correct answer (correct answer) to the question 81 and is an option that represents at least one element of color, decoration, expression, terminology, and layout. The incorrect answer option 83 is, for example, an option that has an element different from at least one element of color, decoration, expression, terminology, and layout represented by the correct answer option 82.

[0022] Color includes concepts such as shade, brightness, and saturation of a color. Decoration includes concepts such as pattern, emphasis effect, size, and shape. Emphasis effect includes effects such as shadow and three-dimensional effect given to a shape. Expression includes a sentence form or sound representing the question sentence 81, the correct answer option 82, or the incorrect answer option 83. Expression includes a phrase that represents a relationship between the correct answer option 82 and the incorrect answer option 83. Terms include words, numbers, or phrases that represent the question sentence 81, the correct answer option 82, or the incorrect answer option 83, or their meanings.

[0023] The incorrect answer option 83 is designed, for example, to indicate a correct answer that the user can intuitively recall from the content of the question sentence 81. That is, the trigger UI 80 is designed so that, when the user intuitively selects an option, most users are likely to select the incorrect answer option 83 that should not be selected. Alternatively, when the user selects the correct answer option 82 in the trigger UI 80, the trigger UI 80 is designed so that the user is confused between the correct answer option 82 and the incorrect answer option 83, so that it takes many users a long time to complete the operation.

[0024] The incorrect answer option 83 reminds the user of the correct answer to the question 81 by at least one element of color, decoration, expression, terminology, and arrangement indicated by the content of the question 81 or the content of the correct answer option 82. The content of the question 81 includes at least one characteristic of the meaning, manner, and index suggested by the sentence shown as the question 81, the term shown as the question 81, or the characters (numbers) contained in the sentence or the term. The content of the correct answer option 82 includes at least one characteristic of the meaning, manner, and index suggested by the sentence shown as the correct answer option 82, the term shown as the correct answer option 82, or the characters (numbers) contained in the sentence or the term.

[0025] For example, among contents that can be expressed using at least one characteristic of the meaning, manner, and index contained in the content asked about, the content for which confirmation is sought, or the content expressed in question 81, the content that indicates the correct answer to question 81 is shown in correct answer option 82, and other contents that were not shown in correct answer option 82 are shown in incorrect answer option 83. Incorrect answer option 83 is designed, for example, so that some of the elements shown in the content of correct answer option 82 are different, or so that the same content shown in the content of correct answer option 82 is expressed by a different element.

[0026] The above-mentioned example is shown below. As shown in FIG. 2(a), a part or all of the decoration of the incorrect answer option 83a in the trigger UI 80a may be different from a part or all of the decoration of the correct answer option 82a, but may be the same as the expression indicated by the content of the term in the question 81a. The question 81a is a sentence saying "Please choose the one with the larger value." In addition to the value indicating the correct answer to the question 81a, the content that can be expressed using the size that is an index included in the content asked in the question 81a includes, for example, the size of the option as decoration. In this case, the value indicated by the correct answer option 82a ("8" in FIG. 2(a)) is set to be larger than the value indicated by the incorrect answer option 83a ("3" in FIG. 2(a)). On the other hand, the size of the characters indicating the value of the incorrect answer option 83a is set to be larger than the size of the characters indicating the value of the correct answer option 82a.

[0027] In addition, in the inducement UI 80a, the expression (relationship) indicated by the content of the correct answer option 82a to the content of the incorrect answer option 83a may be expressed by a part or all of the decoration of the incorrect answer option 83a to a part or all of the decoration of the correct answer option 82a. In this case, the value indicated by the correct answer option 82a, which is the correct answer to the question sentence 81 ("8" in (a) of FIG. 2), is set to be larger than the value indicated by the incorrect answer option 83a ("3" in (a) of FIG. 2). In this case, the "larger" relationship indicated by the content "8" of the correct answer option 82 to the content "3" of the incorrect answer option 83a is expressed by size instead of value, and the size of the incorrect answer option 83a is expressed to be larger than the size of the correct answer option 82.

[0028] In the elicitation UI 80a, a user who recognizes the content of the question 81a as a whole (completely) and the content of each of the options 82a and 83a is likely to select the correct answer option 82a with a large value. On the other hand, a user who only partially recognizes the content of the sentence of the question 81a or fails to understand the content of each of the options 82a and 83a is likely to overlook the word "value" in the question 81a and recognize only the term "large." Therefore, the user is likely to intuitively select the incorrect answer option 83a, which is visually represented in a large size.

[0029] As shown in FIG. 2B, some or all of the terms of the incorrect answer option 83b in the trigger UI 80b may be in accordance with the aspect indicated by the content of the terms in the question 81b, compared to some or all of the terms of the correct answer option 82b. In FIG. 2, the color of the decoration of the character part of the question 81b and the color of the decoration of the character part of the incorrect answer option 83b are represented by a diagonal line pattern, and the diagonal line pattern indicates the color "red". In FIG. 2, the color of the decoration of the character part of the correct answer option 82b is represented by a pointillism (dot) pattern, and the pointillism pattern indicates the color "blue". The question 81b is a sentence that reads "What is the color of this character?" The content that can be expressed for the color, which is the aspect asked about in the question 81b, includes, for example, the color indicated by the decoration, in addition to the color indicated by the term that is the content indicating the correct answer to the question 81b. In this case, the color implied by the term in correct answer option 82b ("red" in FIG. 2(b)) is different from the color implied by the term in incorrect answer option 83b ("blue" in FIG. 2(b)) and is set to match the correct answer indicated by the content of question sentence 81. On the other hand, the color given to the term as decoration in incorrect answer option 83a is different from the color given to the term as decoration in correct answer option 82a and is set to match the correct answer indicated by the content of question sentence 81.

