Recommendation device, information providing system, recommendation method and storage medium

Abstract

Provided are a recommendation device, an information providing system, a recommendation method, and a storage medium. A recommendation device according to one embodiment of the present invention comprises: a communication device; and a processor; the processor obtains performance information generated based on a performance of a first user via the communication device; determines at least one of other users whose music utilization trend is similar to that of the first user, other users whose function utilization trend used during performance is similar to that of the first user, and other users whose music mastery characteristics are similar to those of the first user as can be judged based on the distribution of scores calculated each time according to performance as a second user from among other multiple users; determines recommendation information to be provided to the first user based on the determined performance information of the second user; and transmits the determined recommendation information from the communication device. According to the present invention, by providing high-quality information to the target user, it is possible to well support the performance of an instrument by the target user.

Description

Technical Field

[0001] The present invention relates to a recommendation device, an information providing system, a method and a storage medium for providing information about music. Background Art

[0002] In the technical field of supporting self-learning of musical instrument playing, the following prior art is known as a performance training device that gives optimal suggestions based on the user's performance skills and motivation (for example, Patent Document 1). This technology evaluates the dynamic change based on the number of keystrokes and generates suggestions.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2013-148773 Summary of the invention

[0004] However, since the information serving as advice is generated based only on the performance and operation of the target user, high-quality information for the target user is not necessarily provided.

[0005] A recommendation device according to a technical solution of the present invention comprises: a communication device; and at least one processor; the at least one processor obtains performance information generated based on a performance of a first user via the communication device; determines at least one of other users whose music utilization trend is similar to that of the first user, other users whose function utilization trend is similar to that of the first user when performing, and other users whose music mastery characteristics are similar to those of the first user as can be judged from the distribution of scores calculated according to the performance as a second user from other multiple users; determines recommendation information to be provided to the first user based on the performance information of the determined second user; and transmits the determined recommendation information from the communication device. According to the present invention, by providing high-quality information to the target user, it is possible to well support the instrument performance of the target user.

[0006] According to the present invention, by providing high-quality information to the target user, it is possible to favorably support the target user in playing a musical instrument. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a system configuration diagram of an implementation mode of the information providing system.

[0008] Figure 2 This is a diagram showing an example of the hardware configuration of a server or a terminal.

[0009] Figure 3 This is a sequence diagram showing an operation example of the information providing system according to the first embodiment.

[0010] Figure 4This is a flowchart showing an example of terminal processing of the information providing system according to the second embodiment.

[0011] Figure 5 This is a flowchart showing an example of processing for evaluating and scoring a user's performance according to the second embodiment.

[0012] Figure 6 This is a flowchart showing an example of server processing of the information providing system according to the second embodiment.

[0013] Figure 7 This is a diagram showing an example of a log according to the second embodiment.

[0014] Figure 8 It is a diagram showing a structural example of a music database according to the second embodiment.

[0015] Fig. 9 This is a diagram showing an example of the usage trend and the distance between users of the usage trend according to the second embodiment.

[0016] Fig.10 This is a diagram showing an example of score distribution for each user and user type according to the second embodiment.

[0017] Fig.11 This is a diagram showing an example of the score log characteristic according to the second embodiment.

[0018] Fig.12 It is an explanatory diagram of the music grasping characteristics according to the second embodiment.

[0019] Fig.13 This is a diagram showing an example of parameters of music piece mastering characteristics and distances between users of the music piece mastering characteristics according to the second embodiment. DETAILED DESCRIPTION

[0020] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Figure 1 This is a system structure diagram of an implementation method of the information providing system. The musical instrument 103 is connected to a terminal 102 such as a smart device such as a smartphone or a tablet computer via a communication interface such as USB-MIDI (Universal Serial Bus-Musical Instrument Digital Interface). The terminal 103 is connected to a network 105 such as the Internet via its built-in communication device. On the network 105, server devices such as the recommendation server 101 and the music data server 104 are connected via a wide area network or a local area network, especially a router device not shown in the figure.

[0021] The musical instrument 103 is, for example, an electronic keyboard instrument having a USB-MIDI interface or keyboard, but any other electronic musical instrument may be used. If the user plays the musical instrument 103, performance-related information associated with the performance is sent to the terminal 102 via USB-MIDI. The performance-related information is note on / off information indicating which musical sound is pressed or released by the performance, information indicating which function such as a metronome, a beat switch, a damper pedal, a step lesson, an AB repeat, a soft pedal, a sustained music section, etc. is operated, etc.

