INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM

Description

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to an information processing device, an information processing method, and a non-transitory computer-readable recording medium.

Priority is claimed on Japanese Patent Application No. 2025-41146, filed Mar. 14, 2025, the content of which is incorporated herein by reference.

Description of Related Art

Various systems are available to help improve golf skills. For example, Japanese Unexamined Patent Application Publication No. 2023-016253 addresses the issue that “a user who already has advanced skills may be matched with a coach whose technical level is insufficient, or a user who wants to learn a specific skill such as putting or bunker shots may be matched with a coach who is not good at that specific skill”, and discloses a system that matches a user with a coach or school that can provide the technical instruction they desire by specifying desired conditions.

However, especially for beginners, it is difficult to provide the system with the information (desired conditions) necessary to recommend a coach that is suitable for them, as they may not know their own skill level or the type of coach that is suitable for them.

One of the objectives is to propose a golf lesson professional that is suitable for any user.

SUMMARY OF THE INVENTION

According to one aspect of the present invention, an information processing device comprises an access means capable of accessing a lesson professional information management table that stores, in association with each other, a lesson professional ID that is identification information of a lesson professional, lesson professional information including the name of the lesson professional, and evaluation information for each predetermined item of attribute information, and in which, among the predetermined items of attribute information, evaluation information provided by a user who has taken a lesson from the lesson professional is registered in an item of attribute information that is the same as an attribute information of the user; an acquisition means for acquiring attribute information of a target user; a swing image acquisition means for acquiring an image of the target user's swing; a swing type classification means for analyzing the image and classifying the swing into a predetermined swing type; a trajectory image acquisition means for acquiring an image including the trajectory of a ball hit by the target user; a trajectory classification means for analyzing the image and classifying the trajectory of the ball into a predetermined ball flight tendency; an extraction means for extracting, based on the evaluation information corresponding to attribute information that is the same as the attribute information acquired by the acquisition means and stored in the lesson professional information management table, a lesson professional ID associated with a lesson professional with a high evaluation; and an output means for outputting the lesson professional information associated with the extracted lesson professional ID, wherein the acquisition means acquires the swing type of the target user as a first item of the attribute information and the ball flight tendency of the target user as a second item of the attribute information, and the extraction means extracts the lesson professional ID based on the evaluation information for the first item of the attribute information and the evaluation information for the second item of the attribute information stored in the lesson professional information management table.

According to one aspect of the present invention, an information processing method includes the steps of: a computer acquiring an image of a swing of a target user; analyzing the image to classify the swing into a predetermined swing type; acquiring an image including a trajectory of a ball hit by the target user; analyzing the image to classify the trajectory of the ball into a predetermined ball flight tendency; acquiring attribute information of the target user; accessing a lesson professional information management table that stores, in association with each other, a lesson professional ID that is identification information of a lesson professional, lesson professional information including the name of the lesson professional, and evaluation information for each predetermined item of attribute information, and in which, among the predetermined items of attribute information, evaluation information provided by a user who has taken a lesson from the lesson professional is registered in an item of attribute information that is the same as an attribute information of the user; extracting, based on the evaluation information corresponding to attribute information that is the same as the acquired attribute information and stored in the lesson professional information management table, a lesson professional ID associated with a lesson professional with a high evaluation; and outputting the lesson professional information associated with the extracted lesson professional ID, wherein in the acquisition step, the swing type of the target user is acquired as a first item of the attribute information and the ball flight tendency of the target user is acquired as a second item of the attribute information, and in the extraction step, the lesson professional ID is extracted based on the evaluation information for the first item of the attribute information and the evaluation information for the second item of the attribute information stored in the lesson professional information management table.

According to one aspect of the present invention, a non-transitory computer-readable recording medium stores a program that causes a computer to execute processing comprising: acquiring an image of a swing of a target user; analyzing the image to classify the swing into a predetermined swing type; acquiring an image including a trajectory of a ball hit by the target user; analyzing the image to classify the trajectory of the ball into a predetermined ball flight tendency; acquiring attribute information of the target user; and accessing a lesson professional information management table that stores, in association with each other, a lesson professional ID that is identification information of a lesson professional, lesson professional information including the name of the lesson professional, and evaluation information for each predetermined item of attribute information, and in which, among the predetermined items of attribute information, evaluation information provided by a user who has taken a lesson from the lesson professional is registered in an item of attribute information that is the same as an attribute information of the user; extracting, based on the evaluation information corresponding to attribute information that is the same as the acquired attribute information and stored in the lesson professional information management table, a lesson professional ID associated with a lesson professional with a high evaluation; and outputting the lesson professional information associated with the extracted lesson professional ID, wherein in the acquisition step, the swing type of the target user is acquired as a first item of the attribute information and the ball flight tendency of the target user is acquired as a second item of the attribute information, and in the extraction step, the lesson professional ID is extracted based on the evaluation information for the first item of the attribute information and the evaluation information for the second item of the attribute information stored in the lesson professional information management table.

