Information processing device, information processing method, and program

Abstract

To recommend a lesson suitable for a person who is to take some lesson.SOLUTION: An information processing device comprises: an acquisition unit 131 which acquires actual result data which indicates an actual result that a user U1 who is an object to which a lesson is recommended, has browsed each of a plurality of browsing contents; an identification unit 132 which identifies one or more browsing contents in which the actual result data satisfies a predetermined condition; and a determination unit 133 which determines a lesson to be recommended to the user U1, on the basis of types of the one or more browsing contents.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

[0002] BACKGROUND ART Conventionally, a system has been known that allows users to search for lessons by setting conditions (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-12618 Summary of the Invention [Problem to be solved by the invention]

[0004] When searching for lessons using conventional systems, it is possible to find lessons that meet certain conditions, such as the day of the week or time of day that the lesson can be attended. However, it is not always possible to find lessons that the person taking the lesson (e.g., a child) can continue for a long time.

[0005] The present invention has been made in consideration of these points, and aims to make it possible to recommend lessons that are suitable for people who take lessons. [Means for solving the problem]

[0006] An information processing device of a first aspect of the present invention is an information processing device for recommending lessons suitable for a target person, and has an acquisition unit that acquires performance data indicating the target person's performance in viewing each of a plurality of viewing contents, an identification unit that identifies one or more viewing contents for which the performance data satisfies a predetermined condition, and a determination unit that determines a lesson to recommend to the target person based on the type of the one or more viewing contents.

[0007] The system may further include a memory unit that stores a recommendation database in which the types of viewing content are associated with recommended hobby candidates, and the determination unit may determine the hobby to recommend to the subject by selecting the hobby candidate associated with the one or more types of viewing content identified by the identification unit in the recommendation database.

[0008] The performance data may include the length of time that the subject person viewed each of the multiple viewing contents, and the identification unit may identify the one or more viewing contents that satisfy the specified condition that the length indicated by the performance data is relatively long.

[0009] The performance data may include the number of times the subject person has viewed each of the plurality of viewing contents, and the identification unit may identify the one or more viewing contents that satisfy the specified condition that the number of times indicated by the performance data is relatively high.

[0010] The performance data may include the subject's level of concentration measured while the subject is viewing each of the plurality of viewing contents, and the identification unit may identify the one or more viewing contents that satisfy the specified condition that the level of concentration is relatively high.

[0011] The performance data may include the subject's satisfaction level measured while the subject is viewing each of the plurality of viewing contents, and the identification unit may identify the one or more viewing contents that meet the specified condition that the satisfaction level is relatively high.

[0012] The performance data may include at least one of the length of time that the subject viewed each of the plurality of viewing contents or the number of times the subject viewed each of the plurality of viewing contents, and at least one of the subject's concentration or satisfaction measured while the subject viewed each of the plurality of viewing contents, and the identification unit may identify the one or more viewing contents that satisfy the specified condition that the level of interest is relatively high, as determined by subtracting the level of dissatisfaction determined based on at least one of the concentration or satisfaction from the level of viewing determined based on at least one of the length or the number of times.

[0013] The information processing device may further have a memory unit that stores a recommendation database in which a person's activity tendencies are associated with recommended hobby candidates, the acquisition unit may further acquire activity tendency data indicating the activity tendencies of the subject's family, and the determination unit may determine the hobby to recommend to the subject based further on the hobby candidate associated with the activity tendencies indicated by the activity tendency data in the recommendation database.

[0014] The information processing device may further have a memory unit that stores a recommendation database in which a person's financial leeway is associated with recommended hobby candidates, the acquisition unit may further acquire leeway data indicating the financial leeway of the subject's family, and the determination unit may determine the hobby to recommend to the subject based on the hobby candidates associated with the financial leeway indicated by the leeway data in the recommendation database.

[0015] An information processing device of a second aspect of the present invention is an information processing device for recommending lessons suitable for a subject, and has an acquisition unit that acquires performance data indicating the subject's performance in viewing each of multiple viewing contents, and a determination unit that determines, as the lesson to recommend to the subject, a lesson that has a relatively high probability of being continued for a predetermined period or more based on the content of one or more lessons and the expected duration of the lesson, which are output from a machine learning model created by machine learning using as training data performance data for learning indicating the performance of multiple people in viewing each of multiple viewing contents, content data indicating the content of the lessons that each of the multiple people has continued, and duration data indicating the period for which the lesson has been continued.

[0016] An information processing device of a third aspect of the present invention is an information processing device for recommending hobbies suitable for a subject, and includes an acquisition unit that acquires performance data indicating the subject's performance in viewing each of multiple viewing contents, and a determination unit that inputs the performance data into a machine learning model created by machine learning using as training data performance data indicating the performance of multiple people in viewing each of multiple viewing contents and hobbies data indicating the content of hobbies that each of the multiple people has continued for a predetermined period or more, and determines, as the hobbies to recommend to the subject, hobbies that have a relatively high probability of being continued for the predetermined period or more, which are output from the machine learning model.

