system

Abstract

An object of a system according to an embodiment is to generate optimal BGM on the basis of performance data of a user and support performance improvement.SOLUTION: A system includes a collection unit, a generation unit, and a reproduction unit. The collection unit collects performance data of a user. The generation unit generates BGM on the basis of the data collected by the collection unit. The reproduction unit reproduces the BGM generated by the generation unit in real time.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 technology leaves music selection up to the user while running, which has the drawback of not being able to improve performance.

[0005] The system according to the embodiment aims to generate optimal background music based on the performance data of a user and to support performance improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a playback unit. The collection unit collects user performance data. The generation unit generates background music based on the data collected by the collection unit. The playback unit plays the background music generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can generate optimal background music based on the performance data of a user, thereby supporting performance improvement. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A system according to an embodiment of the present invention uses a generation AI to generate background music in real time based on running performance data, thereby supporting a user in improving their performance. When a user starts running, this system collects performance data in real time, inputs the data into the generation AI, generates background music, and plays it in real time. This allows the user to enjoy running while listening to music that is optimal for their performance. For example, if the user wants to increase their pace, the system generates fast-tempo music, and if the user wants to relax, the system generates slower music. This allows the user to listen to music that matches their performance, maximizing the benefits of their running.

[0029] A performance improvement system according to an embodiment includes a collection unit, a generation unit, and a playback unit. The collection unit collects a user's performance data. For example, the collection unit can collect data such as distance, pace, runtime, calories, running route, attributes, time period, and past records. The collection unit can collect data through sensors or a smartphone application. The generation unit generates background music (BGM) based on the collected data using a generation AI. For example, the generation unit can generate fast-paced music if the user wants to increase their pace and slower music if the user wants to relax. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates optimal BGM according to the user's performance status. The playback unit plays the generated BGM in real time. For example, the playback unit can play the generated BGM in real time through the user's earphones or speakers. As a result, the performance improvement system according to an embodiment can support the user's performance improvement by generating and playing BGM in real time based on the user's performance data.

[0030] The collection unit can collect data such as distance, pace, runtime, calories, running route, attributes, time period, and past records. For example, the collection unit can measure distance using GPS. Pace can be measured by calculating distance per hour. Runtime can be measured by measuring the time from the start to the end of a run. Calories can be measured based on a calorie consumption calculation method. Running route can be collected using GPS data. Attributes can be collected, such as age, gender, and fitness level. Time period can be collected in 24-hour or 12-hour format. Past records can collect past running data. This allows for the collection of diverse performance data, enabling more accurate background music generation.

[0031] The generation unit can analyze the collected data and generate background music according to the user's performance status. The generation unit can, for example, analyze the collected data using a machine learning algorithm. The purpose of the analysis is to understand the user's performance status and generate optimal background music. For example, the generation unit can analyze data such as the user's heart rate, speed, and runtime, and generate background music that is optimal for the user's current status. This can support performance improvement by generating optimal background music according to the user's performance status.

[0032] The generation unit can generate music with a fast tempo when the user wants to increase their pace, and can generate slower music when the user wants to relax. For example, if the user's pace is fast, the generation unit can generate fast music with a high BPM (beats per minute). For example, music with a BPM of 120 or more can be generated. On the other hand, if the user wants to relax, the generation unit can generate slower music with a low BPM. For example, music with a BPM of 60 or less can be generated. This allows the user to maximize the benefits of running by generating background music according to their needs.

[0033] The playback unit can play the generated background music in real time through the user's earphones or speakers. The playback unit can play the generated background music in real time using, for example, Bluetooth (registered trademark) compatible earphones or speakers. The playback unit can transmit the generated background music to the user's device and play it in real time. This can support the user's performance improvement by playing the generated background music in real time.

[0034] The collection unit can collect data through sensors or smartphone applications. For example, the collection unit can collect data using a heart rate sensor or an acceleration sensor. The collection unit can collect GPS data and user input data through a smartphone application. This allows for more accurate performance data to be obtained by collecting data from a variety of devices.