According to the present invention, it is possible to suggest a golf lesson professional that is suitable for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a golf lesson system according to an embodiment.

FIG. 2 is a diagram illustrating an example of a screen for registering user information according to the embodiment.

FIG. 3 is a diagram illustrating an example of a screen displaying a swing analysis result according to the embodiment.

FIG. 4 is a diagram illustrating an example of a screen for registering an evaluation of a lesson professional according to the embodiment.

FIG. 5 is a diagram illustrating an example of a user information management table according to the embodiment.

FIG. 6 is a diagram illustrating an example of a lesson professional evaluation value registration table according to the embodiment.

FIG. 7 is a diagram illustrating an example of a lesson professional information management table according to the embodiment.

FIG. 8 is a flowchart illustrating an example of a golf lesson execution process according to the embodiment.

FIG. 9 is a flowchart illustrating an example of a lesson professional extraction process according to the embodiment.

FIG. 10 is a diagram illustrating an example of a hardware configuration of a golf lesson system according to the embodiment.

DETAILED DESCRIPTION OF THE INVENTION

Embodiment

System Configuration

A golf lesson system 1 includes a user terminal 10, a server 20, and a lesson professional terminal 30.

The user terminal 10 is, for example, a mobile terminal such as a smartphone or a personal computer (PC) used by a user. A user is a player who takes golf lessons. The user terminal 10 includes an input unit 11, a display unit 12, a control unit 13, a storage unit 14, a camera 15, and a communication unit 16.

The input unit 11 is an input device such as a touch panel, a button, a keyboard, or a mouse.

The display unit 12 is a display device configured with an LCD, an organic EL, or the like.

The control unit 13 functions as various processing units by running various programs such as an OS (operating system). For example, the control unit 13 reads out a swing analysis application 141 from the storage unit 14 and executes it. The swing analysis application 141 uses the camera 15 to take a video showing the user's swing and the ball's trajectory, sends the captured video (or multiple still images) to the server 20, receives swing analysis results and ball flight tendency analysis results from the server 20, displays the results on the display unit 12, and also has a function of recommending a lesson professional to the user.

The storage unit 14 stores various types of data. The storage unit 14 stores an OS, various applications, the swing analysis application 141, and the like.

The camera 15 captures the user's swing and the trajectory of the ball. The camera 15 does not necessarily have to be provided on the user terminal 10; it may be connected to the user terminal 10, or it may exist separately from the user terminal 10 and be configured to transmit only the captured images to the user terminal 10 or the server 20.

The communication unit 16 performs data communication with the server 20 and the lesson professional terminal 30.

The server 20 is a PC or a server terminal. The server 20 includes a user management unit 21, a lesson professional management unit 22, a storage unit 23, and a communication unit 24.

The user management unit 21 manages user information. The user management unit 21 includes a user information acquisition unit 211 and an analysis unit 212.

The user information acquisition unit 211 acquires the user's name, physique, gender, golf experience, score, and images including the swing and ball trajectory (images taken by the camera 15).

The analysis unit 212 analyzes the user's swing and the trajectory of the ball from the images and classifies the user's swing and the ball's trajectory. The classification results of the swing and the trajectory of the ball are called the swing type and ball flight tendency, respectively. In the present embodiment, criteria and perspectives (referred to as attribute items) for classifying and analyzing the user's characteristics and golf player traits are used. The system focuses on the user's physique, gender, golf experience, score, swing type, and ball flight tendency. The specific user attributes for each of these attribute items, that is, information that represents the nature and characteristics of the user for each attribute item, are referred to as attribute information. For example, the attribute information for the attribute item “gender” is male and female, and the attribute information for the attribute item “ball flight tendency” is fade, draw, etc.

The lesson professional management unit 22 manages information on golf lesson professionals. The lesson professional management unit 22 includes a lesson professional information acquisition unit 221, an evaluation value acquisition unit 222, an aggregation unit 223, and a recommendation unit 224.

The lesson professional information acquisition unit 221 acquires information such as the lesson professional's name, age, teaching experience, etc.

The evaluation value acquisition unit 222 acquires an evaluation value of the lesson professional from users who has taken lessons.

The aggregation unit 223 aggregates the evaluation values from users for each lesson professional.

The recommendation unit 224 recommends a lesson professional to a user according to attribute information such as the user's swing type, ball flight tendency, gender, physique, golf history, etc.