[0017] An information processing method of a fourth aspect of the present invention is an information processing method executed by a computer for recommending a hobby suitable for a target person, and includes the steps of obtaining performance data indicating the target person's performance in viewing each of a plurality of viewing contents, identifying one or more of the viewing contents for which the performance data satisfies predetermined conditions, and determining a hobby to recommend to the target person based on the type of the one or more viewing contents.

[0018] A fifth aspect of the program of the present invention is a program for recommending a hobby suitable for a target person, which causes a computer to function as an acquisition unit that acquires performance data indicating the target person's performance in viewing each of multiple viewing contents, an identification unit that identifies one or more viewing contents for which the performance data satisfies predetermined conditions, and a determination unit that determines a hobby to recommend to the target person based on the type of the one or more viewing contents. [Effects of the Invention]

[0019] According to the present invention, it is possible to recommend lessons suitable for a person who is taking lessons. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram for explaining an overview of an information processing system S. [Figure 2] 1 is a diagram illustrating a configuration of an information processing device 1. FIG. [Figure 3] FIG. 10 is a diagram illustrating an example of content data. [Figure 4] FIG. 10 is a diagram illustrating an example of a recommendation database. [Figure 5] FIG. 10 is a diagram illustrating an example of data showing costs required for lessons. [Figure 6] FIG. 10 is a diagram illustrating an example of a performance database. [Figure 7] FIG. 10 is a diagram showing an example of activity trends of a user U2. [Figure 8] 3 is a flowchart showing a processing flow in the information processing device 1. DETAILED DESCRIPTION OF THE INVENTION

[0021] [Outline of Information Processing System S] FIG. 1 is a diagram for explaining an overview of an information processing system S. The information processing system S is a system for recommending suitable lessons to people who wish to take up lessons. In this specification, the person to whom the lesson is to be recommended (i.e., the target person) is referred to as user U1. User U1 is, for example, an infant, but user U1 may also be a child other than an infant or an adult. Also, in this specification, a person who is considering an extracurricular activity for user U1 is referred to as user U2. User U2 is, for example, user U1's parent, but may also be someone other than a parent.

[0022] The information processing system S includes an information processing device 1, a viewing terminal 2, and an analysis terminal 3. The information processing device 1 is a computer, such as a server, that determines which lessons to recommend. The information processing device 1 provides content for user U1 to view, and notifies user U2 of lessons suitable for user U1 in response to a request from user U2. A lesson suitable for user U1 is one that is relatively likely to be continued by user U1 over a long period of time (for example, a period set by user U2).

[0023] The information processing device 1 determines lessons suitable for the user U1 by referring to various data registered in the database DB. The database DB may be stored in the information processing device 1 or in an external device different from the information processing device 1.

[0024] The viewing terminal 2 is a terminal for user U1 to view content, and is, for example, a smartphone, tablet, or personal computer. The viewing terminal 2 displays content transmitted from the information processing device 1 via a network (for example, the Internet or a mobile phone network). The content is, for example, image data of a picture book. The image data may be data of a still image or video.

[0025] The analysis terminal 3 is a terminal used by the user U2, such as a smartphone, tablet, or personal computer. The analysis terminal 3 receives operations from the user U2 to receive recommendations for lessons, and displays information indicating lessons recommended by the information processing device 1, received from the information processing device 1 via the network.

[0026] An overview of the operation of the information processing system S will be described below with reference to Fig. 1. First, the information processing device 1 transmits a plurality of contents to the viewing terminal 2 for viewing by the user U1 ((1) in Fig. 1). The plurality of contents may be selected by the user U1 or the user U2, or may be randomly selected by the information processing device 1. The plurality of contents may be selected by the information processing device 1 from a plurality of contents corresponding to the type of content selected by the user U1 or the user U2, based on the type of content selected by the user U1 or the user U2.

[0027] The viewing terminal 2 displays the content received from the information processing device 1 on the display ((2) in FIG. 1). The user U1 views the content displayed by the viewing terminal 2 ((3) in FIG. 1). The viewing terminal 2 detects when the user U1 starts viewing the content and when the user U1 finishes viewing the content, and transmits viewing data indicating the detection results to the information processing device 1 ((4) in FIG. 1).

[0028] The viewing terminal 2 detects that user U1 has started viewing the content and that user U1 has finished viewing the content, for example, based on the user U1's operations of starting and ending viewing. The viewing terminal 2 may also capture an image of user U1 with a camera while the content is being displayed, and detect that user U1 has started viewing the content and that user U1 has finished viewing the content based on image data obtained by capturing the image.

[0029] Based on the browsing data, the information processing device 1 selects one or more extracurricular activities suitable for user U1 from among a large number of candidate extracurricular activities. Details will be described later, but the information processing device 1 determines that an extracurricular activity is suitable for user U1 if the extracurricular activity has a relatively high relevance to a type of content that the browsing data indicates is viewed by user U1 for a relatively long period of time or a relatively long number of times. The information processing device 1 may select some extracurricular activities from the multiple extracurricular activities determined to be suitable for user U1 based on various conditions set by user U2.

[0030] The information processing device 1 transmits analysis data including information indicating one or more lessons determined to be suitable for the user U1 to the analysis terminal 3 ((5) in FIG. 1). The information indicating the lessons may be the name of the recommended lesson, or may be information indicating a place or institution where the recommended lesson can be taken. The analysis terminal 3 displays the received analysis data on its display ((6) in FIG. 1).