[0030] Also, in the inducement UI 80b, the difference in expression indicated by the content of the correct answer option 82b relative to the content of the incorrect answer option 83b may be expressed by a difference in part or all of the decoration of the incorrect answer option 83b relative to a part or all of the decoration of the correct answer option 82b. In this case, the color ("red" in FIG. 2B) of the term of the correct answer option 82b is set to be different from the color ("blue" in FIG. 2B) of the term of the incorrect answer option 83a. Here, the meaning of the term "blue" of the incorrect answer option 83a is the color blue, whereas the meaning of the term "red" of the correct answer option 82 is the color red. In this case, the color attached to the term as the decoration of the correct answer option 82 is set to blue, and the color attached to the term as the decoration of the incorrect answer option 83a is set to red. In FIG. 2(b), the colors of the correct answer option 82b and the incorrect answer option 83b are interchanged, but it is sufficient if at least the decorative color of the incorrect answer option 83b is a color indicated by the content (terminology) of the correct answer option 82b.

[0031] In the elicitation UI 80b, a user who recognizes the content of the question sentence 81b as a whole (completely) and the content of each of the options 82b and 83b is likely to select the correct answer option 82b in which the color of the term is displayed as the meaning of the term. On the other hand, a user who only partially recognizes the content of the question sentence 81b or fails to understand the content of each of the options 82b and 83b is likely to place importance on the color "red" attached to the term as decoration, rather than on the color "red" that the term means. Therefore, the user is likely to intuitively select the incorrect answer option 83b in which the color attached to the term as decoration is visually displayed as red.

[0032] 2(a) and (b), the incorrect answer option 83 reminds the user of the correct answer to the question 81 by at least one element of color, decoration, expression, terminology, and arrangement indicated by the content of the question 81 and the content of the correct answer option 82, thereby enhancing the inducement effect of making the user select the incorrect answer option 83. In other words, the user needs to fully understand the content of the question 81, the correct answer option 82, and the incorrect answer option 83 and appropriately select the correct answer option 82 that represents the correct answer.

[0033] The color and decoration of the incorrect answer option 83b in the trigger UI 80b may be more luxurious than the color and decoration of the correct answer option 82a. "Luxurious" is an example of an aspect that is relatively easy to catch the user's eye.

[0034] The presentation unit 10 may present the inducement UI 80 by characterizing it by only one element of color, decoration, expression, term, and arrangement, or may present it by characterizing it by a combination of multiple elements. For example, by characterizing each of the options 82 and 83 by combining multiple elements, the user can be induced to select the incorrect answer option 83 more easily than the correct answer option 82.

[0035] After the inference application is launched, a plurality of trigger UIs 80 representing a plurality of tasks that are targets for the user to operate are sequentially presented. When the user taps on the presented portion of either the correct answer option 82 or the incorrect answer option 83 via the reception display unit 5, the operation result is transmitted as the input to the trigger UI 80 is completed. For example, the correct answer option 82 and the incorrect answer option 83 can be operated after the trigger UI 80 is displayed on the reception display unit 5. The operation result will be described later.

[0036] When the correct answer option 82 is selected by the user in the inducement UI 80 and the number of tasks in the estimation application has not been reached in a predetermined manner, the presentation unit 10 presents the inducement UI 80 representing the next task. When the correct answer option 82 is selected by the user in the inducement UI 80 and the number of tasks in the estimation application has been reached in a predetermined manner, the presentation unit 10 presents, for example, a screen representing that all of the tasks in the estimation application have been completed. For example, the presentation unit 10 presents a task by the inducement UI 80, and when the incorrect answer option 83 is selected by the user, presents the inducement UI 80 again. For example, the presentation unit 10 continues to present the inducement UI 80 until the user selects the correct answer option 82.

[0037] The reception display unit 5 outputs, for each operation on the trigger UI 80 by the user (hereinafter referred to as a trial), as operation results, the type of the trigger UI 80 (task) presented by the presentation unit 10, correct / incorrect information indicating whether the option selected by the user was correct or incorrect, the start timing at which the presentation or re-presentation of the trigger UI 80 started on the reception display unit 5, the selection timing at which the user selected an option on the trigger UI 80, and the like. The start timing or selection timing is, for example, a date and time. The reception display unit 5 stores the operation result in the storage unit 90, for example.

[0038] In the above, the presentation unit 10 presents the wrong answer option 83 as the trigger UI 80, which reminds the user of the correct answer to the question 81. However, in some of the tasks among the multiple tasks, the presentation unit 10 may present a UI (hereinafter, referred to as a normal UI) in which the wrong answer option indicates a content that does not remind the user of the correct answer to the question. When there are many tasks by the trigger UI 80, or when a task by the trigger UI 80 that asks the same type of content as a task by a certain trigger UI 80 is periodically displayed, the user's adaptability to the trigger UI 80 may improve (promote familiarity), and the function of the trigger UI 80 to induce the user to give a wrong answer (the trigger effect) may not be exerted significantly. In order to exert the trigger effect of the task by the trigger UI 80 significantly, the presentation unit 10 may, for example, randomly present some of the tasks among the multiple tasks as tasks by the normal UI.

[0039] In the above example, the presentation unit 10 presents one incorrect answer option 83 in one task (one trigger UI 80), but multiple incorrect answer options may be presented in one task. In this case, at least one of the multiple incorrect answer options has the same configuration as the incorrect answer option 83. The other incorrect answer options other than the one incorrect answer option may present content that does not remind the user of the correct answer to the question sentence 81.

[0040] 1, a description will be given of other functional units in the function estimation device 1. The calculation unit 15 calculates operation data related to the results of a user's operation on a plurality of tasks by the user interface presented by the presentation unit 10. For example, the calculation unit 15 calculates the operation data related to the trigger UI 80 after the presentation unit 10 presents a plurality of tasks by the trigger UI 80 a predetermined number of times or for a predetermined period of time.