The storage unit 23 stores various data. For example, the storage unit 23 stores a user information management table (table 301 in FIG. 5) in which user attribute information is registered, a lesson professional information management table (table 303 in FIG. 7) in which information about lesson professionals is registered, and an evaluation value registration table (table 302 in FIG. 6) in which evaluation values of lesson professionals by users who have taken lessons are registered.

The communication unit 24 communicates data with the user terminal 10 and the lesson professional terminal 30.

The lesson professional terminal 30 is a smartphone, PC, or the like used by the lesson professional. The lesson professional communicates with the server 20 using the lesson professional terminal 30 and registers information such as name, age, teaching experience, etc. with the server 20. The lesson professional also communicates with the user terminal 10 using the lesson professional terminal 30 to provide golf instruction to the user. Golf lessons can be conducted not only online but also offline in a face-to-face manner.

FIG. 2 shows a user information registration screen 100 on which a user registers his or her own attribute information. The user information registration screen 100 is an image that is generated by the control unit 13 executing the swing analysis application 141 and output to the display unit 12. The user information registration screen 100 includes a name input field 101, a gender input field 102, a physique input field 103, a golf experience input field 104, a score input field 105, and a registration button 106. The user enters their name in the name input field 101, their gender in the gender input field 102, a value classifying their physique in the physique input field 103 (for example, if their height is 180 cm or more, enter “5”, if their height is 170-180 cm, enter “4”, etc.), their years of golf experience and a classification of their golf experience such as beginner, intermediate or advanced in the golf experience input field 104, and their best score or average score to date, classified into, for example, ranks A to E, in the score input field 105, and then presses the registration button 106. Then, the control unit 13 transmits the entered name and attribute information to the server 20 using the communication unit 16. In the server 20, the user information acquisition unit 211 acquires the transmitted name and attribute information, issues a user ID for each user using a predetermined algorithm, associates the user ID with the acquired attribute information, etc., and registers them in the user information management table 301 (FIG. 5) described below. Note that these attribute information are merely examples and are not limited to these. For example, in addition to the items exemplified in FIG. 2, preferences regarding lesson professionals (such as a desire to be recommended a particular type of lesson professional or to exclude a particular type of lesson professional) may be set. Also, for example, some information such as physique and score may be omitted.

FIG. 3 shows an example of an analysis result display screen 110 that displays the analysis results of a swing. The analysis result display screen 110 is an image that is generated by the control unit 13 executing the swing analysis application 141 and output to the display unit 12. The analysis result display screen 110 displays an image 111 of the user's swing captured by the user using the camera 15 of the user terminal 10, an overall evaluation score 112 (93 points in the example of FIG. 3) by the server 20 (analysis unit 212), and an analysis result 113. The analysis result 113 includes instructions indicating issues of the user for each of the address (A), take-back (TB), halfway back (HB), top (T), halfway down (HD), pre-impact (P), impact (I), halfway follow-through (HF), and finish (F). In the example of FIG. 3, an issue indicating that the head position is raised is presented for the address. Based on the analysis results, the analysis unit 212 classifies the user's swing into one of a predetermined number of swing types, such as an early extension/outside-in type or a squatting/inside-out type. In addition, the analysis unit 212 analyzes the trajectory of the ball hit by the user to identify the user's typical ball flight tendency, and registers the swing type and ball flight tendency in the user information management table 301 (FIG. 5) in association with the user ID.

FIG. 4 shows an example of an evaluation registration screen 120 for registering an evaluation of a lesson professional. The evaluation registration screen 120 is an image that is generated by the control unit 13 executing the swing analysis application 141 and output to the display unit 12. The evaluation registration screen 120 is an image for the user to register an evaluation of the instruction after receiving instruction from a lesson professional. The evaluation registration screen 120 includes an image display field 121 for displaying an image of the lesson professional, a name display field 122 for displaying the name of the lesson professional, and an evaluation registration field 123. The evaluation registration field 123 displays options such as “very good”, “good”, “average”, “not so good”, and “bad”, and the user selects one of the evaluations. When the user selects an evaluation, the control unit 13 transmits the user's ID, the lesson professional's ID, and an evaluation value corresponding to the evaluation selected by the user (e.g., 5: “very good”, 4: “good”, 3: “average”, 2: “not very good”, 1: “bad”) to the server 20 using the communication unit 16. In the server 20, the evaluation value acquisition unit 222 acquires this information and registers it in an evaluation value registration table 302 (FIG. 6). In the example of FIG. 4, an overall evaluation of the lesson professional is registered, but it may also be possible to register evaluations by category (such as the content of instruction, teaching skill, attitude or demeanor, etc.).

