Recommendation device, recommendation method, and program product

Abstract

The invention provides a recommendation device, a recommendation method and a program product. The recommendation method determines recommendation information indicating a music or a function recommended with respect to the performance of the first user on the basis of third performance-related information relating to the performance of the musical instrument by the second user. In the second user, by comparing first performance-related information pertaining to the performance of a musical instrument by the first user and second performance-related information pertaining to the performance of a musical instrument by each of a plurality of users other than the first user, the performance trend of a musical instrument or the performance characteristics of a musical instrument determined from the plurality of users, and the first user For a user, the third performance-related information is performance-related information among the second performance-related information. The recommendation information includes at least one of information on a composition that has not been played by the first user but has been played by the second user, and information on a function that has not been used by the first user and is used by the second user when playing.

Description

[0001] This application is a divisional application of the patent application with the Chinese Patent Application No. 202010979400.7 and the invention title of "Recommendation Device, Information Provision System, Recommendation Method, and Storage Medium", which was filed on September 17, 2020. Technical Field

[0002] The present invention relates to a recommendation device, an information provision system, a method, and a storage medium for providing information about music. Background Art

[0003] Conventionally, in the technical field of supporting self-study of musical instrument performance, as a performance training device that gives optimal advice based on the performance technique and motivation of a user, the following prior art is known (for example, Patent Document 1). This technique evaluates the motivation change based on the number of key presses and generates advice.

[0004] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2013-148773 Summary of the Invention

[0005] However, since the information as advice is generated only based on the performance and operations of the target user, the information of high quality for the target user may not necessarily be given.

[0006] A recommendation method according to one aspect of the present invention determines recommendation information indicating a piece of music or a function recommended for the performance of a first user based on third performance-related information related to the performance of a musical instrument by a second user, where the second user is a user determined from a plurality of users other than the first user whose trend or characteristics of the performance of a musical instrument are similar to those of the first user by comparing first performance-related information related to the performance of a musical instrument by the first user and second performance-related information related to the performance of a musical instrument by each of the plurality of users other than the first user, the third performance-related information is performance-related information among the second performance-related information, and the recommendation information includes at least one of information about a piece of music that the first user has not performed but the second user has performed, and information about a function that the first user has not used but the second user has used during performance.

[0007] In a recommended method according to an aspect of the present invention, in order to determine recommended information indicating a piece of music or a function recommended for the performance of an instrument by a first user, by comparing first performance-related information related to the performance of the instrument of the first user and second performance-related information related to the performance of the instrument of each of a plurality of users other than the first user, a second user whose trend or characteristics of the performance of the instrument are similar to those of the first user is determined from the plurality of users, and the recommended information includes at least one of information on a piece of music that the first user has not performed but the second user has performed, and information on a function that the first user has not used but the second user has used during the performance of the instrument.

[0008] In a recommended device according to an aspect of the present invention, based on third performance-related information related to the performance of the instrument of a second user, recommended information indicating a piece of music or a function recommended for the performance of a first user is determined, the second user being a user whose trend or characteristics of the performance of the instrument are similar to those of the first user, determined from the plurality of users by comparing first performance-related information related to the performance of the instrument of the first user and second performance-related information related to the performance of the instrument of each of a plurality of users other than the first user, the third performance-related information being performance-related information among the second performance-related information, and the recommended information includes at least one of information on a piece of music that the first user has not performed but the second user has performed, and information on a function that the first user has not used but the second user has used during the performance.

[0009] In a recommended device according to an aspect of the present invention, in order to determine recommended information indicating a piece of music or a function recommended for the performance of an instrument by a first user, by comparing first performance-related information related to the performance of the instrument of the first user and second performance-related information related to the performance of the instrument of each of a plurality of users other than the first user, a second user whose trend or characteristics of the performance of the instrument are similar to those of the first user is determined from the plurality of users, and the recommended information includes at least one of information on a piece of music that the first user has not performed but the second user has performed, and information on a function that the first user has not used but the second user has used during the performance of the instrument.

[0010] A program product according to one aspect of the present invention causes a computer to perform the following processing: based on third performance-related information related to the performance of an instrument by a second user, determine recommendation information indicating a piece of music or a function recommended for the performance by a first user, where the second user is a user whose trend of instrument performance or characteristics of instrument performance are similar to those of the first user, and who is determined from among a plurality of users other than the first user by comparing first performance-related information related to the performance of the instrument by the first user and second performance-related information related to the performance of the instrument by each of the plurality of users other than the first user, the third performance-related information being performance-related information among the second performance-related information, and the recommendation information includes at least one of information on a piece of music that the first user has not performed but the second user has performed, and information on a function that the first user has not used but the second user has used during performance.