[0041] The calculation of the operation data related to the trigger UI 80 will be described below. The calculation unit 15 calculates the operation data based on data in the storage unit 90 for each predetermined number of times that a task is presented by the trigger UI 80 or for each predetermined period. The predetermined number of times is, for example, a predetermined number of times when each type of the trigger UI 80 is displayed one or more times. The predetermined period is a predetermined period, for example, several days or several weeks. When the calculation unit 15 calculates the operation data, the calculation unit 15 stores the operation data in the storage unit 90.

[0042] The operation data includes, for example, at least one of correct answer count data on the number of times the correct answer option 82 is selected in the trigger UI 80 and incorrect answer count data on the number of times the incorrect answer option 83 is selected in the trigger UI 80. The calculation unit 15 calculates at least one of the correct answer count data and the incorrect answer count data based on the correct / incorrect information and the number of correct / incorrect information acquired from the storage unit 90.

[0043] The calculation unit 15 calculates, as the correct answer count data, for example, a correct answer selection rate, which is the ratio of the number of times the task was performed by selecting the correct answer option 82 to the number of times the task was performed by the prompting UI 80.

[0044] The calculation unit 15 calculates, as the data on the number of incorrect answers, for example, an incorrect answer repetition rate, which is the ratio of the number of tasks (hereinafter, referred to as repeated tasks) in which the user repeatedly selected the incorrect answer option 83 re-presented in the trigger UI 80 to the number of times the task was performed by the trigger UI 80 of the estimation application. The incorrect answer repetition rate is the ratio of the number of tasks in which the user made multiple incorrect answers in the same trigger UI 80 to the total number of tasks.

[0045] The calculation unit 15 calculates the incorrect answer repetition rate based on the type of task, the correct / incorrect information, and the number of correct / incorrect information stored in the storage unit 90. For example, when the user selects the incorrect answer option 83 in the first-presented inducement UI 80 and then selects the incorrect answer option 83 again in the re-presented inducement UI 80, the task by the inducement UI 80 becomes a repetitive task. When the number of incorrect answers is recorded two or more times in the correct / incorrect information for the same task, the calculation unit 15 determines the task as a repetitive task and calculates an index (hereinafter, referred to as a repetition index) as "1". When the number of incorrect answers is recorded one or less times in the correct / incorrect information for the same task, the calculation unit 15 determines the task as a repetitive task and calculates the repetition index as "0". The calculation unit 15 calculates the repetition index for each task stored in the storage unit 90. The calculation unit 15 calculates the sum of the repetition indexes as the number of repetitive tasks. The calculation unit 15 calculates the incorrect answer repetition rate by dividing the number of repetitive tasks by the total number of tasks.

[0046] The calculation unit 15 may calculate, as the data on the number of incorrect answers, a number of repeated incorrect answers, which is the number of times the incorrect answer option 83 re-presented in one task by the trigger UI 80 is repeatedly selected. In this case, the calculation unit 15 calculates the number of repeated incorrect answers based on the type of task and the correct / incorrect information stored in the storage unit 90. For example, when the user selects the incorrect answer option 83 in the trigger UI 80 presented for the first time and then selects the incorrect answer option 83 again in the trigger UI 80 presented again, the number of repeated incorrect answers becomes "1". After this selection, the number of repeated incorrect answers is incremented by one each time the user selects the incorrect answer option 83 in the trigger UI 80 re-presented in the same task. The calculation unit 15 may calculate, as the data on the number of incorrect answers, a maximum value of the number of repeated incorrect answers in the number of repeated incorrect answers of all tasks.

[0047] The operation data includes operation time data regarding the operation time required by the user to operate the user interface (for example, the operation time required to operate the trigger UI 80 presented by the presentation unit 10). As an example of the operation time data, the calculation unit 15 calculates the operation time for each task based on the start timing and the selection timing.

[0048] The calculation unit 15 calculates a correct answer operation time and an incorrect answer operation time, which are examples of operation time, based on the type of task, the correct / incorrect information, the start timing, and the selection timing. The correct answer operation time is the time from the first presentation timing of each task by the triggering UI 80 by the presentation unit 10 to the correct answer selection timing at which the user selects the correct answer option 82. The incorrect answer operation time is the time from the presentation timing at which each task by the triggering UI 80 is presented or re-presented by the presentation unit 10 to the incorrect answer selection timing at which the user selects the incorrect answer option 83. The calculation method will be described below.

[0049] When the calculation unit 15 calculates the correct operation time, the calculation unit 15 calculates the earliest timing in the same task among the task types as the first appearance timing. The first appearance timing includes, among the start timings, an initial timing at which the first task is presented by the presentation unit 10 after the estimation application is launched, or a switching timing at which the presentation unit 10 switches from another task to a new task. When the calculation unit 15 calculates the correct operation time, the calculation unit 15 assigns, as the correct answer selection timing, a selection timing at which the correct answer is selected in the same task among the task types. The calculation unit 15 obtains the correct operation time by subtracting the first appearance timing from the correct answer selection timing.

[0050] When the calculation unit 15 calculates the incorrect operation time, the calculation unit 15 calculates the presentation timing. The presentation timing includes, among the start timings, an initial timing at which the first task is presented by the presentation unit 10 after the estimation application is started, a switching timing at which the presentation unit 10 switches from another task to a new task, or a re-presentation timing at which the presentation unit 10 re-presents the trigger UI 80. When the calculation unit 15 calculates the incorrect operation time, the calculation unit 15 assigns, as the incorrect answer selection timing, a selection timing at which an incorrect answer is selected in a trial including the above-mentioned presentation timing. The calculation unit 15 obtains the incorrect operation time by subtracting the presentation timing from the incorrect answer selection timing. Note that the calculation unit 15 may use the sum of the above-mentioned incorrect operation times obtained in the same task among the task types as an example of the incorrect operation time.

[0051] The calculation unit 15 calculates a feature amount based on the operation time obtained from a plurality of tasks by the triggering UI 80 as an example of operation time data. The feature amount includes, for example, an average value of the operation time for a plurality of tasks, a variance of the operation time, etc. The feature amount includes, for example, an average value of the correct operation time for a plurality of tasks, a variance of the correct operation time, an average value of the incorrect operation time, a variance of the incorrect operation time, etc.

