system

Abstract

An object of a system according to an embodiment is to improve a user's walking experience by automatically generating music according to a walking speed.SOLUTION: A system includes a step counting part, a BPM determination part, a musical piece generation part, and a tempo adjustment part. The step counting unit measures a walking speed using a step counting function of the smartphone. The BPM determining unit determines the BPM based on the data measured by the step counting unit. The music generation unit generates music on the basis of the BPM determined by the BPM determination unit. The tempo adjusting unit increases or decreases the BPM according to the user's desire.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies are unable to automatically generate music according to walking speed, and there is room for improvement in terms of improving the user's walking experience.

[0005] The system according to the embodiment aims to automatically generate music according to walking speed, thereby improving the walking experience of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a step counting unit, a BPM determining unit, a music generating unit, and a tempo adjusting unit. The step counting unit measures walking speed using the step counting function of the smartphone. The BPM determining unit determines the BPM based on the data measured by the step counting unit. The music generating unit generates music based on the BPM determined by the BPM determining unit. The tempo adjusting unit increases or decreases the BPM according to the user's wishes. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate music according to walking speed, improving the walking experience of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The BGM generation system according to the embodiment of the present invention determines the BPM based on the user's walking speed, generates music that matches that BPM, and adjusts the tempo according to the user's wishes. This allows the BGM generation system to provide music that matches the user's walking speed, making walking and exercise more enjoyable.

[0029] A background music generation system according to an embodiment includes a step counting unit, a BPM determination unit, a music generation unit, and a tempo adjustment unit. The step counting unit measures walking speed using a step counting function of a smartphone. For example, the step counting unit counts steps using an acceleration sensor built into the smartphone to measure walking speed. The BPM determination unit determines the BPM based on the data measured by the step counting unit. For example, if a user walks 100 steps per minute, the BPM determination unit determines the BPM to be 100. The music generation unit generates music based on the BPM determined by the BPM determination unit. For example, the music generation unit generates music that matches the BPM using a generation AI (e.g., a text generation AI or a multimodal generation AI). The tempo adjustment unit increases or decreases the BPM according to the user's preference. For example, if the user desires a faster tempo, the tempo adjustment unit sets the BPM to 120 and generates music that matches that tempo. This allows the BGM generation system to determine the BPM based on the user's walking speed, generate music that matches that BPM, and adjust the tempo according to the user's wishes.

[0030] In addition to counting steps, the step counting unit can analyze the user's walking pattern to determine the BPM more accurately. The step counting unit uses, for example, a combination of the smartphone's acceleration sensor and gyro sensor. For example, the step counting unit analyzes the stride length and walking rhythm in detail to capture walking characteristics. As a result, if the stride length is large, the BPM can be set lower, and if the stride length is small, the BPM can be set higher. In this way, by analyzing the user's walking pattern, the BPM can be determined more accurately.

[0031] The BPM determination unit can use a sensor to detect environmental sounds while walking and adjust the BPM taking them into account. The BPM determination unit detects environmental sounds while walking in real time, for example, using a smartphone microphone. For example, the BPM determination unit detects the sound of wind or traffic and analyzes their volume and frequency. As a result, if the environmental sounds are loud, the BPM is increased to attract the user's attention. In this way, a more appropriate BPM can be provided by taking environmental sounds into account.

[0032] The step counting unit can also be applied to other exercises, and can determine the BPM appropriate for each exercise. For example, the step counting unit counts the number of steps while running, and determines the BPM appropriate for running. For example, the step counting unit sets a BPM that matches the running rhythm based on the step count data while running. This improves running performance. This makes it applicable to other exercises such as running and cycling.

[0033] The step counting unit stores the step count data in the cloud and shares it with other users, thereby determining a BPM suitable for walking or exercising in a group. The step counting unit, for example, stores the step count data in the cloud and shares it with other users. For example, data from users walking in the same place is integrated to determine a BPM suitable for walking in a group. This makes it possible to provide a BPM suitable for walking or exercising in a group.

[0034] The music generation unit can generate individually customized music by reflecting the user's past music preferences. For example, the music generation unit collects the user's past music preference data and customizes the music based on that data using AI. For example, the music generation unit generates individually customized music by referring to music by genres and artists that the user likes. This makes it possible to provide music that reflects the user's past music preferences.