[0022] The terminal 102 receives the performance-related information from the musical instrument 103, and sends the performance-related information as log information to the recommendation server (recommendation information generation server device) 101. In addition, the terminal 102 receives data of a musical instrument played by a user from the music data server 104 according to the selection of the scoring function by the user, performs a process of scoring the performance of the musical instrument 103 performed by the user with respect to the above-mentioned performance-related information, and performs a process of displaying the scoring result, and sends the scoring information such as the intermediate result and the final result of the scoring as log information to the recommendation server 101. Then, the terminal 102 receives information such as recommendation information (recommendation information) and visualization information as the analysis result of the performance from the recommendation server 101 and displays it.

[0023] The recommendation server 101 receives the score information from the terminal 102 , generates information such as recommendation information and visualization information, and sends the information back to the terminal 102 .

[0024] The music data server 104 transmits music data such as note data and timing data of each musical sound of a piece of music played by a user on the musical instrument 103 to the terminal 102 .

[0025] Figure 2 It means that it can be achieved Figure 1 FIG. 1 is a diagram showing an example of a hardware configuration of a computer of the recommendation server 101, the terminal 102, or the music data server 104. The computer includes a smartphone, a tablet terminal, and the like in addition to a server computer. Figure 2 The computer shown in the figure includes a processor (e.g., CPU (Central Processing Unit)) 201, a memory 202, an input device 203, an output device 204, an auxiliary information storage device 205, a medium drive device 206 into which a removable recording medium 209 is inserted, and a communication device 207. These components are connected to each other by a bus 208. The structure shown in the figure is an example of a computer that can realize the above-mentioned devices 101, 102 or 104, and such a computer is not limited to this structure.

[0026] The memory 202 is a semiconductor memory such as a read-only memory (ROM), a random access memory (RAM), or a flash memory, and stores programs and data used in processing.

[0027] The processor 201 executes a program corresponding to the processing of each flowchart described later, using the memory 202, for example.

[0028] The input device 203 is, for example, a keyboard or a pointing device, and is used to input instructions or information from an operator or user. The output device 204 is, for example, a display device, a printer, a speaker, and is used to output inquiries or processing results to an operator or user.

[0029] The auxiliary information storage device 205 is, for example, an SSD (solid state hard disk drive), a hard disk storage device, a magnetic disk storage device, an optical disk device, a magneto-optical disk device, a magnetic disk device, or a semiconductor storage device. The above-mentioned respective devices 101, 102, or 104 store in advance in the auxiliary information storage device 205 programs and data for executing the processing of each flowchart described later used in each device, and these can be loaded into the memory 202 for use.

[0030] The medium drive device 206 drives the removable recording medium 209 to access the recorded content. The removable recording medium 209 is a memory device, a floppy disk, an optical disk, a magneto-optical disk, etc. The removable recording medium 209 may also be a compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a universal serial bus (USB) memory, an SD memory card, etc. The operator or user can save the above-mentioned program and data in the removable recording medium 209 and load it into the memory 202 for use.

[0031] As described above, the computer-readable recording medium storing the above-mentioned program and data is a physical (non-transitory) recording medium such as the memory 202 , the auxiliary information storage device 205 , or the portable recording medium 209 .

[0032] The communication device 207 is connected to the network 105 such as a local area network (LAN) or a wide area network (WAN), and includes a communication interface for performing data conversion accompanying communication. Each of the above-mentioned devices 101, 102, or 104 can receive the above-mentioned programs or data from an external device connected to the network 105 via the communication device 207, load them into the memory 202, and use them. In addition, when the communication device 207 is built in Figure 1 In the case of the terminal 102, in addition to the above functions, it also has an interface such as USB-MIDI and the like. Figure 1 The interface function of the musical instrument 103 is connected.

[0033] In addition, the above-mentioned 101, 102 or 104 devices do not need to include Figure 2 The present invention can be used to implement the present invention in accordance with the present invention. The present invention can also omit all the components, and some of the components can be omitted according to the purpose or conditions. For example, when there is no need to input instructions or information from an operator or user, the input device 203 can be omitted. When the removable recording medium 209 is not used, the medium drive device 206 can also be omitted.

[0034] use Figure 3 The sequence diagram for the above Figure 1 and Figure 2 A first embodiment of the operation of the information providing system will be described.

[0035] If a user (hereinafter, the user who plays the musical instrument 103 is referred to as "the first user") connects the musical instrument 103 and the terminal 102, for example, by USB-MIDI, and uses the musical instrument 103 to play a certain piece of music (the first piece of music), the processor 201 of the terminal 102 obtains the performance-related information via the communication device 207 of the terminal 102, and sends the performance-related information as log information 301 from the communication device 207 of the terminal 102 to the recommendation server 101 via the network 105 ( Figure 3 order S301).