[0052] The calculation unit 15 may calculate operation data including a degree of deviation between the correct answer operation time and the correct answer selection data of the learning user and the correct answer selection data of the target user. Estimation The subject (user) from whom the operation data used for model learning is extracted. Estimation Using the model, we developed indicators that represent task execution and task inhibition functions. Estimation The correct answer selection data includes, for example, a correct answer selection rate. In this embodiment, when the term "user" is simply used, it may include a learning user and a target user.

[0053] The correct answer operation time and the correct answer selection rate of the learning user are related by, for example, a regression equation that derives an approximate correct answer selection rate for the correct answer operation time. The above-mentioned deviation degree refers to, for example, the error of the correct answer operation time and the correct answer selection rate of the target user with respect to the regression equation obtained by the learning user. The error refers to, for example, the difference between the correct answer selection rate obtained from the regression equation for a certain correct answer operation time and the correct answer selection rate of the target user.

[0054] A large deviation indicates that the error is large. A large positive deviation indicates that the correct answer selection rate of the target user is larger than the correct answer selection rate obtained from the regression equation, and the error is large. A large negative deviation indicates that the correct answer selection rate of the target user is smaller than the correct answer selection rate obtained from the regression equation, and the error is large.

[0055] In addition, each variable in the operation data may be calculated by counting for each element (color, decoration, expression, term, and arrangement) characterized for the incorrect answer option 83 of the trigger UI 80. Each variable in the operation data may be calculated by counting for each task or user interface. The calculation unit 15 stores the calculated operation data in the storage unit 90.

[0056] 1 acquires operation data for a plurality of tasks by the trigger UI 80 from the storage unit 90, as shown in FIG 3. The acquisition unit 20 may directly acquire the operation data calculated by the calculation unit 15.

[0057] The acquisition unit 20 acquires, for example, the average operation time, the variance of the operation time, the correct answer selection rate, the average correct answer operation time, the variance of the correct answer operation time, the average incorrect answer operation time, the variance of the incorrect answer operation time, and the number of repeated incorrect answers as operation data for multiple tasks using the trigger UI 80.

[0058] The smaller at least one of the mean operation time, variance of operation time, correct answer rate, mean incorrect answer time, and variance of incorrect answer time suggests a decline in task execution function and task inhibition function. Also, the larger at least one of the mean correct answer time, variance of correct answer time, and incorrect answer repetition rate suggests a decline in task execution function and task inhibition function. A large mean incorrect answer time and a large variance of incorrect answer time strongly suggest a decline in task inhibition function.

[0059] The smaller the average value of the operation time, the faster the user's reaction speed to the task by the triggering UI 80 is suggested. The larger the variance value of the operation time is than a predetermined first threshold, the lower the adaptability (the less used to) the user is to the task by the triggering UI 80. In addition, when the variance value of the operation time is smaller than a predetermined second threshold, the higher the adaptability (the more used to) the user is to the task by the triggering UI 80. For example, the acquisition unit 20 may acquire the variance of the operation time in advance from the operation data, and when the variance value of the operation time is equal to or greater than the second threshold and equal to or less than the first threshold, the acquisition unit 20 may acquire another value of the operation data.

[0060] Although not shown in Fig. 3, the acquisition unit 20 may acquire the degree of deviation of the correct answer operation time and correct answer selection data of the target user from the correct answer operation time and correct answer selection data of the learning user. If the degree of deviation is large in the positive direction and the operation time (correct answer operation time) is small, it is suggested that the task execution function and the task suppression function are functioning normally.

[0061] Furthermore, if the user's task inhibition function is normal, it is estimated that the longer the user's operation time, the longer the user's thinking time will be, and therefore it is estimated that the possibility of selecting correct answer option 82 is high. Therefore, if the deviation degree is large in the positive direction and the operation time (normal operation time) is long, it is suggested that the task execution function is weakened.

[0062] On the other hand, if the deviation is large in the negative direction and the operation time (normal operation time) is long, it is estimated that the task execution function and task inhibition function are weakened. If the deviation is large in the negative direction and the operation time (normal operation time) is short, it is estimated that the task inhibition function is weakened.

[0063] Returning to FIG. 1 again, the learning unit 30 calculates indices representing the task execution function and the task suppression function of the target user. Estimation In order to Estimation Learn about the Model 35. Estimation Model 35 is a model trained by conventional machine learning. Estimation The model 35 may, for example, include a conventional neural network. Estimation The model 35 may be any other model. Estimation The Model 35 is Estimation Part 40 Estimation The function index is an index representing the task execution function and task inhibition function of a learning user or a target user.

[0064] The learning unit 30 generates an estimation model by performing machine learning using the acquired operation information of the learning user as an input (input value) to the estimation model and the acquired functional index of the learning user as an output (output value) from the estimation model. In this way, when the operation information of the learning user is x and the functional index of the learning user is y, the learning unit 30 learns the function f shown in the relational expression y=f(x) of the estimation model. Estimation The model is stored in the storage unit 90. Note that the learning user is Estimation A user who provides operation data and function indicators used in learning the model 35. The learning user and the target user may be the same or different.

[0065] The above-mentioned functional indexes are obtained by a psychological test. The psychological test is carried out in a predetermined procedure in the presence of an expert, with sufficient time for taking the test. One example of the psychological test is the "go-nogo task" shown in FIG. 4. The go-nogo task is carried out, for example, by a learning user using a test terminal equipped with a reception display unit such as a smartphone.

[0066] As shown in (a) of FIG. 4, in the go-nogo task, a task is presented on the screen of the test terminal one after another for a predetermined number of trials, with the response types being a symbol to which the learning user should respond (GO symbol) and a symbol to which the learning user should not respond (NOGO symbol). The learning user takes appropriate action for each response type. For example, in a trial where a GO symbol is presented, the learning user quickly responds by tapping the test terminal, and in a trial where a NOGO symbol is presented, the learning user passes the test terminal without performing any operation.