[0035] The music generation unit can feed back the user's walking data in real time and dynamically adjust the music. For example, the music generation unit collects the user's walking data in real time, and the AI ​​dynamically adjusts the music based on that data. For example, the music generation unit increases the tempo if the user's walking speed increases, and decreases the tempo if the user's walking speed decreases. This allows the walking data to be fed back in real time and the music to be dynamically adjusted.

[0036] The music generation unit can combine natural environmental sounds to enhance the relaxing effect. For example, the music generation unit can combine natural environmental sounds with music created by AI. For example, the music generation unit can incorporate the sounds of birds chirping and a flowing river into the music to enhance the relaxing effect. In this way, the relaxing effect can be enhanced by combining natural environmental sounds.

[0037] The music generation unit can accommodate different genres, allowing users to select songs according to their preferences. For example, the music generation unit can accommodate music created by AI to different genres. For example, the music generation unit can generate music corresponding to genres such as classical, jazz, and rock, allowing users to select from these. This allows music to be provided according to the user's preferences by accommodating different genres.

[0038] The tempo adjustment unit can acquire the user's heart rate data in real time and automatically adjust the tempo according to the heart rate. For example, the tempo adjustment unit acquires the user's heart rate data in real time and automatically adjusts the tempo based on that data. For example, the tempo adjustment unit increases the tempo when the heart rate increases, and decreases the tempo when the heart rate decreases. This makes it possible to provide a tempo that corresponds to the user's heart rate.

[0039] The tempo adjustment unit can suggest an optimal tempo based on the user's exercise goal. For example, the tempo adjustment unit collects the user's exercise goal data and suggests an optimal tempo based on that data. For example, the tempo adjustment unit sets a tempo according to the user's calorie consumption goal to maximize the exercise effect. This makes it possible to provide an optimal tempo according to the user's exercise goal.

[0040] The tempo adjustment unit can also apply the tempo adjustment function to other exercises, providing a tempo suitable for each exercise. The tempo adjustment unit provides a tempo suitable for exercises such as yoga and Pilates. For example, the tempo adjustment unit sets a rhythm that matches yoga poses to enhance the relaxing effect. This makes it possible to apply the tempo adjustment function to other exercises such as yoga and Pilates.

[0041] The tempo adjustment unit can apply the tempo adjustment function to group exercise and integrate the step count data of all members to provide an optimal tempo. The tempo adjustment unit can provide a tempo suitable for group exercise (e.g., a dance class), for example. For example, the tempo adjustment unit can integrate the step count data of all members and set an optimal tempo for the entire group. This can provide a tempo suitable for group exercise.

[0042] The music playback unit can monitor the user's walking data in real time while playing music and dynamically adjust the tempo of the music according to the walking speed. For example, the music playback unit can monitor the user's walking data in real time while playing music and dynamically adjust the tempo of the music based on the data. For example, the music playback unit can increase the tempo if the walking speed increases, and decrease the tempo if the walking speed decreases. This makes it possible to provide a music tempo that corresponds to the walking speed.

[0043] The music playback unit can detect environmental sounds around the user while playing music and adjust the volume and effects of the music accordingly. For example, the music playback unit can detect environmental sounds around the user in real time while playing music and adjust the volume and effects of the music based on that data. For example, the music playback unit can increase the volume if the environmental sounds are loud and decrease the volume if the environmental sounds are quiet. This allows the volume and effects of the music to be adjusted according to the environmental sounds.

[0044] The music playback unit can make the music playback function compatible with other devices, providing a more diverse playback environment. For example, the music playback unit can make the music playback function compatible with smartwatches. For example, the music playback unit can make it possible to play / pause music and adjust the tempo on the smartwatch screen. This makes it possible to support other devices, providing a more diverse playback environment.

[0045] The music playback unit can collect user feedback in real time while the music is being played and reflect it in the next music generation. For example, the music playback unit can collect user feedback in real time while the music is being played and reflect that data in the next music generation. For example, the music playback unit can allow the user to input their evaluation of the tempo and rhythm of the music. This allows the user's feedback to be reflected in the next music generation.

[0046] The user interface can provide visual feedback of the walking data and BPM, allowing the user to intuitively understand their walking performance. The user interface, for example, provides visual feedback of the walking data and BPM. For example, the user interface displays the walking data using graphs and charts, allowing the user to intuitively understand their walking performance. This allows the user to intuitively understand their walking performance.