FIG. 5 shows an example of the user information management table 301. The user information management table 301 includes items such as user ID, swing type, ball flight tendency, golf experience, score, physique, gender, and update date and time. The attribute information (name, gender, physique, golf experience, score) entered by the user on the user information registration screen 100 illustrated in FIG. 2, and the swing type and ball flight tendency analyzed by the analysis unit 212 of the server 20 are registered in the user information management table 301 by the user management unit 21.

FIG. 6 shows an example of the evaluation value registration table 302 for registering evaluation values of lesson professionals. The evaluation value registration table 302 has items such as the user ID of the user who received instruction from the lesson professional, the lesson professional ID of the lesson professional to be evaluated, the evaluation value, and the date of the lesson. The evaluation value acquisition unit 222 registers the evaluation value (e.g., 5: “very good”, 4: “good”, 3: “average”, 2: “not very good”, 1: “bad”) corresponding to the evaluation selected by the user on the evaluation registration screen 120 illustrated in FIG. 4 in the evaluation value registration table 302 in association with the user ID and lesson professional ID.

FIG. 7 shows an example of the lesson professional information management table 303. The lesson professional information management table 303 has items for registering the lesson professional ID, name, gender, overall evaluation score and number of reviews (number of users who made an evaluation), evaluation information and number of reviews for each swing type such as “early extension/outside-in type” and “squatting/inside-out type”, evaluation information and number of reviews for each ball flight tendency such as “fade” and “draw”, evaluation information and number of reviews for each golf experience such as “beginner” and “intermediate”, evaluation information and number of reviews for each score, evaluation information and number of reviews for each physique, evaluation information and number of reviews for each gender, update date and time, etc. Each piece of rating information stores an average value obtained by dividing the total rating values from multiple users by the number of users who rated the item (i.e., the number of reviews). In addition, for each item for which evaluation information is registered, the number of reviews is stored along with the average evaluation value. The average value of the evaluation values is called evaluation information. The above-mentioned overall evaluation score is also a type of evaluation information.

The aggregation unit 223 reads the user information management table 301 and the evaluation value registration table 302, and aggregates the evaluation values for each lesson professional and each attribute information item to calculate the average value, and also aggregates the number of reviews. For example, if there are three users classified as the “early extension/outside-in type”, and all three users are taking lessons from a lesson professional with lesson professional ID=0001, and one of the three users rates the lesson professional as “good” (rating value=4), one as “very good” (rating value=5), and the remaining one as “average” (rating value=3), the aggregation unit 223 will calculate the rating information for this lesson professional from users with an “early extension/outside-in type” swing type as “4” by calculating (4×1+5×1+3×1) ÷3=4, and calculate the number of reviews for the “early extension/outside-in type” as 3. The aggregation unit 223 registers the calculated value in the “early extension/outside-in type (number of reviews)” field of the data for lesson professional ID=0001 in the lesson professional information management table 303. Furthermore, if all of the users who rated the lesson professional as “good” or “average” are female, and the user who rated the lesson professional as “very good” is male, the aggregation unit 223 calculates, by (4×1+3×1)÷2, the evaluation information “3.5” from female users for this lesson professional, and calculates the number of female reviews as 2. Similarly, the aggregation unit 223 calculates, by (5×1)÷1, the evaluation information “5” from male users for this lesson professional, and calculates the number of male reviews as 1. Furthermore, in addition to the three users mentioned above, if there are two more users classified as having a “squatting/inside-out type” swing, and both of these users have taken lessons from lesson professional ID=0001, and both have rated this lesson professional as “average”, and there are no other users who have taken lessons from lesson professional ID=0001, the aggregation unit 223 will calculate, by (4×1+5×1+3×3)÷5=3.6, the overall evaluation score of this lesson professional as “3.6”, and calculate the number of reviews for the overall evaluation score as 5. In addition, the aggregation unit 223 calculates, by (3×2)÷2=3, the evaluation information “3” for the “squatting/inside-out type” for this lesson professional, and calculates the number of reviews for “squatting/inside-out type” as 2. The same applies to the evaluation information and the number of reviews regarding the other attribute information such as the ball flight tendency, golf experience, physique, and score. The aggregation unit 223 registers the calculated evaluation information and number of reviews for each attribute information item in the corresponding items of the data for lesson professional ID=0001 in the lesson professional information management table 303 (the evaluation information and number of reviews described above are unrelated to the evaluation information and number of reviews shown in FIG. 7). In this way, the lesson professional information management table 303 stores evaluation information in which evaluation values from a plurality of users are organized based on the attribute information of the users who made the evaluations. In other words, the lesson professional information management table 303 accumulates evaluation information indicating how lesson professionals are evaluated by users who have certain attribute information.