[0067] If the learning user taps on the GO symbol, or if the learning user leaves the NOGO symbol without performing any operation, the execution result is "success." If the learning user leaves the GO symbol without performing any operation for a predetermined time or longer, or if the learning user mistakenly taps on the NOGO symbol, the execution result is "failure." The test terminal acquires the above information on "success" and "failure" as the execution result. In addition, the test terminal acquires the reaction time, which is the time from when the GO symbol is displayed until the learning user performs a tap operation, as the execution result. The test terminal acquires multiple execution results for multiple learning users.

[0068] As shown in (b) of Fig. 4, the test terminal calculates a function index based on each data obtained as an execution result. The function index is at least one of data items, for example, an overall success rate which is an average value of all learning users of success rates, which are a ratio of the number of tasks whose execution results are successful to the number of times the task is performed, a GO success rate which is a success rate of all learning users in a task in which a GO symbol is presented, a NOGO success rate which is a success rate of all learning users in a task in which a NOGO symbol is presented, and an average reaction time which is an average value of reaction times of all learning users. The test terminal stores the function index in the storage unit 90.

[0069] For example, if the overall success rate is lower than a predetermined threshold, it suggests that at least one of the task execution function and the task inhibition function is weaker than normal. If the GO success rate is lower than a predetermined threshold, it suggests that the task execution function is weaker than normal. If the NOGO success rate is lower than a predetermined threshold, it suggests that the task inhibition function is weaker than normal. If the average reaction time is greater than a predetermined threshold, it suggests that the task execution function is weaker than normal. Each of the above thresholds is set by an expert for the go-nogo task to be performed.

[0070] The learning user executes the task presented by the presentation unit 10 and also executes the psychological test in the test terminal, so that the operation data and the function index are Estimation The operation data is obtained as learning data for the model 35. When the calculation unit 15 calculates the operation data based on the data in the storage unit 90 for a predetermined period, the function index measured during the predetermined period or immediately before or after the predetermined period and the operation data are stored in the learning unit 30. Estimation The model 35 is trained by using, for example, operation data calculated based on data of a plurality of tasks executed by a training user during a certain period and stored in the storage unit 90, and a function index acquired by executing one or a plurality of psychological tests using a test terminal during the certain period. Estimation Used to train Model 35.

[0071] The acquisition unit 20 acquires function indicators of a predetermined number or more of multiple training users. The learning unit 30 uses the operation data and function indicators of the predetermined number or more of multiple training users acquired by the acquisition unit 20. Estimation The model 35 is trained. The predetermined number of people is, for example, several tens or several hundreds of people. The operation data derived in the task using the elicitation UI 80 and the function index derived in the psychological test are data suggesting the state (e.g., decline) of the task execution function and the task inhibition function, respectively. Estimation The model 35 acquires the ability to estimate the functional indicators from each indicator contained in the operational data.

[0072] Estimation The unit 40 converts the operation data of the target user acquired by the acquisition unit 20 into the operation data obtained by the learning unit 30. Estimation By applying it to model 35, the functional index of the target user is Estimation The target user operates the function estimation device 1 and causes the acquisition unit 20 to acquire operation results for the multiple tasks presented by the presentation unit 10. Estimation The unit 40 acquires the operation data of the target user acquired by the acquisition unit 20 for each predetermined period. Estimation By applying the model 35, the performance indicators for each specified period are Estimation do. Estimation The unit 40 acquires the operation data of the target user acquired by the acquisition unit 20 for each presentation of a predetermined task. Estimation By applying model 35, the functional index for each presentation is calculated. Estimation You may do so.

[0073] Estimation Part 40 is EstimationThe task execution function and task inhibition function of the target user may be diagnosed using the functional index obtained. Diagnosis of the task execution function and task inhibition function refers to diagnosing the state of the target user's brain based on the functional index, including whether or not there is an abnormality in the target user's function, whether or not there is a sign of decline (weakness), whether or not the function has declined (weakened) compared to previous or other users of the same age, etc.

[0074] In this case, for example, the acquiring unit 20 acquires a threshold value for the functional index determined by an expert. Estimation The unit 40 may, for example, Estimation The task execution function and the task inhibition function of the target user may be diagnosed based on the functional index of the target user and a threshold value determined by an expert. For example, Estimation If the functional index of the target user is lower than the threshold, Estimation The unit 40 can diagnose that the task execution function and task inhibition function of the target user may be deteriorated.

[0075] Furthermore, for example, the acquiring unit 20 may acquire the functional index of the learning user and the diagnosis results of the task execution function and the task inhibition function of the learning user. Estimation Part 40 is Estimation The task execution function and task inhibition function of the target user may be diagnosed based on the functional index of the target user, the functional index of the learning user, and the diagnosis result of the task execution function and task inhibition function of the learning user. In this case, if the diagnosis result indicates that the task execution function and task inhibition function of the learning user are weakened, Estimation The unit 40 may set the functional index of the learning user as a threshold value. Estimation The unit 40 may set a feature amount such as an average value of the function index as the threshold. For example, Estimation The functional index of the target user is Estimation If it is lower than the threshold value set by Part 40, Estimation The unit 40 can diagnose that the task execution function and task inhibition function of the target user may be deteriorated.

[0076] Estimation Part 40 is Estimation The function of outputting the determined functional index of the target user or the diagnosed task execution function and task suppression function to the reception display unit 5 so that the target user can confirm the functional index or the diagnosed task execution function and task suppression function. In this case, the reception display unit 5 may have a function of outputting the determined functional index or the diagnosed task execution function and task suppression function to the reception display unit 5 so that the target user can confirm the functional index or the diagnosed task execution function and task suppression function. Estimation The functional index of the target user or the diagnosed task execution function and task inhibition function are displayed.