[0047] The user interface may add a voice assistant function to allow a user to adjust the tempo or playback of a song through a voice command. The user interface may, for example, add a voice assistant function to allow a user to adjust the tempo or playback of a song through a voice command. For example, the user interface may allow a user to adjust the tempo by simply saying "speed up the tempo." This allows a user to adjust the tempo or playback of a song through a voice command.

[0048] The user interface can be integrated with other exercise apps to provide a unified operating environment. For example, the user interface can be linked with a running app and a fitness app to allow users to manage all their exercise data in one app. This allows integration with other exercise apps to provide a unified operating environment.

[0049] The user interface may add a social function to enable the user to share walking data or music with other users. The user interface may add, for example, a social function to enable the user to share walking data or music with other users. For example, the user interface may enable the user to compare walking data with friends or share music. This allows the user to share walking data or music with other users.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The BPM determination unit can also monitor the user's heart rate in real time and adjust the BPM based on that data. For example, if the heart rate rises, the BPM is increased, and if the heart rate drops, the BPM is decreased. This allows the system to provide a BPM that suits the user's physical condition. Heart rate data can also be stored in the cloud and used for long-term health management. Furthermore, it is possible to analyze heart rate fluctuation patterns and suggest a BPM that suits the user's exercise intensity.

[0052] The music generation unit can also generate music according to the type and intensity of exercise based on the user's past exercise data. For example, it can analyze past running data and generate music with a tempo suitable for running. It can also provide music of different genres depending on the type of exercise the user prefers. Furthermore, it can generate refreshing music for morning exercise and relaxing music for evening exercise depending on the time of day of exercise.

[0053] The tempo adjustment unit can also suggest an optimal tempo based on the user's exercise goal. For example, it can set the tempo according to the calorie consumption goal to maximize the exercise effect. It can also suggest an appropriate tempo based on the exercise time set by the user. Furthermore, it can suggest different tempos depending on the type of exercise, improving the user's exercise performance.

[0054] The music playback unit can also detect environmental sounds around the user and adjust the volume and effects of the music accordingly. For example, if the environmental sounds are loud, the volume is increased, and if the environmental sounds are quiet, the volume is decreased. Furthermore, if a specific environmental sound (e.g., the sound of traffic or wind) is detected, the music effects can be adjusted to match that sound. Furthermore, environmental sounds can be incorporated into the music to provide a more natural music experience.

[0055] The user interface can also add a voice assistant function, allowing users to adjust the tempo and playback of music with voice commands. For example, users can adjust the tempo by simply saying "speed up the tempo." Voice commands can also be used to play, stop, or skip music. Furthermore, the voice assistant can provide real-time feedback on the user's exercise status and provide appropriate advice.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The step counting unit measures walking speed using the step counting function of the smartphone. For example, the step counting unit counts steps using an acceleration sensor built into the smartphone and measures walking speed. Step 2: The BPM determination unit determines the BPM based on the data measured by the step count unit. For example, if you walk 100 steps in one minute, the BPM determination unit determines the BPM to be 100. Step 3: The music generation unit generates music based on the BPM determined by the BPM determination unit. For example, the music generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate music that matches the BPM. Step 4: The tempo adjustment unit raises or lowers the BPM according to the user's preference. For example, if the user desires an up-tempo song, the tempo adjustment unit sets the BPM to 120 and generates music that matches that tempo.

[0058] (Example 2) The BGM generation system according to the embodiment of the present invention determines the BPM based on the user's walking speed, generates music that matches that BPM, and adjusts the tempo according to the user's wishes. This allows the BGM generation system to provide music that matches the user's walking speed, making walking and exercise more enjoyable.

[0059] A background music generation system according to an embodiment includes a step counting unit, a BPM determination unit, a music generation unit, and a tempo adjustment unit. The step counting unit measures walking speed using a step counting function of a smartphone. For example, the step counting unit counts steps using an acceleration sensor built into the smartphone to measure walking speed. The BPM determination unit determines the BPM based on the data measured by the step counting unit. For example, if a user walks 100 steps per minute, the BPM determination unit determines the BPM to be 100. The music generation unit generates music based on the BPM determined by the BPM determination unit. For example, the music generation unit generates music that matches the BPM using a generation AI (e.g., a text generation AI or a multimodal generation AI). The tempo adjustment unit increases or decreases the BPM according to the user's preference. For example, if the user desires a faster tempo, the tempo adjustment unit sets the BPM to 120 and generates music that matches that tempo. This allows the BGM generation system to determine the BPM based on the user's walking speed, generate music that matches that BPM, and adjust the tempo according to the user's wishes.