Operation

Next, the operation of the golf lesson system 1 will be described with reference to FIGS. 8 and 9. It is assumed that basic information such as lesson professional ID, name, and gender for each lesson professional is registered in the lesson professional information management table 303 in advance.

FIG. 8 is a flowchart illustrating an example of a golf lesson execution process according to the embodiment.

First, the user operates the user terminal 10 to display the user information registration screen 100 shown in FIG. 2 on the display unit 12. The user inputs his/her name, gender, physique, golf experience, score, etc. (Step S1), and presses the registration button 106. The control unit 13 transmits the input attribute information to the server 20 using the communication unit 16 (Step S2). In the server 20, the user information acquisition unit 211 acquires attribute information through the communication unit 24, generates a user ID, and registers the acquired user information and the generated user ID in the user information management table 301 stored in the storage unit 23 in association with each other (Step S3). The user management unit 21 uses the communication unit 24 to transmit the generated user ID to the user terminal 10 (Step S4). In the user terminal 10, the control unit 13 acquires the user ID through the communication unit 16 and records the acquired user ID in the storage unit 14.

After inputting the user information, the user operates the user terminal 10 to activate the camera 15 and captures images of their golf swing and the trajectory of the ball they hit (Step S5). The control unit 13 transmits the images captured by the camera 15 and the user ID to the server 20 using the communication unit 16 (Step S6). In the server 20, the user information acquisition unit 211 acquires images and the like through the communication unit 24, records them in the storage unit 23, and also outputs the acquired images and the like to the analysis unit 212. Next, the analysis unit 212 analyzes the swing (Step S7). The analysis unit 212 analyzes the user's swing and the ball's trajectory from the transmitted images. Any known method can be used to analyze the swing and the trajectory of the ball. The analysis unit 212 also classifies the user's swing into one of the swing types, such as “early extension/outside-in type” or “squatting/inside-out type”. Furthermore, the analysis unit 212 classifies the user's ball flight tendency into one of a plurality of ball flight types such as “fade” or “draw”. The analysis unit 212 registers the swing type and the ball flight tendency in the user information management table 301 in association with the user ID. The analysis unit 212 also transmits the analysis results of the swing type and the ball flight tendency to the user terminal 10 using the communication unit 24 (Step S8). In the user terminal 10, upon acquiring the analysis results, the control unit 13 generates an analysis result display screen 110 as shown in FIG. 3 based on the acquired analysis results, and displays the screen on the display unit 12. In addition, in the server 20, the recommendation unit 224 extracts lesson professional candidates (Step S9). The process of extracting lesson professional candidates will be described with reference to FIG. 9.

FIG. 9 is a flowchart illustrating an example of a lesson professional extraction process according to the embodiment.

The recommendation unit 224 acquires attribute information associated with the user ID of the target user (Step S101). The recommendation unit 224 reads and acquires attribute information such as name, swing type, ball flight tendency, golf experience, score, physique, and gender from the user information management table 301.

Next, the recommendation unit 224 refers to the lesson professional information management table 303 and acquires the number of reviews of evaluation information related to the attribute information of the target user (Step S102). For example, assume that the user's gender is “male”, his physique is “5”, his score is “D”, his golf experience is “beginner”, his ball flight tendency is “draw”, and his swing type is “squatting/inside-out type”. In this case, the recommendation unit 224 will refer to the lesson professional information management table 303 and, for each lesson professional, obtain the number of reviews from each of the items in which evaluation information and review counts for these six attribute information are registered. The recommendation unit 224 determines, for each lesson professional, whether the number of all acquired reviews is equal to or greater than a predetermined threshold (Step S103). Alternatively, the recommendation unit 224 may determine whether the number of reviews for a predetermined number or more of the six items of attribute information (for example, any four items of attribute information) is a predetermined threshold or more.

If there is no lesson professional whose total number of reviews is equal to or greater than the predetermined threshold (Step S103: No), the recommendation unit 224 extracts a lesson professional with a high overall evaluation score (Step S104). The recommendation unit 224 refers to the lesson professional information management table 303 and extracts a lesson professional with a high evaluation information value in the item “overall evaluation score (number of reviews)”. If there are no lesson professionals with a review count above a predetermined threshold, the overall number of reviews will be low, making it impossible to determine which lesson professional is highly or poorly evaluated by users with certain attribute information. In this case, lesson professionals are extracted based on the overall evaluation score without taking into account the user's attribute information. The recommendation unit 224 may extract the lesson professional with the highest evaluation information value, or may extract a predetermined number of lesson professionals in descending order of the evaluation information values of the overall evaluation score.