[0077] The presentation unit in the claims corresponds to the reception and display unit 5 and the presentation unit 10. The acquisition unit in the claims corresponds to the reception and display unit 5, the calculation unit 15, and the acquisition unit 20. The learning unit in the claims corresponds to the learning unit 30. Estimation The department is Estimation Corresponds to part 40.

[0078] Next, the process executed by the function estimation device 1 according to this embodiment (the estimation method performed by the function estimation device 1) will be described with reference to the flowcharts of FIGS. 5 to 7. First, in the function estimation device 1 shown in FIG. Estimation The process of constructing the model 35 will now be described.

[0079] The function estimation device 1 executes a process of acquiring operation data of the learning user (step S1 in FIG. 5). As shown in FIG. 6, step S1 is composed of the following steps. First, the presentation unit 10 presents a task by the trigger UI 80 on the reception display unit 5 at a predetermined timing (step S11). Next, the reception display unit 5 accepts an operation on the trigger UI 80 by the learning user, and the operation result is stored in the storage unit 90 (step S12).

[0080] Next, the calculation unit 15 determines whether or not the learning user has operated the trigger UI 80 a predetermined number of times (step S13). Since the number of operation results by the learning user stored in the storage unit 90 corresponds to the number of tasks by the trigger UI 80 presented by the presentation unit 10, the calculation unit 15 determines whether or not the number of operation results is equal to or greater than a predetermined number. If the calculation unit 15 determines that the number is equal to or greater than the predetermined number (step S13: YES), the function estimation device 1 proceeds to the next step. If the calculation unit 15 determines that the number of operation results is less than the predetermined number of times (step S13: NO), the function estimation device 1 returns to step S11.

[0081] In step S13, if it is determined that the number of operation results stored in the memory unit 90 is equal to or greater than a predetermined number (step S13: YES), the calculation unit 15 calculates operation data of the learning user based on the operation results of the task using the trigger UI 80 (step S14).

[0082] Next, the acquiring unit 20 acquires operation data of the study user based on the operation result of the task by the triggering UI 80 (step S15). This completes the operation data acquisition process (step S1 in FIG. 5) shown in FIG.

[0083] 5, the acquiring unit 20 then acquires the functional index of the training user from which the operation data has been acquired (step S2). Then, the learning unit 30 performs a learning process using the operation data as an explanatory variable and the functional index as a response variable. Estimation The model 35 is trained (step S3). By the above process, the model 35 shown in FIG. Estimation The model building process is complete.

[0084] Next, referring to FIG. 7, in the function estimation device 1 Estimation Using Model 35, the functional index of the target user is calculated. EstimationThe process of determining whether or not a user is a target user will be described. First, the acquisition unit 20 acquires operation data of the target user (step S4). The process in step S4 is carried out, for example, in the same manner as each process in step S1 shown in FIG. 6. Step S4 is realized by a process procedure in which the "learning user" in the process procedure of steps S11 to S15 described above is replaced with the "target user". Next, Estimation The operation data of the target user is Estimation By applying the model 35, the functional index of the target user is Estimation As a result, the functional index of the target user shown in FIG. Estimation The process is completed.

[0085] After step S5, Estimation By part 40, Estimation The task execution function and the task inhibition function of the target user may be diagnosed based on the functional index of the target user and a predetermined threshold value. Estimation The unit 40 performs output processing via the reception and display unit 5. Estimation The functional index of the diagnosed target user, or the task execution function and task inhibition function of the diagnosed target user may be displayed. Estimation The unit 40 performs the following as output processing: Estimation The functional index of the diagnosed target user, or the task execution function and task inhibition function of the diagnosed target user may be stored in the storage unit 90.

[0086] As described above, the function estimation device 1 of this embodiment can estimate the task execution function and task inhibition function of the target user in more detail simply by conducting a simple test, namely a task using the inducing UI 80. The inducing UI 80 of the function estimation device 1 displays both a correct answer option 82 and an incorrect answer option 83 that recalls the correct answer to the question sentence 81. Thus, since the inducing UI 80 can induce the target user to select the incorrect answer option 83, the operation data of the target user can be significantly and closely related to the task execution function and task inhibition function. Therefore, EstimationThe unit 40 calculates in more detail indicators (function indicators) representing the task execution function and task inhibition function of the target user based on the operation data of the target user. Estimation The function estimation device 1 can perform, for example, Estimation The task execution function and task inhibition function of the target user can be easily diagnosed based on the obtained functional index of the target user and a threshold value determined by an expert or a functional index of a learning user.

[0087] In addition, in the learning unit 30, the operation data of the learning user is used as an explanatory variable, and the functional index of the learning user is used as a target variable, so that the functional index is calculated as Estimation do Estimation A model 35 can be constructed using the operation data of the target user as explanatory variables. Estimation By inputting the model 35, the functional indicators of the target users are Estimation The target user can obtain the functional index simply by performing a plurality of tasks that involve operations on the trigger UI 80.

[0088] In addition, the acquisition unit 20 acquires operation data including operation time data regarding the operation time required by the user to operate the trigger UI 80. In this case, since the operation time data of the user can be included in the operation data, Estimation The estimation accuracy of model 35 can be improved.

[0089] The acquiring unit 20 also acquires operation data including correct answer selection data regarding the number of times the user selected the normal correct answer option 72 and the correct answer option 82. In this case, the higher the correct answer selection rate that may be included in the correct answer selection data, the more normal the task execution function and task inhibition function are suggested to be.

[0090] The acquisition unit 20 also acquires operation data including the degree of deviation between the operation time data and the correct answer selection data when the learning user selects the correct answer option and the operation time data and the correct answer selection data when the target user selects the correct answer option. In this case, if the degree of deviation is large, it can be estimated that there is no correlation between the operation time data of the target user and the operation time data of the learning user. If the degree of deviation is small, it can be estimated with high accuracy the index representing the task execution function and the task inhibition function of the target user. In addition, if the task execution function and the task inhibition function are normal, it is estimated that the longer the operation time, the more likely the normal correct answer option 72 or the correct answer option 82 will be selected. Therefore, if the degree of deviation is large and the operation time (normal operation time) is long, the function estimation device 1 can estimate that the task execution function and the task inhibition function are weakened.