[0060] In addition to counting steps, the step counting unit can analyze the user's walking pattern to determine the BPM more accurately. The step counting unit uses, for example, a combination of the smartphone's acceleration sensor and gyro sensor. For example, the step counting unit analyzes the stride length and walking rhythm in detail to capture walking characteristics. As a result, if the stride length is large, the BPM can be set lower, and if the stride length is small, the BPM can be set higher. In this way, by analyzing the user's walking pattern, the BPM can be determined more accurately.

[0061] The BPM determination unit can use a sensor to detect environmental sounds while walking and adjust the BPM taking them into account. The BPM determination unit detects environmental sounds while walking in real time, for example, using a smartphone microphone. For example, the BPM determination unit detects the sound of wind or traffic and analyzes their volume and frequency. As a result, if the environmental sounds are loud, the BPM is increased to attract the user's attention. In this way, a more appropriate BPM can be provided by taking environmental sounds into account.

[0062] The BPM determination unit can use the emotion estimation function to analyze the user's emotional state and determine the BPM according to the emotion. The BPM determination unit uses, for example, a smartphone camera to analyze the user's facial expression and estimate the emotional state. For example, the BPM determination unit may determine that a smiling face represents a positive emotion and increase the BPM. Conversely, a sad face may represent a negative emotion and decrease the BPM. This makes it possible to provide a BPM according to the user's emotional state.

[0063] The step counting unit can also be applied to other exercises, and can determine the BPM appropriate for each exercise. For example, the step counting unit counts the number of steps while running, and determines the BPM appropriate for running. For example, the step counting unit sets a BPM that matches the running rhythm based on the step count data while running. This improves running performance. This makes it applicable to other exercises such as running and cycling.

[0064] The step counting unit stores the step count data in the cloud and shares it with other users, thereby determining a BPM suitable for walking or exercising in a group. The step counting unit, for example, stores the step count data in the cloud and shares it with other users. For example, data from users walking in the same place is integrated to determine a BPM suitable for walking in a group. This makes it possible to provide a BPM suitable for walking or exercising in a group.

[0065] The BPM determination unit can use the emotion estimation function to analyze the stress level felt by the user while walking and determine a BPM for reducing stress. The BPM determination unit, for example, uses the emotion estimation function to analyze the stress level felt by the user while walking. For example, the BPM determination unit analyzes facial expressions and voice to quantify the stress level. As a result, if the stress level is high, the BPM is lowered to enhance the relaxation effect. This makes it possible to provide a BPM that suits the user's stress level.

[0066] The music generation unit can generate individually customized music by reflecting the user's past music preferences. For example, the music generation unit collects the user's past music preference data and customizes the music based on that data using AI. For example, the music generation unit generates individually customized music by referring to music by genres and artists that the user likes. This makes it possible to provide music that reflects the user's past music preferences.

[0067] The music generation unit can feed back the user's walking data in real time and dynamically adjust the music. For example, the music generation unit collects the user's walking data in real time, and the AI ​​dynamically adjusts the music based on that data. For example, the music generation unit increases the tempo if the user's walking speed increases, and decreases the tempo if the user's walking speed decreases. This allows the walking data to be fed back in real time and the music to be dynamically adjusted.

[0068] The music generation unit can use the emotion estimation function to adjust the atmosphere of the music according to the emotional state of the user. For example, the music generation unit uses the emotion estimation function to analyze the emotional state of the user and adjust the atmosphere of the music based on the results. For example, the music generation unit generates a bright piece of music when the user is feeling positive, and a calm piece of music when the user is feeling negative. This makes it possible to provide a music atmosphere according to the user's emotional state.

[0069] The music generation unit can combine natural environmental sounds to enhance the relaxing effect. For example, the music generation unit can combine natural environmental sounds with music created by AI. For example, the music generation unit can incorporate the sounds of birds chirping and a flowing river into the music to enhance the relaxing effect. In this way, the relaxing effect can be enhanced by combining natural environmental sounds.