If there are lesson professionals whose total number of reviews is equal to or greater than the predetermined threshold (Step S103: Yes), the recommendation unit 224 extracts a lesson professional whose evaluation information value is high (the average value of multiple evaluation values from multiple users who have the same attribute information as the target user) based on the same attribute information as the user (Step S105). Based on the attribute information acquired in Step S101, the recommendation unit 224 refers to the lesson professional information management table 303 and extracts a lesson professional having high evaluation information corresponding to the same attribute information as the target user. For example, (1) first, the recommendation unit 224 extracts a lesson professional from the lesson professional information management table 303 whose evaluation information for all six items of attribute information is equal to or greater than a predetermined value (for example, “5” or “4”). If there are multiple lesson professionals that meet this condition, the lesson professional with the largest total or average of the evaluation information of the six items of attribute information may be extracted, or a predetermined number of lesson professionals may be extracted in descending order of the total or average of the evaluation information for the six items of attribute information. If no such lesson professional exists, (2) the recommendation unit 224 extracts, from the lesson professional information management table 303, lesson professionals whose evaluation information for, for example, five of the six items of attribute information is equal to or greater than a predetermined value (for example, “5” or “4”). In this case, too, the lesson professional with the largest total or average of the evaluation information may be extracted, or a predetermined number of lesson professionals may be extracted in descending order of the total or average of the evaluation information. Furthermore, if such a lesson professional still does not exist, (3) the recommendation unit 224 subsequently extracts, in a similar manner, a lesson professional for whom the evaluation information for four of the six attribute information items is equal to or greater than the predetermined value, then three, then two, and so on, thereby extracting a lesson professional having a high total or average of evaluation information for attribute information identical to the user's attribute information.

Alternatively, the recommendation unit 224 may extract a lesson professional from the lesson professional information management table 303 whose total of the six pieces of evaluation information related to the same attribute information as the user is equal to or greater than a predetermined value (e.g., “24”), or may extract a predetermined number of lesson professionals in descending order of the total or average of the evaluation information of the six items of attribute information.

It should be noted that when extracting a teaching professional, it is not necessary to treat all items of attribute information uniformly; it is possible to use only specific items of attribute information (for example, only swing type, or only swing type and ball flight tendency, etc.), or to arbitrarily weight each item of attribute information. For example, if physique and gender are not given much importance, these evaluation values can be multiplied by 0.5, and if the user's habits and tendencies are given importance, evaluation information for items such as swing type and ball flight tendency can be multiplied by 2.0, so that lesson professionals with a high weighted average of the evaluation information after weighting can be preferentially extracted. Furthermore, which attribute information is to be considered important when extracting lesson professionals may be set in advance in the server 20 or may be set by the user. Here, a high evaluation information or a high total, average, or weighted average of the evaluation information is an example that the evaluation information is favorable. For example, if the evaluation information is set as 1: “Very good”, 2: “Good”, 3: “Average”, 4: “Not so good”, and 5: “Bad”, the recommendation unit 224 extracts a lesson professional for whom the total or the like of the evaluation information is the lowest, or extracts a predetermined number of lesson professionals in ascending order of the total or the like of the evaluation information. In this case, a low total of the evaluation information means that the evaluation information is favorable.

Returning to FIG. 8, next, the recommendation unit 224 uses the communication unit 24 to transmit the lesson professional IDs of the lesson professionals extracted in Step S104 or Step S105 as lesson professional candidates to the user terminal 10 (Step S10 in FIG. 8). For example, the recommendation unit 224 transmits information such as the lesson professional ID, an image of the lesson professional, a profile, evaluation information for each item of attribute information, and overall evaluation score.

In the user terminal 10, when the control unit 13 acquires the candidates for lesson professionals, an image showing the candidates for lesson professionals is generated and displayed on the display unit 12 (Step S11). The user operates the user terminal 10 to select a lesson professional from among the candidates (Step S12). The user can select a lesson professional based on the evaluation information for each item of attribute information collected by the server 20, the overall evaluation score, and the like. The control unit 13 uses the communication unit 16 to transmit the lesson professional ID of the lesson professional selected by the user to the server 20 (Step S13). The lesson professional management unit 22 acquires the lesson professional ID through the communication unit 24 and requests the lesson professional identified by the ID to provide a lesson to the user (Step S14). For example, the server 20 transmits request information including the user ID and user attribute information to the lesson professional terminal 30 used by the lesson professional selected by the user. The lesson professional terminal 30 communicates with the user terminal 10 to provide the lesson (Step S15).