[0091] The acquiring unit 20 also acquires operation data including incorrect answer selection data regarding the number of times the user selected the same incorrect answer option 83 in multiple tasks. In this case, it can be estimated that the greater the incorrect answer repetition rate included in the incorrect answer selection data, the weaker the task inhibition function of the target user is.

[0092] Although the embodiment of the present disclosure has been described above, the present disclosure is not limited to the above embodiment. For example, the calculation unit 15 may calculate operation data related to the user's operation result obtained after the presentation unit 10 presents the task by the trigger UI 80 a predetermined first number of times or a predetermined first period or more. It may be assumed that the user is not necessarily adapted (not used) to the operation of the trigger UI 80 immediately after the introduction of the trigger UI 80 by the presentation unit 10. The operation data of each user immediately after the introduction of the trigger UI 80 (less than the first number of times or less than the first period) includes a mixture of the influence of the incorrect answer being induced by the trigger UI 80 and the influence of the adaptability of each user to the trigger UI 80.

[0093] On the other hand, the calculation unit 15 may calculate operation data related to a user's operation result obtained before the presentation unit 10 presents the task by the trigger UI 80 a predetermined second number of times or for a predetermined second period or more. When the user continues to use the trigger UI 80 after the presentation unit 10 introduces it, it may be assumed that the user remembers the form of the trigger UI 80. When the use of the estimation app presenting the trigger UI 80 is continued (for a second number of times or more or for a second period or more), the operation data of each user also includes a mixture of the influence of the incorrect answer being induced by the trigger UI 80 and the influence of the adaptability of each user to the trigger UI 80.

[0094] Therefore, in the learning unit 30, the learning user's operation data of the trigger UI 80 for less than the first number of times, less than the first period, more than the second number of times, or more than the second period is used. Estimation Although it is possible to train the model 35, the fitness of the training user reflected in the operation data may affect the model. Estimation Model 35 performance index Estimation There is a possibility of reduced accuracy.

[0095] Also, Estimation In the unit 40, using operation data of a target user at the beginning of the introduction of the trigger UI 80, which is less than a first number of times, less than a first period, more than a second number of times, or more than a second period, Estimation Although it is possible to input the data into the model 35, the influence of the fitness of the target user reflected in the operation data may cause the Estimation Model 35 performance index Estimation There is a possibility of reduced accuracy.

[0096] As a result, the calculation unit 15 calculates operation data regarding operation results of the training user and the target user obtained by performing the task by the trigger UI 80 for a first number of times or more and less than a second number of times, or for a first period of time or more and less than a second period of time, Estimation In the Model 35 EstimationIt is possible to improve accuracy. Note that the first number of times, the first period, the second number of times, or the second period may be determined based on, for example, a first threshold value or a second threshold value that is a threshold value related to the variance of the operation time obtained by the learning user.

[0097] Furthermore, the function estimation device 1 may not include the calculation unit 15. In this case, the acquisition unit 20 may not acquire the value calculated by the calculation unit 15 stored in the storage unit 90. The acquisition unit 20 may directly acquire operation data indicating the result of the user's operation for each task using the trigger UI 80 presented by the presentation unit 10. Estimation The unit 40 may have the function of the calculation unit 15.

[0098] Furthermore, the function estimation device 1 does not need to include the learning unit 30. In this case, Estimation The unit 40 may obtain an estimation model generated outside the function estimation device 1, and estimate the functional index of the target user using the estimation model. Furthermore, the function estimation device 1 may not use an estimation model generated by machine learning. In this case, the functional index of the target user may be estimated based on predetermined criteria for operation time, correct answer selection rate, number of repeated incorrect answers, etc. Estimation The unit 40 calculates a functional index of the target user based on the operation data obtained by the acquisition unit 20. Estimation For example, Estimation The unit 40 may weight the inverse of the correct answer selection rate, the operation time, the number of repeated incorrect answers, the degree of deviation in the negative direction, etc., and use the sum of these as the user's functional index. For example, by comparing the past operation data stored in the memory unit 90 with the latest operation data newly acquired, if the correct answer selection rate decreases, the operation time increases, the number of repeated incorrect answers increases, the degree of deviation becomes large in the negative direction, etc., Estimation The unit 40 determines that the functional index of the target user is smaller than the functional index obtained in the past. Estimation However, when task execution and inhibition functions are weakened, Estimation You may do so.

[0099] In addition, the acquisition unit 20 may not acquire operation data including operation time data via the calculation unit 15. In this case, the acquisition unit 20 acquires operation data other than the operation time data, and the learning unit 30 uses the operation data other than the operation time data of the learning user to Estimation Train the model 35. Estimation The unit 40 calculates a function index of the target user by using operation data other than the operation time data of the target user. Estimation Similarly, the acquisition unit 20 may not acquire the correct answer selection data, the incorrect answer selection data, and the above-mentioned deviation degree. In this case, the learning unit 30 may use the learning user's operation data other than the data not acquired by the acquisition unit 20. Estimation Train the model 35. Estimation The unit 40 calculates the functional index of the target user by using the operation data of the target user other than the data not acquired by the acquisition unit 20. Estimation You may do so.

[0100] The block diagrams used in the description of the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wireless, etc.). The functional block may be realized by combining the one device or the multiple devices with software.

[0101] Functions include, but are not limited to, judgment, determination, judgement, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the function of transmission is called a transmitting unit or a transmitter. As described above, there is no particular limitation on the method of realization of either of these.

[0102] For example, the function estimation device 1 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure. As shown in Fig. 8, the above-mentioned function estimation device 1 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, and a bus 1007.