[0036] Furthermore, if the first user sets the scoring function to be effective and performs a performance, the processor 201 of the terminal 102 receives the data of the music of the instrument being performed from the music data server 104 via the network 105 and the communication device 207 of the terminal 102 through the first user, executes the processing of scoring the performance of the instrument 103 performed by the first user with respect to the above-mentioned performance-related information, and sends the scoring information such as the intermediate result and the final result of the scoring as the log information 302 from the communication device 207 of the terminal 102 to the recommendation server 101 via the network 105 ( Figure 3 order S302).

[0037] In addition, the processor 201 of the terminal 102 displays the above-mentioned scoring result on the output device 204 (for example, a liquid crystal display of a smart device of the terminal 102) ( Figure 3 order S303).

[0038] In the recommendation server 101, the processor 201 of the recommendation server 101 receives the performance-related information and score information transmitted as log information 301, 302 from the terminal 102 via the network 105 via the communication device 207 of the recommendation server 101. The processor 201 sends (from the communication device 207 of the recommendation server 101) the performance-related information (operation, music use, function use, etc.) and score information (score) and information determined based on a plurality of performance-related information corresponding to performances performed by a plurality of other users including a second user other than the first user who plays the musical instrument 103 stored in the memory 202 to the first user. Figure 3 order S304).

[0039] Here, the processor 201 of the recommendation server 101 classifies the first user into one of a plurality of types including at least a relaxed type, a perseverant type, or a frustrated type based on a plurality of performance-related information of the first user, and decides on information to be provided to the first user based on the performance-related information of a second user of the same type as the classified type.

[0040] Furthermore, when score information is transmitted from the terminal 102 as the log information 302 , the processor 201 of the recommendation server 101 determines the plurality of categories based on at least the score information and the practice period of each user's performance.

[0041] Furthermore, the information provided to the first user may include at least one of information about a second piece of music that the first user has not played but other users have played and information about a function that other users have used when playing the first piece of music.

[0042] As examples of more specific information to be provided to the first user, the following information can be cited.

[0043] · Next challenge song (by level)

[0044] ·Functions to support practice

[0045] ·Introduction to songs that are frequently played by users of the same level

[0046] Scoring / judgment and ranking of music pieces

[0047] Technical suggestions

[0048] Next, the processor 201 of the recommendation server 101 generates information ( Figure 3 order S305).

[0049] Then, the processor 201 of the recommendation server 101 sends back the recommendation information determined in the procedure S303 and the visualization information generated in the procedure S305 from the communication device 207 of the recommendation server 101 to the terminal 102 via the network 105 ( Figure 3 order S306).

[0050] The terminal 102 displays the recommendation and the analysis result visualization on the output device 204 (liquid crystal display, etc.) of the terminal 102, based on the visualization information in the information transmitted from the recommendation server 101 via the network 105 and the communication device 207 of the terminal 102, together with the display of the score result in the sequence S303, by the following display method: Figure 3 The order is S307, S308).

[0051] ·Text display of the above recommended information

[0052] Time series scoring information (also shows scores and suggestions between partitions)

[0053] Growth curve (with comparison with other users)

[0054] Radar chart (showing the characteristics of the first user)

[0055] In the past, musical instruments and smart phones were used to provide functions to users, but through the recommendation server 101, more extensive and optimal recommendation information can be provided. It is boring to play a musical instrument by one person, so it is helpful to bring the motivation to continue it. It makes it possible to realize that it is not just one person practicing, but other people are also doing similar things. It is not just one person, but it is possible to provide information such as reference that other people have done as recommendation information. If other people notice things that they have not noticed (music, function), etc., in the process of learning to play the favorite music, the user forms appropriate suggestions or recommendations based on the data (big data) of the learning process of other users similar to that, and for the user in the learning process, it is possible to obtain suggestions from the success examples of others in reality. Since it is not a suggestion derived from mechanical logic, it is highly persuasive, and it is possible to be aware of others, and it is also possible to expect the effect of continuous improvement in the motivation to continue practicing.

[0056] Next, use the following Figures 4 to 6 Flowchart and Figures 7 to 13 The illustration shows Figure 1 and Figure 2 A second embodiment of a more specific operation example of the information providing system will be described.

[0057] Figure 4 In the second embodiment, Figure 1The flowchart is a flowchart of an example of terminal processing executed by the processor 201 of the terminal 102 of the information providing system. The flowchart shows the processing of the processor 201 in the terminal 102 executing the control program stored in the memory 202.