[0035] The collection unit can analyze the user's past performance data and select the optimal data collection method. For example, the collection unit can identify time periods in which the user demonstrated high performance in the past and concentrate data collection on those time periods. The collection unit can also optimize the data collection method under specific conditions based on the user's past performance data. For example, the collection unit can adjust the frequency and timing of data collection based on the user's past performance data. In this way, the optimal data collection method can be selected by analyzing the past performance data.

[0036] When collecting data, the collection unit can adjust the type of data to be collected based on the user's current health condition and fatigue level. For example, if the user is tired, the collection unit can prioritize collecting data such as heart rate and respiratory rate. Furthermore, if the user's health condition is good, the collection unit can focus on collecting performance data such as distance and pace. Furthermore, if the user is feeling unwell, the collection unit can temporarily suspend data collection and prioritize the user's health condition. This allows more appropriate data to be collected by adjusting the type of data to be collected according to the user's health condition and fatigue level.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect data using voice recognition technology. If the user uses text input, the collection unit can collect data using text analysis technology. If the user uses gesture input, the collection unit can collect data using gesture recognition technology. This allows for more efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is running in a specific area, the collection unit can collect data based on the geographical information of the area. Furthermore, if the user is running in a tourist spot, the collection unit can prioritize collecting data related to tourist spots. Furthermore, if the user is running near their home, the collection unit can evaluate performance by comparing it with past data. This enables more accurate data collection by prioritized collection of highly relevant data based on the user's geographical location information.

[0039] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user posts about running on social media, the collection unit can collect data based on the content of the post. The collection unit can also collect information about events and groups related to running from the user's social media activities. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities.

[0040] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the frequency and timing of data collection based on the user's past feedback. The collection unit can also optimize a specific data collection method based on the user's past feedback. Furthermore, the collection unit can customize the data collection techniques and tools by reflecting the user's feedback. In this way, a more appropriate data collection method can be customized by reflecting the user's past feedback.

[0041] When generating background music, the generation unit can adjust the level of detail of the background music based on the importance of the user's performance data. For example, if the user has a fast pace, the generation unit can generate background music with a fast tempo. Furthermore, if the user's runtime is long, the generation unit can generate background music to maintain continuous motivation. Furthermore, if the user's calorie consumption is high, the generation unit can generate energetic background music. Thus, by adjusting the level of detail of the background music based on the importance of the user's performance data, more appropriate background music can be generated.

[0042] When generating background music, the generation unit can apply different generation algorithms depending on the user's performance category. For example, if the user is sprinting, the generation unit can apply an algorithm that generates high-energy background music in a short time. If the user is running long distances, the generation unit can apply an algorithm that generates background music to maintain sustained motivation. Furthermore, if the user is doing a recovery run, the generation unit can apply an algorithm that generates background music with a relaxing effect. In this way, by applying different generation algorithms depending on the user's performance category, more appropriate background music can be generated.

[0043] When generating background music, the generation unit can improve the accuracy of the generation by referring to the user's past background music generation results. For example, the generation unit can analyze the user's favorite background music patterns in the past and generate similar background music. The generation unit can also adjust the background music generation algorithm based on the user's past feedback. Furthermore, the generation unit can compare the user's past running data with the background music generation results and generate optimal background music. This allows the generation accuracy to be improved by referring to the user's past background music generation results.

[0044] When generating background music, the generation unit can determine the priority of background music based on the time of submission of the user's performance data. For example, the generation unit can preferentially generate background music that increases motivation immediately after the user starts running. The generation unit can also generate background music that maintains continuous motivation when the user is in the middle of a run. Furthermore, the generation unit can generate background music that increases a sense of accomplishment when the user is nearing the end of a run. In this way, by determining the priority of background music based on the time of submission of the user's performance data, more appropriate background music can be generated.

[0045] When generating background music, the generation unit can adjust the order of background music based on the relevance of the user's performance data. For example, if the user has a fast pace, the generation unit can prioritize playing background music with a fast tempo. Furthermore, if the user's runtime is long, the generation unit can sequentially play background music that helps maintain continuous motivation. Furthermore, if the user's calorie consumption is high, the generation unit can prioritize playing energetic background music. In this way, by adjusting the order of background music based on the relevance of the user's performance data, more appropriate background music can be generated.