[0011] A program product according to one aspect of the present invention causes a computer to perform the following processing: in order to determine recommendation information indicating a piece of music or a function recommended for the performance by a first user, determine a second user whose trend of instrument performance or characteristics of instrument performance are similar to those of the first user from among a plurality of users by comparing first performance-related information related to the performance of the instrument by the first user and second performance-related information related to the performance of the instrument by each of the plurality of users other than the first user, and the recommendation information includes at least one of information on a piece of music that the first user has not performed but the second user has performed, and information on a function that the first user has not used but the second user has used during the performance of the instrument.

[0012] A recommendation device according to one aspect of the present invention includes: a communication device; and at least one processor; the at least one processor obtains performance information generated based on the performance of a first user via the communication device; determines, as a second user, at least one of other users whose trend of using music pieces is similar to that of the first user, other users whose trend of using functions during performance is similar to that of the first user, and other users whose characteristics of mastering music pieces can be determined to be similar to those of the first user based on the distribution of scores calculated according to the performance, from among a plurality of other users; determines recommendation information to be provided to the first user based on the performance information of the determined second user; and sends 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 by the target user.

[0013] According to the present invention, by providing high-quality information to the target user, it is possible to well support the instrument performance by the target user. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a system configuration diagram of an embodiment of an information providing system.

[0015] Figure 2 It is a diagram showing an example of the hardware configuration of a server or a terminal.

[0016] Figure 3 It is a sequence diagram showing an example of the operation of the information providing system according to the first embodiment.

[0017] Figure 4 It is a flowchart showing an example of terminal processing of the information providing system according to the second embodiment.

[0018] Figure 5 It is a flowchart showing an example of the process of evaluating and scoring a user's performance according to the second embodiment.

[0019] Figure 6 It is a flowchart showing an example of server processing of the information providing system according to the second embodiment.

[0020] Figure 7 It is a diagram showing an example of the log according to the second embodiment.

[0021] Figure 8 It is a diagram showing an example of the structure of the music database according to the second embodiment.

[0022] Figure 9 It is a diagram showing an example of the usage trend and the distance between users in the usage trend according to the second embodiment.

[0023] Figure 10 It is a diagram showing an example according to the score distribution and user type of users according to the second embodiment.

[0024] Figure 11 It is a diagram showing an example of the score log characteristics according to the second embodiment.

[0025] Figure 12 It is an explanatory diagram of the music mastery characteristics according to the second embodiment.

[0026] Figure 13 It is a diagram showing an example of the parameters of the music mastery characteristics and the distance between users in the music mastery characteristics according to the second embodiment. Specific Embodiments

[0027] Hereinafter, embodiments for implementing the present invention will be described in detail with reference to the accompanying drawings. Figure 1This is a system structure diagram of an embodiment of an information providing system. The musical instrument 103 is connected to a terminal 102 of a smart device such as a smart phone 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 a recommendation server 101 and a music piece data server 104 are connected via a wide area network or a local area network, particularly a router device (not shown).

[0028] The musical instrument 103 is, for example, an electronic keyboard instrument having a USB-MIDI interface or a 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 notes are pressed or released by the performance, and information indicating which functions such as a metronome, a time signature switch, a sustain pedal, a step-by-step course, an AB repeat, a soft pedal, and a sostenuto are operated.

[0029] 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 the data of the music piece played by the user on the musical instrument 103 from the music piece data server 104 according to the selection of the scoring function performed 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, 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 an analysis result of the performance from the recommendation server 101 and displays it.

[0030] The recommendation server 101 generates information such as recommendation information and visualization information by receiving the scoring information from the terminal 102 and sends it back to the terminal 102.

[0031] The music piece data server 104 sends music piece data such as note data and timing data of each musical note of the music piece when the user plays the musical instrument 103 to the terminal 102.

[0032] Figure 2 It means that it can be realized Figure 1A diagram showing an example of the hardware structure of a computer of the recommendation server 101, the terminal 102, or the music data server 104. In addition to server computers, such computers also include smartphones, tablet terminals, etc. Figure 2 The computer shown is equipped with a processor (e.g., a 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 interconnected by a bus 208. The structure shown in this diagram is an example of a computer that can implement each of the above devices 101, 102, or 104, and such a computer is not limited to this structure.

[0033] The memory 202 is, for example, 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.

[0034] The processor 201, for example, uses the memory 202 to execute programs corresponding to the processing of each of the flowcharts described later.

[0035] The input device 203 is, for example, a keyboard, a pointing device, etc., and is used for input of instructions or information from an operator or user. The output device 204 is, for example, a display device, a printer, a speaker, etc., and is used for output of inquiries or processing results to an operator or user.