[0031] User U2 can understand the recommended lessons by looking at the displayed analysis data. If multiple lessons are displayed, user U2 may select the lesson that he or she wants user U1 to learn from the multiple lessons ((7) in FIG. 1). The analysis terminal 3 sends selection data indicating the selection to the information processing device 1 ((8) in FIG. 1). The information processing device 1 may provide user U2 with information about places where the selected lesson can be taken, or may register the selected lesson in the database DB. The configuration and operation of the information processing device 1 will be described in detail below.

[0032] [Configuration of information processing device 1] 2 is a diagram showing the configuration of the information processing device 1. The information processing device 1 has a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 has a content providing unit 130, an acquisition unit 131, an identification unit 132, and a determination unit 133.

[0033] Communication unit 11 includes a communication interface for transmitting and receiving data between viewing terminal 2 and analysis terminal 3 via a network. Communication unit 11 inputs data received from viewing terminal 2 or analysis terminal 3 to acquisition unit 131. Communication unit 11 also transmits data input from determination unit 133 to viewing terminal 2 or analysis terminal 3.

[0034] The storage unit 12 has a storage medium such as a ROM (Read Only Memory), a RAM (Random Access Memory), or an SSD (Solid State Drive). The storage unit 12 stores a program executed by the control unit 13. The storage unit 12 also stores a database including various data used by the control unit 13 to determine lessons to recommend. The storage unit 12 stores, for example, content data indicating attributes of the browsing content to be browsed by the user U1, along with the browsing content.

[0035] Fig. 3 is a diagram showing an example of content data, in which the content name, the genre to which each piece of content belongs, the length of the content, and the difficulty level of the content are associated with each other.

[0036] The storage unit 12 may store a recommendation database in which types of content to be viewed are associated with candidate lessons to be recommended. FIG. 4 is a diagram showing an example of the recommendation database. In the recommendation database shown in FIG. 4, genres of content to be viewed are associated with the recommendation level of each of a plurality of lessons. The recommendation level is a numerical value indicating the degree of recommendation, and the higher the numerical value, the higher the degree of recommendation.

[0037] The recommendation database is created, for example, based on the relationship between the past content viewing records of many users U1 and the duration of the lessons they have continued, so that the higher the average duration of lessons continued by people who have viewed at least one of the specified content for a relatively long time or number of times, the higher the recommendation level. If the genre of the viewed content is baseball, baseball has the highest recommendation level, followed by soccer, which is common with baseball in that it is a ball game, and the recommendation levels of skating and gymnastics, which are common with baseball in that it is a sport, are higher than the recommendation levels of piano, chorus, and calligraphy, which are non-sports. On the other hand, if the genre of the viewed content is piano, piano has the highest recommendation level, followed by chorus, which is common with piano in that it is music.

[0038] In the recommendation database, activity trends of user U1 or a person associated with user U1 (e.g., user U2) may be associated with recommended extracurricular activities. Activity trends are represented, for example, by the details of competitions or concerts in which user U2 has participated, sports events that user U2 has attended, performances at concerts that user U2 has attended, television viewing history, the details of social media sites or websites that user U2 has accessed, or the details of emails that user U2 has sent or received.

[0039] For example, the recommendation database is configured so that if user U1 or user U2 tends to have many opportunities to watch sports, the recommendation level of sports-related lessons will be high and the recommendation level of lessons other than sports will be low.The recommendation database is configured so that if user U1 or user U2 tends to have many opportunities to go to concerts, the recommendation level of music-related lessons will be relatively high.

[0040] Similarly, the recommendation database is configured so that if user U2 has a strong tendency to watch sports programs on television, a strong tendency to access sports-related social networking sites or websites, or a strong tendency to exchange emails about sports-related topics, the recommendation level for sports-related lessons will be high and the recommendation level for lessons not related to sports will be low.

[0041] In the recommendation database, the financial leeway of a person associated with user U1 (e.g., user U2) may be associated with recommended extracurricular activities. Financial leeway is represented, for example, by the amount obtained by subtracting the annual expenses from the annual income of user U1's parents. As an example, the recommendation database is configured so that when financial leeway is high, all extracurricular activities are highly recommended, and when financial leeway is low, skating, which is a relatively expensive extracurricular activity, is less highly recommended.

[0042] The storage unit 12 may store data indicating the cost of each lesson associated with the user U1. FIG. 5 is a diagram showing an example of data indicating the cost of lessons. In FIG. 5, each type of lesson is associated with the annual tuition fee, transportation cost, total cost, and type of access. The cost of the lesson may also include the cost of equipment required for the lesson.

[0043] The storage unit 12 may also store performance data indicating the performance of the user U1 in viewing the viewing content in association with the user U1. The performance data may include, for example, at least one of the length of time the user U1 viewed each of the plurality of viewing contents or the number of times the user U1 viewed each of the plurality of viewing contents.