[0058] Figure 5 Yes means Figure 4 A flowchart of a more detailed action example of step S401 in terminal processing.

[0059] Figure 6 In the second embodiment, Figure 1 The flowchart is a flowchart of an example of server processing executed by the processor 201 of the recommendation server 101 of the information providing system. The flowchart shows processing when the processor 201 executes the control program stored in the memory 202 in the recommendation server 101.

[0060] First, in Figure 4 In the flowchart of the first embodiment described above, Figure 3 The operations of the sequence S301 and S302 are similar. The processor 201 of the terminal 102 obtains the performance-related information from the musical instrument 103. On the other hand, the processor 201 of the terminal 102 receives the data of the music played by the user (the first user) on the musical instrument from the music data server 104 via the network 105 and the communication device 207 of the terminal 102, and performs the processing ( Figure 4 Step S401).

[0061] use Figure 5 The details of the processing are described in the flowchart of FIG. 1. First, the processor 201 of the terminal 102 performs the initialization of the model performance, the area for accompaniment reproduction, various variables, etc. on the memory 202 of the terminal 102 ( Figure 5 Step S501).

[0062] Next, the processor 201 of the terminal 102 starts sending the model performance and automatic performance data for accompaniment obtained from the music data server 104 to the musical instrument 103 via the USB-MIDI from the communication device 207 of the terminal 102, and starts the performance ( Figure 5 Step S502).

[0063] Then, the processor 201 of the terminal 102 repeatedly performs a series of processing of the following steps S503 to S505 during the performance. In this series of processing, the processor 201 of the terminal 102 first performs the processing of the model performance and the accompaniment reproduction ( Figure 5 Step S503).

[0064] Specifically, in the model performance and accompaniment reproduction processing, the processor 201 of the terminal 102 sequentially reads the automatic performance data of the model performance and accompaniment reproduction obtained from the music data server 104 in the memory 202 of the terminal 102, and outputs the note number to the sound source of the musical instrument 103 at the timing of sounding / silencing according to the time. The reproduction processing is executed according to the settings such as the on / off of the beat or metronome, the presence or absence of the guidance of the model performance, etc. through the user operation.

[0065] Next, the processor 201 of the terminal 102 performs evaluation processing of the user's performance based on the performance-related information transmitted from the musical instrument 103 ( Figure 5 Step S504). Specifically, the processor 201 of the terminal 102 increases the number of correct answers, the number of false touches, etc. according to whether there is a model performance near the timing of the user's performance, the comparison of the note number (pitch), the deviation of the timing, etc. The number of correct answers can also be "correct" or "roughly correct" according to the degree of the above-mentioned deviation. In addition, if the music is divided into the first half and the second half according to the elapsed time, and the "correct answer" and "roughly correct answer" are counted, in the determination of the user type (relaxed type, perseverant type, frustrated type, etc.) in the recommendation server 101 described later, it is possible to make a more subtle determination of the user type based on the difference between "the second half is completely unacceptable, but the first half is acceptable" and "not acceptable as a whole". In addition, the processor 201 of the terminal 102 counts the number of notes of the model performance for the standardization in step S506 described later.

[0066] Next, the processor 201 of the terminal 102 determines whether the model performance and the accompaniment reproduction are finished and the performance is finished ( Figure 5 Step S505).

[0067] If the performance has not ended (the determination of step S505 is NO), the processor 201 of the terminal 102 repeatedly executes a series of processes from steps S503 to S505.

[0068] If the performance is finished (the determination in step S505 is YES), the processor 201 of the terminal 102 executes the scoring process ( Figure 5 In this process, the processor 201 of the terminal 102 performs correction for timing deviation and false touch based on the ratio of the number of correct answers evaluated in step S505 to the number of notes of the model performance counted as described above.

[0069] Then, the processor 201 of the terminal 102 ends Figure 5 The flowchart shows Figure 4 Evaluation processing of step S401.

[0070] Back to Figure 4The processor 201 of the terminal 102 compares the score result based on the evaluation process in step S401 with the score of the first embodiment. Figure 3 The same is true for the case of the sequence S303, which is displayed on the output device 204 (for example, a liquid crystal display of a smart device as the terminal 102) ( Figure 4 Step S402).

[0071] Then, the processor 201 of the terminal 102 uploads the score result in step S402 and the operation status of each function in the information received as the performance-related information from the musical instrument 103 from the communication device 207 of the terminal 102 to the recommendation server 101 via the network 105 ( Figure 4 Step S403).

[0072] Then, the processor 201 of the terminal 102 enters a standby state until a response from the recommendation server 101 is received via the communication device 207 of the terminal 102 from the network 105 ( Figure 4 (the determination of steps S404→S405 is repeated as NO).