[0036] The auxiliary information storage device 205 is, for example, an SSD (Solid State Drive), a hard disk storage device, a disk storage device, an optical disc device, an optical disk device, a disk device, or a semiconductor storage device. Each of the above devices 101, 102, or 104 has programs and data for executing the processing of each of the flowcharts described later stored in the auxiliary information storage device 205 in advance, and can load them into the memory 202 for use.

[0037] The medium drive device 206 drives the removable recording medium 209 and accesses its recorded content. The removable recording medium 209 is a memory device, a floppy disk, an optical disc, an optical disk, etc. The removable recording medium 209 can also be a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a universal serial bus (USB) memory, an SD memory card, etc. An operator or user can save the above programs and data on the removable recording medium 209 and load them into the memory 202 for use.

[0038] Thus, the computer-readable recording medium for storing the above program and data is a physical (non-transitory) recording medium such as the memory 202, the auxiliary information storage device 205, or the removable recording medium 209.

[0039] The communication device 207 is connected to a 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 devices 101, 102, or 104 can receive the above program or data from an external device connected to the network 105 via the communication device 207, and load them into the memory 202 for use. In addition, when the communication device 207 is built in Figure 1 the terminal 102, in addition to the above functions, it also has an interface function for connecting to Figure 1 the musical instrument 103 via an interface such as USB-MIDI.

[0040] In addition, each of the above devices 101, 102, or 104 does not need to include Figure 2 all the components, and a part of the components can also be omitted according to the use or conditions. For example, when it is not necessary 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 be omitted.

[0041] Use Figure 3 the sequence diagram to describe the first embodiment of the operation of the above Figure 1 and Figure 2 information providing system.

[0042] If a user (hereinafter, the user who plays the musical instrument 103 is called the "first user") connects the musical instrument 103 and the terminal 102 with, for example, USB-MIDI and plays a certain piece of music (the first piece of music) using the musical instrument 103, the processor 201 of the terminal 102 obtains performance-related information via the communication device 207 of the terminal 102, and uses this performance-related information as log information 301, and sends it from the communication device 207 of the terminal 102 to the recommendation server 101 via the network 105 ( Figure 3 order S301).

[0043] In addition, if the first user sets the scoring function to be effective and performs a performance, the processor 201 of the terminal 102 receives data of the music of the musical instrument to be performed by the first user from the music data server 104 via the network 105 and the communication device 207 of the terminal 102, and executes the process of scoring the performance of the musical instrument 103 performed by the first user with respect to the above performance-related information. The scoring information such as the intermediate result and the final result of the scoring is sent 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 in the order of S302).

[0044] In addition, the processor 201 of the terminal 102 displays the above scoring result on the output device 204 (for example, the liquid crystal display of the smart device of the terminal 102) ( Figure 3 in the order of S303).

[0045] In the recommendation server 101, the processor 201 of the recommendation server 101 receives the performance-related information and the scoring information transmitted as the log information 301 and 302 from the terminal 102 via the network 105 through the communication device 207 of the recommendation server 101. And the processor 201 sends the performance-related information (operations, music utilization, function utilization, etc.), the scoring information (scores), and the information determined based on the multiple performance-related information stored in the memory 202 corresponding to the performances by multiple other users including the second user other than the first user of the musical instrument 103, from the communication device 207 of the recommendation server 101 for providing to the above first user ( Figure 3 in the order of S304).

[0046] Here, the processor 201 of the recommendation server 101 classifies the first user into a certain type among multiple types including at least the relaxed type, the perseverant type, or the frustrated type based on the multiple performance-related information of the first user, and determines the information to be provided to the first user based on the performance-related information of the second users of the same type as the type into which the first user is classified.

[0047] In addition, in the case where the scoring information is transmitted from the terminal 102 as the log information 302, the processor 201 of the recommendation server 101 determines the above multiple types at least based on the score information and the practice period of the performance of each user.

[0048] Furthermore, the information to be provided to the above first user may include at least one of the information of a certain second music that the first user has not performed but other users have performed and the information of the function used by other users during the performance of the first music.

[0049] As an example of the more specific information to be provided to the first user, the following information can be cited.

[0050] · Next challenging piece of music (by level)

[0051] · Function for practice support

[0052] · Introduction of pieces of music frequently played by users at the same level

[0053] · Interval scoring / judgment and ranking of scored pieces of music

[0054] · Technical suggestions

[0055] Next, the processor 201 of the recommendation server 101 generates information for visualizing the recommendation information determined in the order S304 and the result of analysis according to the user type ( Figure 3 order S305).

[0056] Moreover, the processor 201 of the recommendation server 101 returns information such as the recommendation information determined in the order S303 and the visual information generated in the order S305 from the communication device 207 of the recommendation server 101 to the terminal 102 via the network 105 ( Figure 3 order S306).