[0044] The performance data may include at least one of the user U1's concentration level measured while the user U1 is viewing each of the multiple viewing contents, or the user U1's satisfaction level measured while the user U1 is viewing each of the multiple viewing contents. The performance data may include at least one of the length of time the user U1 viewed each of the multiple viewing contents or the number of times the user U1 viewed each of the multiple viewing contents, and at least one of the user U1's concentration level or satisfaction level measured while the user U1 is viewing each of the multiple viewing contents.

[0045] Fig. 6 is a diagram showing an example of an achievement database. Fig. 6(a) shows achievement data for a first user U1, and Fig. 6(b) shows achievement data for a second user U1. The achievement data shown in Fig. 6 associates the genre of content that user U1 can view, the time spent by user U1 viewing, the number of times user U1 viewed, and the level of concentration and satisfaction while user U1 was viewing. The level of concentration and satisfaction are shown on a five-point scale from 1 point to 5 points, with 5 points representing the highest level of concentration or satisfaction and 1 point representing the lowest level of concentration or satisfaction.

[0046] The performance data in Figure 6(a) shows that soccer-related content is viewed more frequently and for longer periods of time than content in other genres, while the performance data in Figure 6(b) shows that piano-related content is viewed more frequently and for longer periods of time than content in other genres.

[0047] The configuration and operation of the control unit 13 will be described below with reference to Fig. 2. The control unit 13 has, for example, a CPU (Central Processing Unit), and functions as a content providing unit 130, an acquiring unit 131, an identifying unit 132, and a determining unit 133 by executing programs stored in the storage unit 12.

[0048] The content providing unit 130 provides viewing content. As an example, the content providing unit 130 first causes the viewing terminal 2 or the analysis terminal 3 to display a list of viewable content as shown in FIG. 3 via the communication unit 11. When the content providing unit 130 acquires, from the viewing terminal 2 or the analysis terminal 3, instruction data that specifies the viewing content to be displayed on the viewing terminal 2, the content providing unit 130 reads out the specified viewing content from the storage unit 12 and transmits the read viewing content to the viewing terminal 2.

[0049] The content providing unit 130 may transmit randomly selected viewing content without receiving designation of viewing content, or viewing content selected based on rules designated by the user U2, to the viewing terminal 2. When an instruction to stop viewing the viewing content is acquired from the viewing terminal 2 or the analysis terminal 3, the content providing unit 130 terminates transmission of the viewing content.

[0050] The acquisition unit 131 acquires performance data indicating the performance of user U1 viewing each of a plurality of viewing contents. The acquisition unit 131 notifies the acquired performance data to the identification unit 132. The acquisition unit 131 notifies the identification unit 132 of the performance data via the storage unit 12, for example, by storing the performance data in the storage unit 12 in association with user U11.

[0051] The acquisition unit 131 acquires, for example, the time from when the content providing unit 130 starts transmitting the viewing content to when the content providing unit 130 finishes providing the viewing content as the performance data. The acquisition unit 131 may acquire, as the performance data, the viewing time or the number of views measured by the viewing terminal 2. The acquisition unit 131 may acquire, as the performance data, the concentration level or satisfaction level of the user U1 who is viewing the viewing content, which is specified by the viewing terminal 2.

[0052] The acquisition unit 131 may acquire the performance data by identifying the viewing time or the number of views based on the time from when the content providing unit 130 starts transmitting the viewed content to when the content providing unit 130 finishes providing the viewed content. After the content providing unit 130 starts transmitting the viewed content, the acquisition unit 131 may increment by 1 the number of views of the viewed content of the same genre in the performance data stored in the storage unit 12.

[0053] When the viewed content includes multiple pages, the acquisition unit 131 determines, for example, the time when a page related to the content of a predetermined genre is viewed as the time when the user U1 viewed the viewed content of the predetermined genre. The acquisition unit 131 may also determine the time when all pages of the viewed content including the content of the genre are viewed as the time when the user U1 viewed the viewed content.

[0054] When the user U1 views at least one page containing content of a predetermined genre among the multiple pages, the acquisition unit 131 increments the number of times the user U1 has viewed the viewed content of the genre in the achievement data by 1. The acquisition unit 131 may increment the number of times the user U1 has viewed the viewed content of the genre in the achievement data by 1 every time all pages of the viewed content containing content of the genre are viewed.

[0055] The acquisition unit 131 may acquire, as the performance data, captured image data created by the viewing terminal 2 capturing an image of the user U1 while the viewing content is displayed on the viewing terminal 2. The acquisition unit 131 identifies at least one of the concentration level or satisfaction level of the user U1 by analyzing the captured image data, and stores the concentration level or satisfaction level in the storage unit 12 in association with the user U1 and the viewing content. The acquisition unit 131 increases the concentration level value in the performance data as the percentage of time that the user U1 spends gazing at the screen increases. The acquisition unit 131 increases the satisfaction level value as the percentage of time that the user U1 spends smiling increases.

[0056] The acquisition unit 131 may acquire activity tendency data indicating activity tendencies of the family of user U1. The acquisition unit 131 acquires, from the analysis terminal 3, data indicating activity tendencies of user U2 identified by the analysis terminal 3 based on, for example, the content of an operation performed by user U2 on the analysis terminal 3 (for example, the content of an accessed website). The acquisition unit 131 stores the acquired activity tendency data in the storage unit 12 in association with at least one of user U1 and user U2.