[0073] Regarding the recommendation server 101's response to the above upload Figure 6 The processor 201 of the recommendation server 101 first adds the score information and function operation status received from the terminal 102 via the network 105 and the communication device 207 of the recommendation server 101 to the score log and operation log stored in the memory 202 of the recommendation server 101 or the auxiliary information storage device 205 ( Figure 6 Step S601).

[0074] Figure 7 It means in Figure 6 FIG. 1 is a diagram showing an example of a score log and an operation log specifically generated in step S601. Figure 7 The score log illustrated in (a) of FIG. 1 records the user ID identifying the user, the music ID identifying the scored music, the scoring date, and the score indicating the scoring result, which are included in the scoring information received from the terminal 102. Figure 7 The operation log illustrated in (b) records the user ID identifying the user, the function ID identifying the function, the date when the function was operated, and the value indicating the operation amount of the function included in the function operation status of the function received from the terminal 102.

[0075] Next, the processor 201 of the recommendation server 101 determines the recommendation type ( Figure 6Specifically, based on the user's past score log, if the current score result is close to the highest score, or if the score result is low and the score result has not improved, the recommendation type is set to "music", and in other cases, the recommendation type is set to "function".

[0076] Next, the processor 201 of the recommendation server 101 determines the recommendation type ( Figure 6 Step S603).

[0077] When it is determined in step S603 that the recommendation type is "music", the processor 201 of the recommendation server 101 analyzes the music usage trend, sets other users who use similar music as the second user, and searches for ( Figure 6 Step S604).

[0078] Figure 8 This is a diagram showing an example of a music database stored in the memory 202 or the auxiliary information storage device 205 of the recommendation server 101 as basic data for analyzing the music usage trend. In the music database, as attributes of the music, a plurality of music data records having items such as a music ID for identifying the music, a difficulty level as an indicator of the difficulty of playing, a music genre, and a music name are stored.

[0079] The processor 201 of the recommendation server 101 Figure 6 In step S604, according to Figure 7 (a) The score log for the most recent period and Figure 8 The music database of the recommendation server 101 is generated in the memory 202 for each user ID for the music recorded in the score log. Figure 8 The average difficulty level (difficulty level information of the music practiced so far) obtained from the music database, the number of times the exercise music is used, the number of times the pop music is used, the number of times the piano music is used (the genre information of the music practiced so far), and the total number of times the singing is used, Fig. 9 Furthermore, the processor 201 of the recommendation server 101 Figure 6 In step S604, Fig. 9As shown in (a-2), a user group consisting of any two users is selected from the multiple users recorded in the score log, and the square of the difference between the two item values ​​of each item is calculated according to the user group, and the calculation result is accumulated for all items according to the user group and the square root of the accumulated result is calculated, and table data for calculating the distance of each user group is generated on the memory 202 of the recommendation server 101. In addition, the processor 201 of the recommendation server 101 Figure 6 In step S604, from Fig. 9 From the table data of (a-2), a user group including the user ID corresponding to the corresponding user notified from the terminal 102 and having the smallest distance is selected, and other users with similar function utilization trends are used as second users, and a user in the user group who is not the corresponding user is found.

[0080] That is, the server device determines another user whose music usage tendency is similar to that of the first user as the second user based on at least one of the difficulty level information and genre information of the music practiced so far.

[0081] In the above Fig. 9 In the music usage trend data exemplified in (a-1) of FIG. 1 , the number of times of each item other than the difficulty level is totaled, but the ratio of each item may be totaled.

[0082] Next, the processor 201 of the recommendation server 101 refers to Figure 7 (a) of the score log, in step S604, among the similar music used by the user, the music not used by the corresponding user is selected as the recommendation information = recommended music ( Figure 6 Step S605).

[0083] When it is determined in step S603 that the recommendation type is "function", the processor 201 of the recommendation server 101 analyzes the function utilization trend, analyzes the user or music mastering characteristics (the period required to master a certain music and the trend of score transition / score distribution) of similar function utilization, and searches for other users with similar music mastering characteristics as second users from past operation logs. Figure 6 Step S606).