[0057] Based on the visual information in the information transmitted from the recommendation server 101 to the terminal 102 via the network 105 and the communication device 207 of the terminal 102, together with the score result display in the order S303, the terminal 102 performs recommended display and visualization of the analysis result on the output device 204 (such as a liquid crystal display) of the terminal 102 by the following display method ( Figure 3 orders S307, S308).

[0058] · Text display of the above-mentioned recommendation information

[0059] · Time-series score information (also expressing interval scoring and suggestion explanations)

[0060] · Growth curve (with comparison with other users)

[0061] · Radar chart (indicating the characteristics of the first user)

[0062] Previously, musical instruments and smartphones were used to provide functions to users, but through the recommendation server 101, a wider range of optimal recommendation information can be provided. Playing a musical instrument alone is boring, so it helps to bring the motivation to continue like that. It enables one to realize that it is not just oneself practicing alone, and that others are also doing similar things. Instead of just oneself, information that can be used as a reference among what others have done can be provided as recommendation information. For example, if others notice things (pieces of music, functions) that one has not noticed, during the process of learning to play a favorite piece of music, appropriate suggestions or recommendations can be formed based on data (big data) of the learning processes of other similar users. For users in the learning process, they can obtain suggestions such as successful examples from real others. Since the suggestions are not derived from mechanical logic, they are more persuasive. One can be aware of others, and it is also possible to expect the effect of continuously increasing the motivation to continue practicing.

[0063] Next, the following Figures 4 to 6 flowchart and Figures 7 to 13 explanation diagram are used to describe the second embodiment of a more specific operation example of the information providing system representing Figure 1 and Figure 2

[0064] Figure 4 is a flowchart showing an example of terminal processing executed by the processor 201 of the terminal 102 of the information providing system in the second embodiment. This flowchart shows the processing in which the processor 201 in the terminal 102 executes the control program stored in the memory 202. Figure 1

[0065] Figure 5 is a flowchart showing a more detailed operation example of step S401 in the Figure 4 terminal processing.

[0066] Figure 6 is a flowchart showing an example of server processing executed by the processor 201 of the recommendation server 101 of the information providing system in the second embodiment. This flowchart shows the processing in which the processor 201 in the recommendation server 101 executes the control program stored in the memory 202. Figure 1

[0067] First, in the Figure 4 flowchart, similar to that of the above first embodiment Figure 3In the same manner as the operations of S301 and S302, the processor 201 of the terminal 102 acquires performance-related information from the musical instrument 103. On the other hand, by receiving 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, it performs a process of scoring the performance of the musical instrument 103 by the user with respect to the above-mentioned performance-related information ( Figure 4 step S401).

[0068] Use Figure 5 of the flowchart to explain the details of this process. First, the processor 201 of the terminal 102 initializes the exemplary performance, the area for accompaniment reproduction, various variables, etc. on the memory 202 of the terminal 102 ( Figure 5 step S501).

[0069] Next, the processor 201 of the terminal 102 starts sending out the exemplary performance and the automatic performance data for accompaniment obtained from the music data server 104 to the musical instrument 103 via USB-MIDI from the communication device 207 of the terminal 102, and starts the performance ( Figure 5 step S502).

[0070] Then, during the performance, the processor 201 of the terminal 102 repeatedly executes a series of processes of the following steps S503 to S505. In this series of processes, the processor 201 of the terminal 102 first executes the processes of exemplary performance and accompaniment reproduction ( Figure 5 step S503).

[0071] Specifically, in the exemplary performance and accompaniment reproduction processes, the processor 201 of the terminal 102 sequentially reads out the exemplary performance and the automatic performance data for accompaniment reproduction obtained from the music data server 104 into the memory 202 of the terminal 102, and outputs note numbers to the sound source of the musical instrument 103 at the timing of sounding / silencing according to the time. Through user operations, according to settings such as the beat or the on / off of the metronome, the presence or absence of guidance for the exemplary performance, etc., the reproduction process is executed.

[0072] Next, the processor 201 of the terminal 102 performs an evaluation process of the user's performance based on the performance-related information transmitted from the musical instrument 103 ( Figure 5Step S504). Specifically, the processor 201 of the terminal 102 increments the count of the number of correct answers, the number of accidental touches, etc., based on whether there is a model performance near the timing of the user's performance, the comparison of note numbers (pitches), and the deviation of timing. The number of correct answers can be, for example, "correct answer" or "roughly correct answer" according to the degree of the above deviation. In addition, if the count of "correct answer" and "roughly correct answer" is performed by dividing the music piece into the first half and the second half according to the elapsed time, in the determination of the user type (relaxed type, perseverant type, frustrated type, etc.) in the recommended server 101 described later, a more detailed determination of the user type can be made based on the difference between "the second half is completely hopeless, but the first half is okay" and "the whole is hopeless". In addition, the processor 201 of the terminal 102 counts the number of notes of the model performance for normalization in step S506 described later.