[0057] The acquisition unit 131 may acquire financial margin data indicating the financial margin of a family member of the user U1 (for example, a user U2). The acquisition unit 131 acquires, for example, financial margin data input by the user U2 to the analysis terminal 3. The acquisition unit 131 stores the acquired financial margin data in the storage unit 12 in association with at least one of the user U1 and the user U2.

[0058] The identification unit 132 identifies one or more viewed contents whose history data acquired by the acquisition unit 131 satisfies a predetermined condition. The predetermined condition is, for example, that the degree of viewing by user U1 is relatively large, or that the degree of viewing is equal to or greater than a threshold. The degree of viewing increases, for example, as the history data indicates, for example, the longer at least one of the time or number of times that user U1 viewed the content. The degree of viewing may also increase as the degree of concentration or satisfaction of user U1 while viewing the content increases.

[0059] That is, the predetermined condition is, for example, a condition that the length of time that user U1, as indicated by the performance data, has viewed the viewed content is relatively long. In the example shown in FIG. 6, the total viewing time of soccer-related viewed content for user U1 corresponding to FIG. 6(a) is 110 minutes, which is longer than the viewing time of viewed content in other genres. Therefore, the identification unit 132 identifies the soccer-related viewed content as the viewed content that satisfies the predetermined condition. On the other hand, for user U1 corresponding to FIG. 6(b), the total viewing time of piano-related viewed content is 60 minutes, which is longer than the viewing time of viewed content in other genres. Therefore, the identification unit 132 identifies the piano-related viewed content as the viewed content that satisfies the predetermined condition.

[0060] The predetermined condition may be that the number of times that user U1, as indicated by the performance data, has viewed the viewed content is relatively high. In the example shown in FIG. 6, the number of times that user U1, corresponding to FIG. 6(a), has viewed the viewed content related to soccer is 8, which is more than the number of times that user U1 has viewed the viewed content in other genres. Therefore, the identification unit 132 identifies the viewed content related to soccer as the viewed content that satisfies the predetermined condition. On the other hand, for user U1, corresponding to FIG. 6(b), the number of times that user U1, corresponding to FIG. 6(b), has viewed the viewed content related to piano is 6, which is more than the number of times that user U1 has viewed the viewed content in other genres. Therefore, the identification unit 132 identifies the viewed content related to piano as the viewed content that satisfies the predetermined condition.

[0061] The predetermined condition may be a condition that the level of concentration of user U1, as indicated by the performance data, while viewing the viewed content is relatively high. In the example shown in FIG. 6, the average level of concentration of user U1, corresponding to FIG. 6(a), while viewing soccer-related viewed content is 4.7 points, which is higher than the average level of concentration while viewing viewed content of other genres. Therefore, the identification unit 132 identifies the soccer-related viewed content as the viewed content that satisfies the predetermined condition. On the other hand, for user U1, corresponding to FIG. 6(b), the average level of concentration while viewing piano-related viewed content is 4.5 points, which is higher than the average level of concentration while viewing viewed content of other genres. Therefore, the identification unit 132 identifies the piano-related viewed content as the viewed content that satisfies the predetermined condition.

[0062] The predetermined condition may be a condition that the satisfaction level of user U1 while viewing the viewed content, as indicated by the performance data, is relatively high. In the example shown in FIG. 6, the average satisfaction level of user U1, corresponding to FIG. 6(a), while viewing soccer-related viewed content is 4.3 points, which is higher than the average satisfaction level while viewing viewed content of other genres. Therefore, the identification unit 132 identifies the soccer-related viewed content as the viewed content that satisfies the predetermined condition. On the other hand, for user U1, corresponding to FIG. 6(b), the average satisfaction level while viewing piano-related viewed content is 4.5 points, which is higher than the average satisfaction level while viewing viewed content of other genres. Therefore, the identification unit 132 identifies the piano-related viewed content as the viewed content that satisfies the predetermined condition.

[0063] The predetermined condition may be that the degree of browsing calculated by combining multiple pieces of information among the browsing time, the number of times of browsing, the concentration level, and the satisfaction level is relatively high. As an example, the identification unit 132 calculates the degree of browsing by the following formula: Viewing rate = (a1 × H1) + (a2 × H2) + (a3 × H3) + b × C Here, H1, H2, and H3 are the times spent viewing each of the three pieces of content in a specific genre, and a1, a2, and a3 are coefficients corresponding to the concentration or satisfaction of user U1 while viewing each of the three pieces of content. C is the number of times the content in that genre was viewed, and b is a coefficient.

[0064] It is considered that the less time user U1 looks away and the less body movement user U1 makes while viewing the viewing content, the more focused user U1 is on the viewing content. On the other hand, it is considered that user U1 becomes restless while viewing viewing content that does not interest him, and the time (L) during which his eyes look away from the viewing content and the time (M) during which his body moves at a predetermined amount or more become longer. Therefore, the acquisition unit 131 may determine user U1's level of interest in viewing content of a predetermined genre based on the degree of viewing and user U1's level of dissatisfaction.