[0084] First, the processing of step S606 in the case of analyzing the function utilization trend will be described. Figure 6 In step S606, according to Figure 7 (b) of the operation log of the most recent certain period, and generate a total of the number of times each function recorded in the operation log is used according to the user ID on the memory 202 of the recommendation server 101. Fig. 9Furthermore, the processor 201 of the recommendation server 101 Figure 6 In step S606, Fig. 9 As shown in (b-2), by selecting a user group consisting of two arbitrary users from a plurality of users logged in the operation log, calculating the square of the difference between the two item values ​​of each of the above items according to the user group, and then summing up the calculation results for all items according to the user group and calculating the square root of the summed results, table data for calculating the distance of each user group is generated on the memory 202 of the recommendation server 101. In addition, the processor 201 of the recommendation server 101 Figure 6 In step S606, from Fig. 9 From the table data of (b-2), a user group including the user ID corresponding to the corresponding user notified from the terminal 102 and having the smallest distance is selected, and other users with similar function utilization trends are used as second users, and a user in the user group who is not the corresponding user is found.

[0085] In the above Fig. 9 In the function utilization trend data exemplified in (b-1) of FIG. 1 , the number of times of each item is totaled, but the ratio of each item may be totaled.

[0086] Next, the processing of step S606 in the case of analyzing the music mastering trend is described. As basic data for analyzing the music mastering trend, it is effective to use the score distribution of the user. For this purpose, the processor 201 of the recommendation server 101 Figure 7 The score values ​​of the score logs of (a) can be calculated by performing cluster analysis, for example, and summing them up according to the users. Fig.10 The score distribution diagram shows the score distribution of each user (U001, U002, U003) as shown in (a). This score distribution diagram shows the trend of the time series of scores by user. Fig.10 If we observe the user type shown in (b) Fig.10 (a) and Fig.10 (b), user ID=U002 is a type that improves in playing by continuously improving the score from a low score over a relatively long period of time (e.g., 1 month), and can be called a so-called perseverant type. In addition, user ID=U003 is a type that only practices for a short period of time and gives up at a low score, and can be called a so-called frustrated type. Furthermore, user ID=U001 is a type that is good at playing but obtains a relatively high score in a relatively short period of time, and can be called a so-called relaxed type. In step S606, the processor 201 of the recommendation server 101 Fig.10From the score distribution graph data calculated as shown in (a), for example, users of a cluster whose centroid distances are closest to the cluster of the corresponding user are selected as users with similar music mastering characteristics.

[0087] That is, the server device determines other users having similar music mastering characteristics to the first user as second users based on the user types determined from the score distribution of each user, where the similarity of music mastering characteristics can be determined from the distribution of scores calculated according to performances.

[0088] Next, other specific processing of step S606 in the case of searching for other users with similar music mastering characteristics will be described. Figure 7 In the score log of (a), after being aggregated by user ID, the result is calculated as Fig.12 The initial score, the achieved score, the practice interval, the score change, and the current score change of each index value shown are generated on the memory 202 of the recommendation server 101 during the practice. Fig.11 Table data shown in (a).

[0089] Furthermore, in step S606, the processor 201 of the recommendation server 101 calculates the average value of each of the above-mentioned index values ​​according to the above-mentioned table data and the user ID, and generates a Fig.13 Then, the processor 201 of the recommendation server 101 in step S606, as shown in FIG. Fig.13 As shown in (b), a user group consisting of any two users is selected from multiple users logged in the score log, and the square of the difference between the two index values ​​of the above index values ​​is calculated according to the user group, and then the calculation results are accumulated according to the index points of the user groups and the square root of the accumulated results is calculated, and table data for calculating the distance of each user group is generated on the memory 202 of the recommendation server 101. And, in step S606, the processor 201 of the recommendation server 101 Fig.13 In the table data of (b), a user group including the user ID corresponding to the corresponding user notified from the terminal 102 and having the smallest distance is selected, and a user in the user group who is not the corresponding user is found as a user with similar music mastering characteristics.

[0090] In this way, by comparing the information of the corresponding user who sends the recommendation information with any one of the other multiple users, it is possible to find any one of the users whose characteristics (music usage trend, function usage trend, and music mastery characteristics) are closest to the corresponding user.

[0091] In the above Figure 6After the processing of step S606, the processor 201 of the recommendation server 101 refers to Figure 7 (b) operation log, in step S606, among the similar functions used by the user, the function that the corresponding user has not used is selected as the recommended information = recommended function ( Figure 6 Step S607).

[0092] After step S605 or S607, the processor 201 of the recommendation server 101 sends the recommended music or recommended function selected as the recommendation information back to the terminal 102 from the communication device 207 of the recommendation server 101 via the network 105. Figure 6 Then, the processor 201 of the recommendation server 101 ends the server processing as the return processing to the terminal 102.

[0093] Back to Figure 4 If there is a response from the recommendation server 101, the judgment of step S405 is YES. As a result, the processor 201 of the terminal 102 displays the recommendation information received from the recommendation server 101 via the network 105 and the communication device 207 of the terminal 102 on the liquid crystal display of the output device 204 of the terminal 102, etc., and performs the recommendation display.