[0073] Next, the processor 201 of the terminal 102 determines whether the model performance and the accompaniment reproduction have ended and the performance has ended ( Figure 5 Step S505).

[0074] If the performance has not ended (the determination in step S505 is NO), the processor 201 of the terminal 102 repeatedly executes the series of processes of steps S503 to S505.

[0075] If the performance has ended (the determination in step S505 is YES), the processor 201 of the terminal 102 performs a scoring process ( Figure 5 Step S506). In this process, the processor 201 of the terminal 102 performs a deduction correction 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, with timing deviation and accidental touches.

[0076] Then, the processor 201 of the terminal 102 ends the Figure 5 evaluation process of Figure 4 step S401 shown in the flowchart.

[0077] Returning to Figure 4 the description, the processor 201 of the terminal 102 will display the scoring result based on the evaluation process in step S401 on the output device 204 (for example, the liquid crystal display of the smart device as the terminal 102) in the same manner as in the case of Figure 3 sequence S303 of the first embodiment ( Figure 4 Step S402).

[0078] In addition, the processor 201 of the terminal 102 uploads the scoring result in step S402 and the operation status of each function in the information received from the musical instrument 103 as performance-related information from the communication device 207 of the terminal 102 to the recommended server 101 via the network 105. Figure 4step S403).

[0079] Then, the processor 201 of the terminal 102 remains in a standby state until receiving a return from the recommendation server 101 via the communication device 207 of the terminal 102 from the network 105 ( Figure 4 repetition where the determination in step S404→S405 is NO).

[0080] Regarding the server processing of the recommendation server 101 for the above upload response Figure 6 is described. First, the processor 201 of the recommendation server 101 appends the score information and the functional 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 the operation log stored in the memory 202 or the auxiliary information storage device 205 of the recommendation server 101 ( Figure 6 step S601).

[0081] Figure 7 It is a diagram showing an example of the score log and the operation log specifically generated in Figure 6 step S601. In the score log illustrated in (a) of Figure 7 , the user ID identifying the user included in the score information received from the terminal 102, the music piece ID identifying the music piece scored, the date of scoring, and the score indicating the scoring result are recorded. In the operation log illustrated in (b) of Figure 7 , the user ID identifying the user included in the functional operation status of the function received from the terminal 102, the function ID identifying the function, the date when the function was operated, and the value indicating the operation amount of the function are recorded.

[0082] Next, the processor 201 of the recommendation server 101 determines the recommendation type based on the scoring result of the user's score information received from the terminal 102 and the past score log of the corresponding user ( Figure 6 step S602). Specifically, based on the user's past score log, when the current scoring result is already close to the highest score, or when the scoring result is low and there is no improvement in the scoring result, the recommendation type is set to "music piece", and in other cases, the recommendation type is set to "function".

[0083] Next, the processor 201 of the recommendation server 101 determines the recommendation type determined in step S602 ( Figure 6 step S603).

[0084] In the case where it is determined in the determination of step S603 that the recommended type is "music piece", the processor 201 of the recommendation server 101 analyzes the music piece usage trend while setting other users with similar music piece usage as the second user, and searches from the past score logs ([ Figure 6 step S604).

[0085] Figure 8 FIG. is an example of a music piece 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 piece usage trend. In the music piece database, as attributes of the music piece, a plurality of music piece data records are stored, each including a music piece ID for identifying the music piece, a difficulty level as an index indicating the difficulty of performance, the genre of the music piece, and the music piece name.

[0086] The processor 201 of the recommendation server 101 in Figure 6 step S604, according to Figure 7 the score logs of a certain recent period in (a) and Figure 8 the music piece database, for each user ID, for the music pieces recorded in the score logs, generates on the memory 202 of the recommendation server 101 a total of each item of the average value of the difficulty levels (difficulty level information of the music pieces practiced so far), the number of times of using practice pieces, the number of times of using pop music, the number of times of using piano pieces (genre information of the music pieces practiced so far), and the number of times of using singing, Figure 8 the music piece usage trend data exemplified in (a-1). Furthermore, the processor 201 of the recommendation server 101 in Figure 9 step S604, as shown in Figure 6 (a-2), selects a user group composed of any two users from the multiple users recorded in the score logs, calculates the sum of the squares of the differences between the two item values for each item according to the user group, and then calculates the square root of the accumulated result by accumulating all items according to the user group, and generates table data for calculating the distance of each user group on the memory 202 of the recommendation server 101. And the processor 201 of the recommendation server 101 in Figure 9 step S604, from Figure 6 the table data in (a-2), selects the user group that includes the user ID corresponding to the corresponding user notified from the terminal 102 and has the smallest distance, and uses other users with similar function usage trends as the second user, and finds the user in that user group who is not the corresponding user. Figure 9

[0087] ​That is, the server device determines other users whose music usage trends are similar to those of the first user as the second user, based on at least one of the difficulty information of the music practiced so far and the genre information of the music practiced so far.