[0070] The music generation unit can accommodate different genres, allowing users to select songs according to their preferences. For example, the music generation unit can accommodate music created by AI to different genres. For example, the music generation unit can generate music corresponding to genres such as classical, jazz, and rock, allowing users to select from these. This allows music to be provided according to the user's preferences by accommodating different genres.

[0071] The music generation unit can use the emotion estimation function to automatically generate music that matches a specific emotion when the user feels that emotion. For example, the music generation unit uses the emotion estimation function to automatically generate music that matches a specific emotion when the user feels that emotion. For example, the music generation unit generates a cheerful song when the user feels joy, and a calm song when the user feels sad. In this way, music that matches a specific emotion of the user can be automatically generated.

[0072] The tempo adjustment unit can acquire the user's heart rate data in real time and automatically adjust the tempo according to the heart rate. For example, the tempo adjustment unit acquires the user's heart rate data in real time and automatically adjusts the tempo based on that data. For example, the tempo adjustment unit increases the tempo when the heart rate increases, and decreases the tempo when the heart rate decreases. This makes it possible to provide a tempo that corresponds to the user's heart rate.

[0073] The tempo adjustment unit can suggest an optimal tempo based on the user's exercise goal. For example, the tempo adjustment unit collects the user's exercise goal data and suggests an optimal tempo based on that data. For example, the tempo adjustment unit sets a tempo according to the user's calorie consumption goal to maximize the exercise effect. This makes it possible to provide an optimal tempo according to the user's exercise goal.

[0074] The tempo adjustment unit uses the emotion estimation function to adjust the tempo according to the user's emotional state and respond to changes in emotion. The tempo adjustment unit, for example, uses the emotion estimation function to analyze the user's emotional state and adjusts the tempo based on the results. For example, the tempo adjustment unit increases the tempo when the user is feeling positive and decreases the tempo when the user is feeling negative. This makes it possible to provide a tempo that matches the user's emotional state.

[0075] The tempo adjustment unit can also apply the tempo adjustment function to other exercises, providing a tempo suitable for each exercise. The tempo adjustment unit provides a tempo suitable for exercises such as yoga and Pilates. For example, the tempo adjustment unit sets a rhythm that matches yoga poses to enhance the relaxing effect. This makes it possible to apply the tempo adjustment function to other exercises such as yoga and Pilates.

[0076] The tempo adjustment unit can apply the tempo adjustment function to group exercise and integrate the step count data of all members to provide an optimal tempo. The tempo adjustment unit can provide a tempo suitable for group exercise (e.g., a dance class), for example. For example, the tempo adjustment unit can integrate the step count data of all members and set an optimal tempo for the entire group. This can provide a tempo suitable for group exercise.

[0077] The tempo adjustment unit can use the emotion estimation function to automatically adjust the tempo to match the user's specific emotion when the user feels that emotion. For example, the tempo adjustment unit uses the emotion estimation function to automatically adjust the tempo to match the user's specific emotion when the user feels that emotion. For example, the tempo adjustment unit increases the tempo when the user feels joy and decreases the tempo when the user feels sad. This makes it possible to automatically provide a tempo that matches the user's specific emotion.

[0078] The music playback unit can monitor the user's walking data in real time while playing music and dynamically adjust the tempo of the music according to the walking speed. For example, the music playback unit can monitor the user's walking data in real time while playing music and dynamically adjust the tempo of the music based on the data. For example, the music playback unit can increase the tempo if the walking speed increases, and decrease the tempo if the walking speed decreases. This makes it possible to provide a music tempo that corresponds to the walking speed.

[0079] The music playback unit can detect environmental sounds around the user while playing music and adjust the volume and effects of the music accordingly. For example, the music playback unit can detect environmental sounds around the user in real time while playing music and adjust the volume and effects of the music based on that data. For example, the music playback unit can increase the volume if the environmental sounds are loud and decrease the volume if the environmental sounds are quiet. This allows the volume and effects of the music to be adjusted according to the environmental sounds.

[0080] The music playback unit can use the emotion estimation function to automatically adjust the playback order of songs according to the user's emotional state. For example, the music playback unit uses the emotion estimation function to analyze the user's emotional state and automatically adjust the playback order of songs based on the results. For example, the music playback unit preferentially plays cheerful songs when the user is feeling positive, and calm songs when the user is feeling negative. This makes it possible to provide a playback order of songs according to the user's emotional state.