[0094] In the second embodiment described above, by Fig.13 (a) and Fig.10 (b) shows that, Figure 10 to Figure 13 The music mastery characteristic parameter table calculated in this way well represents user types such as the relaxed type, the perseverant type, the frustrated type, etc. In the second embodiment, by determining recommendation information corresponding to the corresponding user based on the performance-related information of users with similar user types, more appropriate recommendation information can be provided.

[0095] In the second embodiment described above, Figure 6 In step S606, the processor 201 of the recommendation server 101 may determine which analysis method to use, the function utilization trend or the music grasping characteristic, randomly, or change it each time.

[0096] As described in the second embodiment, by analyzing the respective trends such as music utilization trends, function utilization trends, music mastering characteristics, etc., when recommending music, recommending functions, or suggesting practice methods, it is possible to reliably obtain recommendations that are easy for users to adopt, be aware of other users, and achieve the effect of maintaining motivation for practice.

[0097] In the case where the frustrated type person is not likely to accept recommendations based on the performance-related information of other frustrated type people, the user's switching operation may cause the frustrated type person to accept recommendations based on the performance-related information of other perseverant type people, for example.

[0098] Furthermore, as in Figure 5 As described in step S504, if the music is divided into the first half and the second half according to the elapsed time, and the "correct answer" and the "approximately correct answer" are counted, then in the judgment of the user type (relaxed type, perseverant type, frustrated type, etc.) in the recommendation server 101, it is possible to make a more detailed judgment of the user type based on the difference between "the second half is completely bad, but the first half is okay" and "the whole is bad", and based on these judgments, more appropriate recommendation information can be provided.

[0099] As another variation example other than the first and second embodiments, in order to let the user know what tempo the user has practiced the music at, tempo information may be added during the scoring process of the music, and the tempo information may also be recorded in the score log, so that it is recommended to practice with a slower tempo as an exercise in the case of no improvement. Furthermore, in order to let the user know what tempo the user will hopefully improve in the degree of learning (score) of the music at, an example of a tempo value suitable for practice may be recommended by referring to the logs of users with similar levels to the user.

[0100] Furthermore, as another modification, usage information of metronome, REC, and AB repeat as functions that are assumed to be used during practice can be added, and functions with high usage frequency can be recommended as functions that may be effective for practicing a specific piece of music. For example, if the function that a user used most frequently until he or she achieved a high score in a piece of music was a metronome, a metronome can be recommended as a recommended practice function for the same piece of music.

[0101] That is, the server device determines other users who have a similar function usage trend as the first user when practicing as the second user, based on at least one of the number of metronome usage, the number of beat changes, and the number of pedal usage used during practice so far.

[0102] As still another modification, the genres of music that the user is good at or likes may be narrowed down based on the user's highest score or number of times used, and music with a difficulty level close to the average difficulty level of music that the user has used may be recommended within the genre.

[0103] As still another modification, a favorite genre may be estimated based on the number of times the music is used, and music may be recommended based on the estimated genre.

[0104] The embodiments of the present invention and their advantages are described in detail above, but those skilled in the art can make various changes, additions, and omissions without departing from the scope of the present invention clearly described in the claims. That is, even if all of these processes are not performed by one processor, but by multiple processors, or are performed separately by multiple devices, these embodiments also fall within the scope of the present invention.

[0105] In addition, the present invention is not limited to the above-mentioned embodiments, and can be variously deformed in the implementation stage without departing from the main purpose. In addition, each embodiment can be implemented in appropriate combination as long as possible, in which case the effect of the combination can be obtained. Furthermore, the above-mentioned embodiments include inventions at various stages, and various inventions can be extracted by appropriate combinations of the disclosed multiple constituent elements. For example, even if some constituent elements are deleted from all the constituent elements shown in the embodiments, if the problem described in the problem to be solved by the invention column can be solved and the effect described in the invention effect column can be obtained, the structure with the deleted constituent element can be extracted as an invention.

Claims

1. A recommendation device, characterized in that: The invention comprises: a communication device; and a processor; the processor executing: obtaining performance information generated based on a performance of a first user via the communication device; determining a second user from at least a plurality of other users based on the obtained performance information, the second user being a second user having music mastery characteristics similar to those of the first user as determined based on the distribution of scores calculated for each performance; determining recommendation information to be provided to the first user based on the determined performance information of the second user; and sending the determined recommendation information from the communication device.