[0088] In the above Figure 9 In the music usage trend data illustrated in (a-1) above, the counts of each item other than difficulty are totaled, but the proportions of each item may also be totaled.

[0089] Next, the processor 201 of the recommendation server 101 selects, as the recommendation information = recommended music, music that the corresponding user has not used from the music used by similar users found in step S604 by referring to the Figure 7 score log in (a) of Figure 6 (step S605 of

[0090] In the case where it is determined in the determination of step S603 that the recommendation type is "function", the processor 201 of the recommendation server 101 analyzes the function usage trend while analyzing the users or music mastery characteristics similar in function usage (the period and score change / score distribution trend required to master a certain piece of music), and searches for other users with similar music mastery characteristics as the second user from the past operation logs Figure 6 (step S606 of

[0091] First, the processing of step S606 in the case of analyzing the function usage trend will be described. The processor 201 of the recommendation server 101, in Figure 6 step S606 of Figure 7 generates, on the memory 202 of the recommendation server 101, function usage trend data that totals the usage counts of each function recorded in the operation log by user ID according to the operation log of a certain recent period in Figure 9 (b-1) of Figure 6 above. Furthermore, the processor 201 of the recommendation server 101, in Figure 9 step S606 of Figure 6 generates, on the memory 202 of the recommendation server 101, table data that calculates the distance for each user group by, as shown in Figure 9In the table data of (b - 2), select the user group that contains the user ID corresponding to the corresponding user notified from the terminal 102 and has the smallest distance, and use other users with similar function utilization trends as the second users, and find the user in this user group who is not the corresponding user.

[0092] In the above Figure 9 In the function utilization trend data illustrated in (b - 1), sum up the frequencies of each item, or the proportions of each item can also be summed up.

[0093] Next, the processing of step S606 in the case of analyzing the music mastering trend will be described. As the basic data for analyzing the music mastering trend, the score distribution according to users is effective. For this purpose, the processor 201 of the recommendation server 101 can calculate Figure 7 by clustering and summing up the score values of the score logs in (a) according to users, for example, performing cluster analysis, and can calculate Figure 10 the score distribution according to users (U001, U002, U003) as illustrated in (a). This score distribution diagram represents Figure 10 the user types as shown in (b) with the trend of the time series of scores as an index according to users. If observing Figure 10 in (a) and Figure 10 in (b), the user ID = U002 is the type that continuously improves the score and makes progress in playing from a lower score state for a relatively long time (for example, 1 month), and can be called the so-called perseverant type. In addition, the user ID = U003 is the type that practices only for a short time and gives up at a lower score, and can be called the so-called frustrated type. Furthermore, the user ID = U001 is the type that is good at playing with a relatively high score in a relatively short time, and can be called the so-called easy type. In step S606, the processor 201 of the recommendation server 101, in the data of the score distribution diagram calculated as shown in Figure 10 in (a), for example, selects the users in the cluster with the distance between the cluster centroids closest to the cluster of the corresponding user as the users with similar music mastering characteristics.

[0094] That is, the server device uses other users with music mastering characteristics similar to those of the first user as the second users, which is determined based on the types of users determined according to the score distribution of each user, and the similarity of the music mastering characteristics can be judged according to the distribution of the scores calculated according to the performance.

[0095] Next, other specific processing of step S606 in the case of finding other users with similar music mastering characteristics will be described. In step S606, the processor 201 of the recommendation server 101, in Figure 7In the score log of (a), after summarizing by user ID, calculate as Figure 12 For each of the index values shown, such as the initial score, the achieved score, the practice interval, the score change, and the score change during the current practice period, generate Figure 11 The table data exemplified in (a) of

[0096] Furthermore, in step S606, the processor 201 of the recommendation server 101 calculates the average value of each of the above index values according to the user ID based on the above table data, and generates Figure 13 The parameter table data of the music mastery characteristics exemplified in (a) of Figure 13 As shown in (b) of Figure 13 Select a user group composed of any two users logged in the score log. By calculating the square of the difference between the two index values for each of the above index values according to the user group, and then accumulating the index scores according to the user group and calculating the square root of the accumulated result, generate the table data for calculating the distance of each user group in the memory 202 of the recommendation server 101. And in step S606, the processor 201 of the recommendation server 101 selects from

[0097] The table data of (b) of

[0098] In the above Figure 6 After the processing of step S606, the processor 201 of the recommendation server 101 selects, as the recommendation information = recommended function ( Figure 7 In step S607), a function that the corresponding user has not used from the functions used by the similar users found in step S606 by referring to the Figure 6 Operation log of (b) of

[0099] After step S605 or S607, the processor 201 of the recommendation server 101 returns the recommended music or recommended function selected as the recommendation information from the communication device 207 of the recommendation server 101 to the terminal 102 via the network 105 ( Figure 6Step S608). Then, the processor 201 of the recommendation server 101 ends the server processing as the return processing for the terminal 102.