[0065] Specifically, the identification unit 132 may identify one or more viewed contents that satisfy a predetermined condition that the degree of interest determined by subtracting the degree of dissatisfaction determined based on at least one of the concentration level and the satisfaction level from the degree of viewing determined based on at least one of the length and the number of times is relatively high. The degree of interest is calculated, for example, using the following formula: Interest level = Viewing level - Dissatisfaction level = Viewing level - (c × L + d × M) Here, c and d are predetermined coefficients.

[0066] The determination unit 133 determines a lesson to recommend to the user U1 by selecting a lesson candidate associated with one or more types of viewed content identified by the identification unit 132 in the recommendation database. The determination unit 133 notifies the analysis terminal 3 of the determined lesson via the communication unit 11. When the type of viewed content identified by the identification unit 132 is "baseball," the determination unit 133 determines to recommend "baseball" and "soccer," which are lesson candidate candidates corresponding to the content genre "baseball" shown in FIG. 4 and have lesson recommendation degrees equal to or greater than a predetermined threshold (e.g., 5). The predetermined threshold may be a value set by the user U2 via the analysis terminal 3, a value set by a business operator that provides services using the information processing device 1, or a value set at the time of manufacturing the information processing device 1.

[0067] The determination unit 133 may notify the analysis terminal 3 of the multiple lessons to be recommended in association with the lesson recommendation degree. The determination unit 133 may notify the analysis terminal 3 of the multiple lessons to be recommended in a state where the lessons are arranged in descending order of the lesson recommendation degree.

[0068] The period during which user U1 can continue a lesson depends on factors other than user U1's interest in the content being viewed. For example, the period during which user U1 can continue a lesson depends on the behavioral or preference tendencies of user U2, the relationship between the cost of the lesson and user U2's financial leeway, the distance required to travel to take the lesson, etc. Therefore, the determination unit 133 may determine the lesson to recommend based on these factors as well.

[0069] The determination unit 133 determines the lesson to recommend to user U1 based, for example, on a candidate lesson associated with the activity tendency indicated by the activity tendency data in the recommendation database. For example, if user U2 tends to frequently watch sports, the determination unit 133 preferentially recommends a lesson related to sports. If user U2 tends to frequently attend concerts, the determination unit 133 preferentially recommends a lesson related to music. Similarly, if user U2 tends to watch sports programs on television, to access sports-related social networking sites or websites, or to exchange emails about sports-related topics, the determination unit 133 preferentially recommends a lesson related to sports.

[0070] FIG. 7 is a diagram showing an example of the activity tendency of user U2. Circles in FIG. 7 indicate activities that the father or mother relatively frequently engage in. The father shown in FIG. 7(a) has a strong interest in soccer, and the mother has a strong interest in skating. In such a case, the decision unit 133 determines to preferentially recommend soccer or skating. The father shown in FIG. 7(b) has a strong interest in choral singing, and the mother has a strong interest in piano and choral singing. In such a case, the decision unit 133 determines to preferentially recommend piano or choral singing.

[0071] The activity tendency may be calculated as a parent activity tendency value by integrating the activity tendencies of the father and the mother. As an example, the determination unit 133 calculates the parent activity tendency value for each extracurricular activity category using the following formula: Parents' activity tendency score = e × father's activity level + f × mother's activity level + g × father's past activity level + h × mother's past activity level

[0072] In the above formula, e, f, g, and h are coefficients. The activity amount is, for example, given a score of 1 when there is a circle in FIG. 7 and a score of 0 when there is no circle, but may be expressed by multiple scores depending on the activity amount. The determination unit 133 determines to preferentially recommend lessons in a genre with a high parent activity tendency value.

[0073] The determination unit 133 may determine a lesson to recommend to user U1 based on the lesson candidate associated with the financial leeway indicated by the leeway data in the recommendation database. The determination unit 133 tentatively determines multiple lesson candidate based on, for example, one or more types of browsing content identified by the identification unit 132. In such a case, the determination unit 133 does not recommend any of the tentatively determined lesson candidate candidates that do not match user U2's financial leeway.

[0074] As an example, suppose the determination unit 133 provisionally determines soccer and skating as candidate extracurricular activities based on the type of one or more browsed content identified by the identification unit 132. As shown in FIG. 5, the annual cost of learning soccer is 100,000 yen, while the annual cost of learning skating is 660,000 yen. If user U2 has a high level of financial leeway and the amount obtained by subtracting living expenses from annual income is greater than 660,000 yen, the determination unit 133 determines to recommend both soccer and skating. If user U2 has a low level of financial leeway and the amount obtained by subtracting living expenses from annual income is less than 660,000 yen, the determination unit 133 determines to recommend soccer and not to recommend skating. By operating in this manner, the determination unit 133 can prevent the recommendation of extracurricular activities that user U2 will have difficulty continuing due to a lack of financial leeway.

[0075] However, if the place where the lesson is held is far from the user's home and requires a long travel time, it may be difficult to continue the lesson. Therefore, the determination unit 133 may determine the lesson to recommend based on the distance from the user's home to the place of the lesson or the time required for travel. As an example, the determination unit 133 may provisionally determine soccer, skating, piano, and singing as lesson candidate lessons based on the type of one or more viewing content identified by the identification unit 132. As shown in FIG. 5, soccer and singing can be taken by walking. In such a case, the determination unit 133 preferentially recommends soccer and singing.