When the lesson is finished, the user operates the user terminal 10 to call up the evaluation registration screen 120 shown in FIG. 4 and inputs an evaluation of the lesson professional (Step S16). The control unit 13 transmits the input evaluation (e.g., evaluation value such as “5” or “4”), the lesson professional ID, and the user ID to the server 20 using the communication unit 16 (Step S17). In the server 20, the evaluation value acquisition unit 222 acquires the evaluation value through the communication unit 24 and registers the acquired evaluation value in the evaluation value registration table 302 stored in the storage unit 23 in association with the lesson professional ID and user ID (Step S18). Next, the aggregation unit 223 aggregates the evaluation values of the lesson professional (Step S19). For example, the aggregation unit 223 refers to the user information management table 301 to acquire attribute information associated with the user ID. Next, the aggregation unit 223 refers to the lesson professional information management table 303 to obtain, among the evaluation information and the number of reviews associated with the lesson professional ID, the evaluation information and the number of reviews corresponding to items that are the same as the attribute information of the target user. Next, for each item, the aggregation unit 223 multiplies the acquired evaluation information by the number of reviews, adds the evaluation value registered in Step S18, and divides the added value by the value obtained by adding 1 to the acquired number of reviews to calculate new evaluation information that reflects the evaluation by the user. Next, the aggregation unit 223 overwrites the evaluation information of the corresponding item of attribute information with the calculated new evaluation information, and adds 1 to the number of reviews for that item to update the lesson professional information management table 303. The aggregation unit 223 performs the same process on the evaluation information and the number of reviews corresponding to each item that is the same as the attribute information of the target user. This will update the evaluation information of the lesson professional who provided the lesson.

Effects

As described above, according to the present embodiment, a golf lesson professional that is suited to the characteristics and tendencies of a user, such as swing habits and ball flight tendency, is recommended, that is, a lesson professional who is highly evaluated by other users having the same attribute information as the user. This allows, for example, a golf lesson professional suited to a user who is unable to determine which lesson professional is suitable for that user to be suggested. Furthermore, the user can ascertain which lesson professional is best suited to him/her, and can improve his/her skills by receiving instruction from the lesson professional that is best suited to him/her. In addition, lesson professionals can attract students for whom the lesson professionals are particularly well suited, and can provide highly effective lessons.

FIG. 10 is a diagram illustrating an example of a hardware configuration of the golf lesson system according to the embodiment. A computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage device 903, an input/output interface 904, and a communication interface 905. The above-mentioned user terminal 10, server 20, and lesson professional terminal 30 are implemented in the computer 900. The above-described processes are stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, loads it into the main storage device 902, and executes the above-mentioned processing in accordance with the program. Furthermore, the CPU 901 allocates a storage area in the main storage device 902 in accordance with the program. Furthermore, the CPU 901 allocates a storage area in the auxiliary storage device 903 for storing data currently being processed in accordance with the program.

It should be noted that in at least one embodiment, the auxiliary storage device 903 is an example of a non-transitory tangible medium. Other examples of non-transitory tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, and the like, which are connected via the input/output interface 904. Furthermore, when this program is distributed to the computer 900 via a communication line, the computer 900 that receives the program may load the program into the main storage device 902 and execute the above processing. The program may also be for realizing some of the above-mentioned functions. Furthermore, the program may be a so-called differential file (differential program) that realizes the above-mentioned functions in combination with other programs already stored in the auxiliary storage device 903.

In addition, components in the above-described embodiments may be replaced with well-known components as appropriate, without departing from the spirit of the present invention. Furthermore, the technical scope of the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present invention.

Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes.

Supplementary Note 1

A recommendation system comprises an access means capable of accessing a lesson professional information management table that stores, in association with each other, a lesson professional ID that is identification information of a lesson professional, lesson professional information including the name of the lesson professional, and evaluation information for each predetermined item of attribute information, and in which, among the predetermined items of attribute information, evaluation information provided by a user who has taken a lesson from the lesson professional is registered in an item of attribute information that is the same as an attribute information of the user; an acquisition means for acquiring attribute information of a target user; an extraction means for extracting, based on the evaluation information corresponding to attribute information that is the same as the attribute information acquired by the acquisition means and stored in the lesson professional information management table, a lesson professional ID associated with a lesson professional with a high evaluation; and an output means for outputting the lesson professional information associated with the extracted lesson professional ID.

Supplementary Note 2

The recommendation system according to Supplementary Note 1, wherein, among the predetermined items of attribute information, information obtained by dividing the sum of evaluation values of the lesson professional provided by users by the number of people who evaluated the lesson professional is registered in an item of attribute information that is the same as the attribute information of the user.