[0081] The music playback unit can make the music playback function compatible with other devices, providing a more diverse playback environment. For example, the music playback unit can make the music playback function compatible with smartwatches. For example, the music playback unit can make it possible to play / pause music and adjust the tempo on the smartwatch screen. This makes it possible to support other devices, providing a more diverse playback environment.

[0082] The music playback unit can collect user feedback in real time while the music is being played and reflect it in the next music generation. For example, the music playback unit can collect user feedback in real time while the music is being played and reflect that data in the next music generation. For example, the music playback unit can allow the user to input their evaluation of the tempo and rhythm of the music. This allows the user's feedback to be reflected in the next music generation.

[0083] The music playback unit can use the emotion estimation function to automatically play music that matches the user's emotion when the user feels that emotion. For example, the music playback unit uses the emotion estimation function to automatically play music that matches the user's emotion when the user feels that emotion. For example, the music playback unit plays a cheerful song when the user feels happy, and a calm song when the user feels sad. This makes it possible to automatically play music that matches the user's emotion.

[0084] The user interface can provide visual feedback of the walking data and BPM, allowing the user to intuitively understand their walking performance. The user interface, for example, provides visual feedback of the walking data and BPM. For example, the user interface displays the walking data using graphs and charts, allowing the user to intuitively understand their walking performance. This allows the user to intuitively understand their walking performance.

[0085] The user interface may add a voice assistant function to allow a user to adjust the tempo or playback of a song through a voice command. The user interface may, for example, add a voice assistant function to allow a user to adjust the tempo or playback of a song through a voice command. For example, the user interface may allow a user to adjust the tempo by simply saying "speed up the tempo." This allows a user to adjust the tempo or playback of a song through a voice command.

[0086] The user interface can dynamically change the design and color scheme of the interface according to the emotional state of the user using the emotion estimation function. For example, the user interface uses the emotion estimation function to analyze the emotional state of the user and dynamically changes the design and color scheme of the interface based on the results. For example, the user interface changes to a bright color scheme when the user is feeling positive, and changes to a subdued color scheme when the user is feeling negative. This makes it possible to provide the design and color scheme of the interface according to the emotional state of the user.

[0087] The user interface can be integrated with other exercise apps to provide a unified operating environment. For example, the user interface can be linked with a running app and a fitness app to allow users to manage all their exercise data in one app. This allows integration with other exercise apps to provide a unified operating environment.

[0088] The user interface may add a social function to enable the user to share walking data or music with other users. The user interface may add, for example, a social function to enable the user to share walking data or music with other users. For example, the user interface may enable the user to compare walking data with friends or share music. This allows the user to share walking data or music with other users.

[0089] The user interface can use the emotion estimation function to suggest an interface that matches the user's emotion when the user feels a particular emotion. For example, the user interface can use the emotion estimation function to suggest an interface that matches the user's emotion when the user feels a particular emotion. For example, the user interface can suggest a calm interface when the user is relaxed, and an energetic interface when the user is excited. In this way, an interface that matches the user's particular emotion can be suggested.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The BPM determination unit can also monitor the user's heart rate in real time and adjust the BPM based on that data. For example, if the heart rate rises, the BPM is increased, and if the heart rate drops, the BPM is decreased. This allows the system to provide a BPM that suits the user's physical condition. Heart rate data can also be stored in the cloud and used for long-term health management. Furthermore, it is possible to analyze heart rate fluctuation patterns and suggest a BPM that suits the user's exercise intensity.

[0092] The music generation unit can also generate music according to the type and intensity of exercise based on the user's past exercise data. For example, it can analyze past running data and generate music with a tempo suitable for running. It can also provide music of different genres depending on the type of exercise the user prefers. Furthermore, it can generate refreshing music for morning exercise and relaxing music for evening exercise depending on the time of day of exercise.

[0093] The tempo adjustment unit can also suggest an optimal tempo based on the user's exercise goal. For example, it can set the tempo according to the calorie consumption goal to maximize the exercise effect. It can also suggest an appropriate tempo based on the exercise time set by the user. Furthermore, it can suggest different tempos depending on the type of exercise, improving the user's exercise performance.