[0076] Furthermore, the determination unit 133 may determine the lesson to recommend based on the quality of the environment, such as whether or not there is an appropriate coach in a place where the lesson can be taken within a threshold distance or travel time from the user U1. As an example, the determination unit 133 determines the lesson to be recommended when the distance or travel time from the user U1 is within a first threshold and the quality of the environment is equal to or greater than a second threshold.

[0077] [Processing flow in information processing device 1] Fig. 8 is a flowchart showing the flow of processing in the information processing device 1. The flowchart shown in Fig. 8 starts after the content providing unit 130 provides browsing content to the user U1.

[0078] The acquiring unit 131 acquires from the viewing terminal 2 performance data indicating that the user U1 has started or finished viewing the viewing content (S1). The identifying unit 132 identifies the viewing content that satisfies a predetermined condition, such as the level of interest of the user U1 being equal to or greater than a threshold Ta1, based on at least one of the length of time, number of times, concentration during viewing, and satisfaction level indicated by the performance data (S2).

[0079] The determination unit 133 refers to the recommendation database and provisionally selects one candidate lesson to recommend based on the type of viewing content identified by the identification unit 132 (S3). The determination unit 133 determines whether the financial leeway of user U2 is equal to or greater than a threshold Tb1 for the provisionally selected lesson (S4). If the financial leeway is equal to or greater than the threshold Tb1 (YES in S4), the determination unit 133 proceeds to S7.

[0080] If the financial surplus is less than the threshold Tb1 and equal to or greater than the threshold Tb2 (YES in S5), the determination unit 133 determines whether the interest level is equal to or greater than a threshold Ta2 that is greater than the threshold Ta1 (S6). If the determination unit 133 determines that the interest level is equal to or greater than the threshold Ta2 (YES in S6), the process proceeds to S7 because the interest level is sufficiently high even if the financial surplus is not very high.

[0081] In S7, the decision unit 133 determines whether the candidate lesson location is easy to commute to based on the distance or travel time from the home to the lesson location (S7). If the decision unit 133 determines that the location is easy to commute to (YES in S7), the decision unit 133 decides to recommend the provisionally selected lesson candidate (S8). If the decision unit 133 determines NO in S5, S6, and S7, the decision unit 133 decides not to recommend the provisionally selected lesson candidate (S9).

[0082] If there are other candidate lessons (YES in S10), the determination unit 133 repeats the processes from S4 to S9. If there are no other candidate lessons (NO in S10), the determination unit 133 ends the process.

[0083] [Variations] In the above explanation, the control unit 13 determines the lesson to recommend based on various factors related to user U1 or user U2 by referring to the recommendation table, but the control unit 13 may also determine the lesson to recommend by using a machine learning model.

[0084] The determination unit 133 may, for example, input performance data into the machine learning model and, based on the content of one or more lessons and the expected duration of the lessons output from the machine learning model, determine, as lessons to recommend to user U1, lessons that are relatively likely to be continued for a predetermined period or longer. The performance data may, for example, associate the content viewed by user U1 with the viewing time and the number of views. The performance data may include the level of concentration or satisfaction. The performance data may also be captured image data capturing the facial expression of user U1 while user U1 is viewing.

[0085] This machine learning model is created by machine learning using training data that indicates the number of times a plurality of people have viewed each of a plurality of viewing contents, content data that indicates the details of the lessons that each of the plurality of people has continued, and period data that indicates the period for which the lesson has been continued as training data. When such machine learning model is input, it can output a probable period for which another user U1, who has a similar track record to the inputted performance data, continued the lesson. Therefore, by using such a machine learning model, the determination unit 133 can more easily select a lesson that user U1 is likely to continue for a long time.

[0086] The machine learning model may be created by learning using, as training data, activity tendency data, financial leeway, accessibility to a place where the lesson can be taken (i.e., distance or travel time), etc. in addition to the performance data. In this case, the determination unit 133 inputs the activity tendency data, financial leeway, and accessibility to a place where the lesson can be taken into the machine learning model in addition to the performance data. By using such a machine learning model, the determination unit 133 further increases the probability that user U1 can select a lesson that he or she can continue for a long time.

[0087] The machine learning model may be created by machine learning using training data indicating the number of times a plurality of people have viewed each of a plurality of viewing contents and lesson data indicating the details of lessons that each of the plurality of people has continued for a predetermined period of time as training data. The predetermined period is a period set by the user U2 or a period determined by a provider that provides services using the information processing device 1, such as three years.

[0088] The decision unit 133 may input performance data into this machine learning model and determine, as the lesson to recommend to user U1, a lesson that is relatively likely to be continued by user U1 for a predetermined period or longer, which is output from the machine learning model. By using such a machine learning model, the decision unit 133 increases the probability of selecting a lesson that user U1 can continue for a predetermined period or longer. Note that this machine learning model may also be created by learning using, in addition to performance data, activity tendency data, financial availability, accessibility to a place where the lesson can be taken (i.e., distance or travel time), etc. as training data.