[0100] Return to Figure 4 Regarding the description of the terminal processing, if there is the above return from the recommendation server 101, the determination in step S405 is YES. As a result, the processor 201 of the terminal 102 displays the above 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 or the like of the output device 204 of the terminal 102, and performs recommendation display.

[0101] In the above second embodiment, by comparing Figure 13 (a) of Figure 10 with Figures 10 to 13 (b) of

[0102] it can be seen that the music mastery characteristic parameter table calculated as Figure 6 in step S606 of

[0103] in the above second embodiment, the processor 201 of the recommendation server 101 can randomly determine or change for each time which analysis method to adopt between the function utilization trend and the music mastery characteristic.

[0104] As described in the second embodiment, by analyzing respective trends such as the music utilization trend, the function utilization trend, and the music mastery characteristic, when recommending and introducing music, or recommending and introducing 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 the motivation for practice.

[0105] Furthermore, as described in Figure 5 step S504 of

[0106] As other modification examples other than the above-described first and second embodiments, in order to let the user know at what tempo the piece of music has been practiced, tempo information may be added during the score processing of the piece of music. By also recording the tempo information in the score log, when it is recommended to practice by slowing down the tempo as practice in case of no progress. Furthermore, in order to let the user know at what tempo practicing with respect to the learning degree (score) of the piece of music is likely to improve, by referring to the logs of users similar to the user's level, an example of a tempo value suitable for practice may be recommended.

[0107] Furthermore, as another modification example, usage information of a metronome, REC, AB repeat, which are functions assumed to be used during practice, may be added. Regarding functions with a relatively high usage frequency, they can be recommended as functions that may be effective for practicing a specific piece of music. For example, when the function with the highest usage frequency for a certain user until achieving a high score in the piece of music is the metronome, the metronome can be recommended as a recommended practice function for the same piece of music.

[0108] That is, the server device determines the second user as another user whose function usage trend during performance is similar to that of the first user, based on at least one of the number of times the metronome has been used, the number of tempo changes, and the number of times the pedal has been used during practice so far.

[0109] As still another modification example, the genre of the pieces of music that the user is good at / likes may be narrowed down according to the user's highest score and the number of times of use, and a piece of music with a difficulty level close to the average difficulty level of the pieces of music used by the user may be recommended within the genre.

[0110] As still another modification example, the preferred style may be inferred based on the number of times of use, and pieces of music may be recommended accordingly.

[0111] As described above, the embodiments of the present invention and their advantages have been described in detail. However, those skilled in the art can make various changes, additions, and omissions without departing from the scope of the present invention clearly recited in the claims. That is, even if all of these processes are not executed by one processor but by multiple processors or separately by multiple devices, these embodiments also fall within the scope of the claims of the present invention.

[0112] In addition, the present invention is not limited to the above-described embodiments, and can be variously modified within the scope without departing from the gist thereof during the implementation phase. Further, as long as possible, the respective embodiments can be appropriately combined and implemented, and in this case, the combined effects can be obtained. Furthermore, the invention includes inventions at various stages in the above-described embodiments, and various inventions can be extracted by appropriately combining the disclosed plurality of constituent elements. For example, even if some of the constituent elements shown in the embodiments are deleted, if the problem described in the problem to be solved by the invention column can be solved and the effect described in the effect of the invention column can be obtained, the structure from which the constituent element has been deleted can be extracted as an invention.

Claims

1. A recommendation method, wherein, based on the third performance-related information involved in the performance of the musical instrument of the second user, recommendation information representing a piece of music or a function recommended for the performance of the first user is determined. The second user is a user whose trend of musical instrument performance or characteristics of musical instrument performance similar to those of the first user is determined from the plurality of users by comparing the first performance-related information involved in the performance of the musical instrument of the first user and the second performance-related information involved in the performance of the musical instruments of each of the plurality of users other than the first user. The third performance-related information is the performance-related information among the second performance-related information. The recommendation information includes at least one of information on a piece of music that the first user has not performed but the second user has performed, and information on a function that the first user has not used but the second user has used during performance.

2. The recommendation method according to claim 1, wherein, the first performance-related information and the second performance-related information include at least one of information on pieces of music performed by the first user and the plurality of users, functions used by the first user and the plurality of users during performance, and scores for the performances of the first user and the plurality of users.