[0103] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the function estimation apparatus 1 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

[0104] Each function in the function estimation device 1 is realized by loading a specific software (program) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communication via a communication device 1004, and control at least one of reading and writing of data in the memory 1002 and the storage 1003.

[0105] The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, and a register. For example, each function of the above-mentioned function estimation device 1 may be realized by the processor 1001.

[0106] The processor 1001 reads out a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the read out programs. As the program, a program that causes a computer to execute at least a part of the operations described in the above-mentioned embodiment is used. For example, each function of the function estimation device 1 may be realized by a control program stored in the memory 1002 and operated in the processor 1001. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may be transmitted from a network via a telecommunication line.

[0107] The memory 1002 is a computer-readable recording medium, and may be configured by at least one of, for example, a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), and a Random Access Memory (RAM). The memory 1002 may be called a register, a cache, a main memory (primary storage device), or the like. The memory 1002 can store a program (program code), a software module, or the like that is executable to implement an information providing method according to an embodiment of the present disclosure.

[0108] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, and a magnetic strip. Storage 1003 may be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0109] The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., in order to realize at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, the above-mentioned acquisition unit 20, etc. may be realized by the communication device 1004.

[0110] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0111] Each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0112] The function estimation device 1 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc., and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0113] Notification of information is not limited to the aspects / embodiments described in this disclosure and may be performed using other methods.

[0114] The order of the processing steps, sequences, flow charts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0115] Information, etc. may be output from a higher layer to a lower layer, or from a lower layer to a higher layer. Information, etc. may be input / output via multiple network nodes.

[0116] The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added. The output information may be deleted. The input information may be transmitted to another device.

[0117] The determination may be based on a value represented by a single bit (0 or 1), a Boolean (true or false) value, or a numerical comparison (e.g., comparison with a predetermined value).

[0118] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched according to execution. Notification of predetermined information (e.g., notification of "X") is not limited to explicit notification, and may be performed implicitly (e.g., by not notifying the predetermined information).

[0119] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented as modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is for illustrative purposes only and does not have any limiting meaning on the present disclosure.

[0120] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0121] Software, instructions, information, and the like may be transmitted and received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave, etc.), then these wired and / or wireless technologies are included within the definition of transmission media.

[0122] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0123] In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0124] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0125] The information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using corresponding other information.

[0126] The names used for the above parameters are not limiting in any way, and the formulas etc. using these parameters may differ from the formulas etc. explicitly disclosed in this disclosure.

[0127] The terms "determining" and "determining" as used in this disclosure may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), and the like. "Judgment" and "decision" may include the deeming of resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" may include the deeming of some action as having been "judged" or "decided." "Judgment" may be read as "assuming," "expecting," or "considering," etc.

[0128] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0129] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0130] Any reference to an element using designations such as "first" and "second" used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not mean that only two elements may be employed, nor that the first element must precede the second element in any way.

[0131] The "parts" in the configuration of each of the above devices may be replaced with "circuits" or "devices", etc.

[0132] When "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0133] In this disclosure, where articles are added by translation, such as "a," "an," and "the" in English, the disclosure may include that the nouns following these articles are in the plural form.

[0134] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." In addition, the term may mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0135] 1... function estimation device, 5... reception and display unit, 10... presentation unit, 15... calculation unit, 20... acquisition unit, 30... learning unit, 35... Estimation Model, 40… Estimation unit, 80...induced UI, 81...question sentence, 82...correct answer options, 83...incorrect answer options, 90...memory unit, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. a presentation unit that presents to the user a plurality of tasks for performing operations on a user interface that induces the user to give an incorrect answer; an acquisition unit that acquires operation data indicating results of operations performed by the user on the plurality of tasks presented by the presentation unit when the number of times the presentation unit has presented the tasks to the user is equal to or greater than a first number of times and less than a second number of times, or when a period during which the presentation unit has presented the tasks to the user is equal to or greater than a first period and less than a second period; an estimation unit that estimates indicators representing a task execution function and a task suppression function of the user based on the operation data of the user acquired by the acquisition unit; Equipped with The user interface includes: Questions and A correct answer option that the user should originally select for the question; and an incorrect answer option that reminds the user of the correct answer to the question by at least one element of color, decoration, expression, terminology, and arrangement indicated by the content of the question or the content of the correct answer option; The acquisition unit is operation time data relating to operation times required for each of the training user and the target user to operate the user interface; Correct answer selection data regarding the number of times the learning user and the target user selected the correct answer option; a degree of deviation between the correct answer selection data of the learning user and the correct answer selection data of the target user with respect to the operation time data when each of the learning user and the target user selects the correct answer option; and acquiring the operational data including the estimation unit estimates indexes representing a task execution function and a task inhibition function of the target user based on the operation time data and the degree of deviation when the target user selects the correct answer option; Functional estimation device.

2. the function estimation device further includes a learning unit configured to learn an estimation model for estimating indexes representing task execution functions and task inhibition functions, the indexes representing task execution functions and task inhibition functions acquired for the learning user being used as explanatory variables and indexes representing task execution functions and task inhibition functions being acquired for the learning user being used as objective variables; The function estimation device according to claim 1 , wherein the estimation unit estimates indicators representing the task execution function and task inhibition function of the target user by applying the operation data of the target user acquired by the acquisition unit to the estimation model obtained by learning by the learning unit.

3. The function estimation device according to claim 1 , wherein the acquisition unit acquires the operation data including operation time data that is data regarding an operation time required for the user to operate the user interface.

4. The function estimation device according to claim 1 , wherein the acquisition unit acquires the operation data including correct answer selection data regarding the number of times the user selected the correct answer option.

5. The function estimation device according to any one of claims 1 to 4, wherein the acquisition unit acquires the operation data including incorrect answer selection data, the incorrect answer selection data being data regarding the number of times the user selected the same incorrect answer option in multiple tasks.

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