Supplementary Note 3

The recommendation system according to Supplementary Note 1 or Supplementary Note 2, wherein the attribute information includes information indicating a nature or characteristic of the user relating to at least one of swing type, ball flight tendency, golf history, score, gender, and physique.

Supplementary Note 4

The recommendation system according to any one of Supplementary Note 1 to Supplementary Note 3, further comprising a means for acquiring an image of the target user's swing, and a means for analyzing the image and classifying the swing into a predetermined swing type, wherein the extraction means extracts the lesson professional ID based on the evaluation information of the attribute information that is the same as the swing type of the target user and stored in the lesson professional information management table.

Supplementary Note 5

The recommendation system according to any one of Supplementary Note 1 to Supplementary Note 4 further comprising a means for acquiring an image including the trajectory of a ball hit by the target user, and a means for analyzing the image and classifying the trajectory of the ball into a predetermined ball flight tendency, wherein the extraction means extracts the lesson professional ID based on the evaluation information of the attribute information that is the same as the ball flight tendency of the target user and stored in the lesson professional information management table.

Supplementary Note 6

The recommendation system according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the predetermined attribute information includes a plurality of items of attribute information, and when the plurality of pieces of attribute information include N items of attribute information of the target user, the extraction means extracts the lesson professional ID for which a sum, an average, or a weighted average of the evaluation information corresponding to each of the N items of attribute information is most favorable, or extracts a predetermined number of lesson professionals in descending order of favorability.

Supplementary Note 7

The recommendation system according to any one of Supplementary Note 1 to Supplementary Note 6, wherein the predetermined attribute information includes a plurality of pieces of attribute information, and when the plurality of pieces of attribute information include N items of attribute information of the target user, the extraction means obtains the number of people who have evaluated each of the N items of attribute information, and if the number of items of attribute information for which the number of people is equal to or greater than a predetermined value is equal to or greater than a predetermined value, extracts the lesson professional ID for which a sum, an average, or a weighted average of the evaluation information corresponding to each of the N items of attribute information is most favorable, or extracts a predetermined number of lesson professionals in descending order of favorability.

Supplementary Note 8

The recommendation system according to any one of Supplementary Note 1 to Supplementary Note 7, wherein the predetermined attribute information includes a plurality of pieces of attribute information, and when the plurality of pieces of attribute information includes N pieces of attribute information of the target user, the extraction means obtains the number of people who have evaluated each of the N items of attribute information, and if the number of pieces of attribute information for which the number of people is equal to or greater than a predetermined value is less than a predetermined value, extracts the lesson professional ID for which a sum, an average, or a weighted average of the evaluation information corresponding to each of the predetermined items of attribute information is most favorable, or extracts a predetermined number of lesson professionals in descending order of favorability.

Supplementary Note 9

The recommendation system according to Supplementary Note 4, comprising a user terminal equipped with a camera, and a server equipped with the acquisition means, the extraction means, and the output means, wherein the user terminal transmits an image of the swing captured by the camera to the server.

Supplementary Note 10

A recommendation method in which a computer acquires attribute information of a target user, accesses a lesson professional information management table that stores, in association with each other, a lesson professional ID that is identification information of a lesson professional, lesson professional information including the name of the lesson professional, and evaluation information for each predetermined item of attribute information, and in which, among the predetermined items of attribute information, evaluation information provided by a user who has taken a lesson from the lesson professional is registered in an item of attribute information that is the same as an attribute information of the user; extracts, based on the evaluation information corresponding to attribute information that is the same as the acquired attribute information and stored in the lesson professional information management table, a lesson professional ID associated with a lesson professional with a high evaluation; and outputs the lesson professional information associated with the extracted lesson professional ID.

Supplementary Note 11

A program for causing a computer to execute a process of acquiring attribute information of a target user, accessing a lesson professional information management table that stores, in association with each other, a lesson professional ID that is identification information of a lesson professional, lesson professional information including the name of the lesson professional, and evaluation information for each predetermined item of attribute information, and in which, among the predetermined items of attribute information, evaluation information provided by a user who has taken a lesson from the lesson professional is registered in an item of attribute information that is the same as an attribute information of the user; extracting, based on the evaluation information corresponding to attribute information that is the same as the acquired attribute information and stored in the lesson professional information management table, a lesson professional ID associated with a lesson professional with a high evaluation; and outputting the lesson professional information associated with the extracted lesson professional ID.

While preferred embodiments of the invention have been described and illustrated above, it should be understood that these are exemplary of the invention and are not to be considered as limiting. Additions, omissions, substitutions, and other modifications can be made without departing from the scope of the present invention. Accordingly, the invention is not to be considered as being limited by the foregoing description, and is only limited by the scope of the appended claims.