[0094] The music playback unit can also detect environmental sounds around the user and adjust the volume and effects of the music accordingly. For example, if the environmental sounds are loud, the volume is increased, and if the environmental sounds are quiet, the volume is decreased. Furthermore, if a specific environmental sound (e.g., the sound of traffic or wind) is detected, the music effects can be adjusted to match that sound. Furthermore, environmental sounds can be incorporated into the music to provide a more natural music experience.

[0095] The user interface can also add a voice assistant function, allowing users to adjust the tempo and playback of music with voice commands. For example, users can adjust the tempo by simply saying "speed up the tempo." Voice commands can also be used to play, stop, or skip music. Furthermore, the voice assistant can provide real-time feedback on the user's exercise status and provide appropriate advice.

[0096] The BPM determination unit can also use its emotion estimation function to analyze the user's emotional state and determine the BPM according to the emotion. For example, if the user is relaxed, the BPM can be set lower, and if the user is excited, the BPM can be set higher. It is also possible to adjust the BPM in real time according to changes in emotion. Furthermore, emotion data can be stored in the cloud and long-term patterns of emotional fluctuations can be analyzed.

[0097] The music generation unit can also use the emotion estimation function to adjust the mood of the music according to the user's emotional state. For example, it can generate a bright song for positive emotions and a calm song for negative emotions. It can also adjust the mood of the music in real time according to changes in emotions. Furthermore, it can suggest music that suits the user's preferences based on the emotion data.

[0098] The tempo adjustment unit can also use the emotion estimation function to adjust the tempo according to the user's emotional state and respond to changes in emotion. For example, it can increase the tempo when the emotion is positive and decrease the tempo when the emotion is negative. It can also adjust the tempo in real time according to changes in emotion. Furthermore, it can suggest tempos that suit the user's preferences based on the emotion data.

[0099] The music player can also use its emotion estimation function to automatically adjust the playback order of songs according to the user's emotional state. For example, if the user is feeling positive, it will prioritize playing cheerful songs, and if the user is feeling negative, it will prioritize playing calm songs. It can also adjust the playback order in real time according to changes in emotions. Furthermore, it can suggest songs that suit the user's preferences based on the emotional data.

[0100] The user interface can also use emotion estimation to suggest an interface that matches the user's emotions when they feel a certain emotion. For example, if the user is relaxed, a calm interface can be suggested, and if the user is excited, an energetic interface can be suggested. It is also possible to change the interface design and color scheme in real time according to changes in emotions. Furthermore, it is possible to provide an interface that matches the user's preferences based on emotional data.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The step counting unit measures walking speed using the step counting function of the smartphone. For example, the step counting unit counts steps using an acceleration sensor built into the smartphone and measures walking speed. Step 2: The BPM determination unit determines the BPM based on the data measured by the step count unit. For example, if you walk 100 steps in one minute, the BPM determination unit determines the BPM to be 100. Step 3: The music generation unit generates music based on the BPM determined by the BPM determination unit. For example, the music generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate music that matches the BPM. Step 4: The tempo adjustment unit raises or lowers the BPM according to the user's preference. For example, if the user desires an up-tempo song, the tempo adjustment unit sets the BPM to 120 and generates music that matches that tempo.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a step counting unit that measures walking speed using the step counting function of a smartphone; a BPM determination unit that determines the BPM based on the data measured by the step count unit; a music generation unit that generates music based on the BPM determined by the BPM determination unit; a tempo adjustment unit that increases or decreases the BPM according to the user's desire A system characterized by:

2. The step counting unit In addition to counting steps, it analyzes the user's walking pattern to determine a more accurate BPM.

2. The system of claim 1.

3. The step counting unit Apply this to other exercises and determine the appropriate BPM for each exercise.

2. The system of claim 1.

4. The music generation unit Generate personalized music based on the user's past musical preferences 2. The system of claim 1.

5. The tempo adjustment unit Obtains the user's heart rate data in real time and automatically adjusts the tempo according to the heart rate.

2. The system of claim 1.

6. The music playback section is The system monitors the user's walking data in real time while the music is playing, and dynamically adjusts the tempo of the music according to the user's walking speed.

2. The system of claim 1.

7. The user interface is Dynamically change interface design and color scheme according to the user's emotional state using emotion estimation functions 2. The system of claim 1.

8. The BPM determination unit Using emotion estimation function, analyze the user's emotional state and determine the BPM according to the emotion.

2. The system of claim 1.

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