[0089] [Effects of information processing device 1] As described above, in the information processing device 1, the identification unit 132 identifies one or more pieces of viewing content for which performance data indicating the performance of user U1 viewing the viewing content satisfies a predetermined condition, and the determination unit 133 determines a lesson to recommend to user U1 based on the type of the one or more pieces of viewing content. By configuring the information processing device 1 in this way, even if user U1 is an infant who is unable to determine what lessons interest him or her, the information processing device 1 is more likely to be able to recommend a lesson suitable for user U1.

[0090] Furthermore, this invention will make it possible to contribute to Goal 9 of the United Nations' Sustainable Development Goals (SDGs), which is "Build resilient infrastructure, promote inclusive and sustainable industrialization, and promote innovation and resilience."

[0091] The present invention has been described above using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by functionally or physically distributing or integrating any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combination also have the effects of the original embodiments. [Explanation of symbols]

[0092] 1. Information processing equipment 2 Viewing devices 3 Analysis terminal 11 Communications Department 12 Storage section 13 Control Unit 130 Contents Provider 131 Acquisition Department 132 Specific part 133 Decision Section

Claims

1. An information processing device for recommending lessons suitable for a target person, an acquisition unit that acquires performance data indicating the performance of the target person in browsing each of a plurality of browsing contents and the financial allowance of a related person associated with the target person; a storage unit that stores a recommendation database in which the type of the content to be viewed, a recommended lesson candidate, and the financial availability of the related person are associated with each other; an identification unit that identifies one or more of the viewed contents, based on the performance data, that satisfy a predetermined condition that the degree of interest of the subject is equal to or greater than a threshold; a determination unit that selects tentative candidates for lessons to be recommended to the subject based on the type of the one or more viewing contents, and determines, from the selected tentative candidates, a lesson candidate that is associated with the financial allowance of the related person acquired by the acquisition unit in the recommendation database and that is determined to be easy to attend based on the distance or travel time from the subject's home to the place where the lesson is to be taken, as the lesson to be recommended to the subject; An information processing device having the above.

2. the performance data includes a length of time that the subject viewed each of the plurality of viewing contents; the identifying unit identifies the one or more pieces of browsing content that satisfy the predetermined condition that the length indicated by the performance data is relatively long; The information processing device according to claim 1 .

3. the performance data includes the number of times the subject has viewed each of the plurality of viewing contents; the identification unit identifies the one or more pieces of browsing content that satisfy the predetermined condition that the number of times indicated by the performance data is relatively large. The information processing device according to claim 1 .

4. the performance data includes a concentration level of the subject measured while the subject is viewing each of the plurality of viewing contents; the identification unit identifies the one or more pieces of browsing content that satisfy the predetermined condition that the degree of concentration is relatively high. The information processing device according to claim 1 .

5. the performance data includes a satisfaction level of the subject measured while the subject is viewing each of the plurality of viewing contents; the specifying unit specifies the one or more pieces of browsing content that satisfy the predetermined condition that the satisfaction level is relatively high. The information processing device according to claim 1 .

6. the performance data includes at least one of a length of time that the subject viewed each of the plurality of viewing contents or a number of times the subject viewed each of the plurality of viewing contents, and at least one of a concentration level or a satisfaction level of the subject measured while the subject viewed each of the plurality of viewing contents; the identification unit identifies the one or more viewed contents that satisfy the predetermined condition that the degree of interest determined by subtracting a degree of dissatisfaction determined based on at least one of the concentration degree or the satisfaction degree from a degree of viewing determined based on at least one of the length or the number of times is relatively high. The information processing device according to claim 1 .

7. An information processing method for recommending lessons suitable for a target person, executed by a computer, comprising: acquiring performance data indicating the performance of the target person in browsing each of a plurality of browsing contents and the financial allowance of related persons associated with the target person; Identifying one or more of the viewed contents, in which the performance data satisfies a predetermined condition that the interest level of the subject is equal to or greater than a threshold; selecting tentative candidates for lessons to be recommended to the subject based on the type of the one or more viewed contents, and determining, from the tentative candidates selected, the lesson candidate that is associated with the acquired financial allowance of the related person in a recommendation database in which the type of viewed content, the recommended lesson candidate, and the related person's financial allowance are associated, and that is determined to be easy to attend based on the distance or travel time from the subject's home to the place where the lesson will be held, as the lesson to be recommended to the subject; An information processing method comprising:

8. A program for recommending suitable lessons to target individuals, On the computer, acquiring performance data indicating the performance of the target person in browsing each of a plurality of browsing contents and the financial allowance of related persons associated with the target person; Identifying one or more of the viewed contents, in which the performance data satisfies a predetermined condition that the interest level of the subject is equal to or greater than a threshold; selecting tentative candidates for lessons to be recommended to the subject based on the type of the one or more viewed contents, and determining, from the tentative candidates selected, the lesson candidate that is associated with the acquired financial allowance of the related person in a recommendation database in which the type of viewed content, the recommended lesson candidate, and the related person's financial allowance are associated, and that is determined to be easy to attend based on the distance or travel time from the subject's home to the place where the lesson will be held, as the lesson to be recommended to the subject; A program to execute.

Images