3. The recommendation method according to claim 1, wherein, other users similar to the first user in at least one of the music utilization trend, the function utilization trend during performance, and the music mastery characteristics that can be determined based on the distribution of scores calculated for each performance are determined as the second user.

4. The recommendation method according to claim 3, wherein, for each of the plurality of users, at least a plurality of numerical information items corresponding to a plurality of items respectively associated with at least one of the music utilization trend, the function utilization trend, and the music mastery characteristics are obtained. For each user group that is a group of the first user and other users, the plurality of numerical information items are used to determine a user similar to the first user in at least one of the music utilization trend, the function utilization trend, and the music mastery characteristics as the second user.

5. The recommendation method according to claim 1, wherein, based on the first performance-related information involved in the performance of the first user, it is determined whether the recommendation type for the first user is a piece of music or a function. According to the determined recommendation type, a piece of music or a function that the first user has not used is determined as the recommendation information from the pieces of music or functions being used by the second user.

6. The recommendation method according to any one of claims 1 to 5, wherein, from the plurality of users, other users with a music utilization trend similar to that of the first user are used as the second user, and the second user is determined based on at least one of the difficulty information of the pieces of music practiced so far and the genre information of the pieces of music practiced so far.

7. The recommendation method according to any one of claims 1 to 5, wherein, Among the multiple users, other users whose functional usage trends during performance are similar to those of the first user are regarded as the second user, and the second user is determined based on at least one of the number of times the metronome has been used, the beat information, and the number of times the pedal has been used during practice up to this point.

8. The recommendation method according to any one of claims 1 to 5, wherein, Among the multiple users, other users whose piece mastery characteristics that can be judged based on the distribution of scores calculated for each performance are similar to those of the first user are regarded as the second user, and the second user is determined according to the type of user determined by the score distribution of each user.

9. A recommendation method, wherein, In order to determine the recommendation information for the piece or function recommended for the performance of the instrument of the first user, by comparing the first performance-related information related to the performance of the instrument of the first user and the second performance-related information related to the performances of the instruments of multiple users other than the first user, a second user whose trend or characteristics of the performance of the instrument are similar to those of the first user is determined from the multiple users, and the recommendation information includes at least one of the information of the pieces that the second user has performed but the first user has not, and the information of the functions that the second user has used during the performance of the instrument but the first user has not.

10. A recommendation device, wherein, Based on the third performance-related information related to the performance of the instrument of the second user, the recommendation information for the piece or function recommended for the performance of the first user is determined. The second user is a user whose trend or characteristics of the performance of the instrument are similar to those of the first user, determined from the multiple users by comparing the first performance-related information related to the performance of the instrument of the first user and the second performance-related information related to the performances of the instruments of multiple users other than the first user. The third performance-related information is the performance-related information among the second performance-related information, and the recommendation information includes at least one of the information of the pieces that the second user has performed but the first user has not, and the information of the functions that the second user has used during the performance but the first user has not.

11. A recommendation device, wherein, In order to determine the recommendation information for the piece or function recommended for the performance of the instrument of the first user, by comparing the first performance-related information related to the performance of the instrument of the first user and the second performance-related information related to the performances of the instruments of multiple users other than the first user, a second user whose trend or characteristics of the performance of the instrument are similar to those of the first user is determined from the multiple users, and the recommendation information includes at least one of the information of the pieces that the second user has performed but the first user has not, and the information of the functions that the second user has used during the performance of the instrument but the first user has not.

12. A program product that causes a computer to perform the following processing: Based on the third performance-related information involved in the performance of the musical instrument by the second user, determine the recommendation information indicating the piece of music or function recommended for the performance by the first user. The second user is a user whose trend of musical instrument performance or characteristics of musical instrument performance are similar to those of the first user, and is determined from the multiple users by comparing the first performance-related information involved in the performance of the musical instrument by the first user and the second performance-related information involved in the performance of the musical instrument by each of the multiple users other than the first user. The third performance-related information is the performance-related information among the second performance-related information. The recommendation information includes at least one of the information on the piece of music that the second user has performed but the first user has not, and the information on the function that the second user has used during performance but the first user has not.

13. A program product that causes a computer to perform the following processing: In order to determine the recommendation information indicating the piece of music or function recommended for the performance by the first user, by comparing the first performance-related information involved in the performance of the musical instrument by the first user and the second performance-related information involved in the performance of the musical instrument by each of the multiple users other than the first user, determine the second user whose trend of musical instrument performance or characteristics of musical instrument performance are similar to those of the first user from the multiple users. The recommendation information includes at least one of the information on the piece of music that the second user has performed but the first user has not, and the information on the function that the second user has used during the performance of the musical instrument but the first user has not.

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