2. The recommendation device according to claim 1, characterized in that: The processor further selects another user having a music usage trend similar to that of the first user as the second user, and determines the second user based on at least one of difficulty information of the music practiced so far and genre information of the music practiced so far.

3. The recommendation device according to claim 1, characterized in that: The processor also determines as the second user another user whose function utilization trend during performance is similar to that of the first user, based at least on one of the number of metronome utilizations, the number of beat changes, and the number of pedal utilizations used during practice so far.

4. The recommendation device according to claim 1, characterized in that: The processor determines the second user based on a type of user determined based on a score distribution of each user.

5. The recommendation device according to claim 1, characterized in that: The music mastering characteristics are determined based on the time series distribution of the scores calculated for each performance.

6. The recommendation device according to claim 1, characterized in that: The music mastering characteristics are obtained by clustering the scores calculated for each performance.

7. The recommendation device according to any one of claims 1 to 6, characterized in that: The recommendation information includes at least one of information about music that the first user has not played but the second user has played, and information about a function that the second user has used when playing the music.

8. An information providing system, comprising a recommendation device and a terminal device, characterized in that: The recommendation device comprises: a communication device; and a processor; the processor executes: obtaining performance information generated based on a performance of a first user via the communication device; determining a second user from at least a plurality of other users based on the obtained performance information, the second user being a second user whose music mastering characteristics are similar to those of the first user as determined based on the distribution of scores calculated for each performance; determining recommendation information to be provided to the first user based on the determined performance information of the second user; and sending the determined recommendation information from the communication device; the terminal device executes: sending the performance information to the recommendation device; receiving the recommendation information sent from the communication device of the recommendation device; and displaying the received recommendation information.

9. The information providing system according to claim 8, wherein: The processor of the recommendation device further selects another user whose music usage trend is similar to that of the first user as the second user, and determines the second user based on at least one of difficulty information of the music practiced so far and genre information of the music practiced so far.

10. The information providing system according to claim 8, wherein: The processor of the recommendation device further determines, as the second user, another user whose function utilization trend during performance is similar to that of the first user, and determines the second user based on at least one of the number of metronome utilizations, the number of beat changes, and the number of pedal utilizations used during practice so far.

11. The information providing system according to claim 8, wherein: The processor of the recommendation device determines the second user based on the type of user determined based on the score distribution of each user.

12. The information providing system according to claim 8, wherein: The music mastering characteristics are determined based on the time series distribution of the scores calculated for each performance.

13. The information providing system according to claim 8, wherein: The music mastering characteristics are obtained by clustering the scores calculated for each performance.

14. The information providing system according to any one of claims 8 to 13, wherein: The recommendation information includes at least one of information about music that the first user has not played but the second user has played, and information about a function that the second user has used when playing the music.

15. A recommendation method, characterized in that: The recommendation device is caused to execute: obtaining performance information generated based on a performance of a first user via a communication device; determining a second user from at least a plurality of other users based on the obtained performance information, the second user being a second user having music mastery characteristics similar to those of the first user as determined based on the distribution of scores calculated for each performance; determining recommendation information to be provided to the first user based on the determined performance information of the second user; and sending the determined recommendation information from the communication device.

16. The recommendation method according to claim 15, characterized in that: The recommendation device further selects another user whose music usage trend is similar to that of the first user as the second user, and determines the second user based on at least one of difficulty information of the music practiced so far and genre information of the music practiced so far.

17. The recommendation method according to claim 15, characterized in that: The recommendation device further selects other users whose function utilization trends used during performance are similar to those of the first user as the second user, and determines the second user based on at least one of the number of metronome utilizations, the number of beat changes, and the number of pedal utilizations used during practice so far.

18. The recommendation method according to claim 15, characterized in that: The recommendation device is caused to determine the second user based on the user type determined based on the score distribution of each user.

19. The recommendation method according to claim 15, characterized in that: The music mastering characteristics are determined based on the time series distribution of the scores calculated for each performance.

20. The recommendation method according to claim 15, characterized in that: The music mastering characteristics are obtained by clustering the scores calculated for each performance.

21. The recommendation method according to any one of claims 15 to 20, characterized in that: The recommendation information includes at least one of information about music that the first user has not played but the second user has played, and information about a function that the second user has used when playing the music.

22. A storage medium, characterized in that: A program is stored, and the program causes the recommendation device to execute: obtaining performance information generated based on a performance of a first user via a communication device; determining a second user from at least a plurality of other users based on the obtained performance information, the second user being a second user whose music mastering characteristics are similar to those of the first user as determined based on the distribution of scores calculated according to each performance; determining recommendation information to be provided to the first user based on the determined performance information of the second user; and sending the determined recommendation information from the communication device.

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