[0046] When generating background music, the generation unit can adjust the use of technical terms in the background music according to the user's level of expertise. For example, if the user is a beginner, the generation unit can generate simple, easy-to-understand background music. If the user is an intermediate user, the generation unit can generate background music that includes slightly more technical terms. If the user is an advanced user, the generation unit can generate background music that uses a lot of technical terms. This allows for the generation of more appropriate background music by adjusting the use of technical terms in the background music according to the user's level of expertise.

[0047] When playing background music, the playback unit can select the optimal playback method by referring to the user's past playback history. For example, the playback unit can analyze the user's past favorite background music patterns and play similar background music. The playback unit can also adjust the background music playback algorithm based on the user's past feedback. Furthermore, the playback unit can compare the user's past running data with the background music playback results and play the optimal background music. This allows a more appropriate playback method to be selected by referring to the user's past playback history.

[0048] When playing background music, the playback unit can customize the playback content according to the user's current task. For example, if the user is sprinting, the playback unit can play fast-paced, energetic background music. If the user is doing a recovery run, the playback unit can play relaxing background music. Furthermore, if the user is doing long-distance running, the playback unit can play background music to maintain sustained motivation. In this way, by customizing the playback content according to the user's current task, more appropriate background music can be played.

[0049] The playback unit can improve the playback method by reflecting user feedback when playing background music. For example, the playback unit can adjust the frequency and timing of background music playback based on feedback provided by the user in the past. The playback unit can also optimize a specific background music playback method based on user feedback. Furthermore, the playback unit can customize background music playback techniques and tools by reflecting user feedback. This makes it possible to provide a more appropriate playback method by reflecting user feedback.

[0050] When playing background music, the playback unit can select the optimal playback method by taking into account the user's device information. For example, if the user is using a smartphone, the playback unit can provide a playback method that matches the screen size. Furthermore, if the user is using a tablet, the playback unit can provide a playback method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the playback unit can provide a simple and highly visible playback method. This allows for more appropriate background music to be played by selecting the optimal playback method based on the user's device information.

[0051] When playing background music, the playback unit can make the playback content multilingual according to the user's language setting. For example, the playback unit can automatically set the language of the background music based on the language setting of the user's device. The playback unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the playback unit can play background music in that language. This makes it possible to play more appropriate background music by making the playback content multilingual according to the user's language setting.

[0052] The playback unit can adjust the playback volume during background music playback, taking into account the user's environmental sounds. For example, the playback unit can set the volume lower when the user is in a quiet environment. Furthermore, the playback unit can set the volume higher when the user is in a noisy environment. Furthermore, when the user is running outdoors, the playback unit can automatically adjust the volume according to the surrounding environmental sounds. This allows for more appropriate background music to be played by adjusting the playback volume according to the user's environmental sounds.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can collect the user's dietary data in addition to the user's performance data. For example, the collection unit can collect data such as the user's calorie intake, nutrients, and meal timing. This allows the generation unit to generate appropriate background music taking the user's dietary data into consideration when energy replenishment is needed. Furthermore, the playback unit can play background music that promotes relaxation after meals based on the user's dietary data. This makes it possible to utilize the user's dietary data to support more personalized performance improvement.

[0055] The collection unit can collect the user's sleep data in addition to the user's performance data. For example, the collection unit can collect data such as the user's sleep time, sleep quality, and sleep cycle. This allows the generation unit to generate background music that promotes fatigue recovery by taking the user's sleep data into consideration. Furthermore, the playback unit can play background music that has a relaxing effect based on the user's sleep data. This makes it possible to utilize the user's sleep data to more effectively support performance improvement.

[0056] The generator can generate background music taking into account the user's weather data in addition to the user's performance data. For example, the generator can collect data such as the weather, temperature, and humidity of the area where the user runs and generate optimal background music based on this data. For example, the generator can generate background music that gives a feeling of coolness on a hot day and background music that gives a warm feeling on a cold day. This makes it possible to provide a more comfortable running experience by utilizing the user's weather data.

[0057] The generator can generate background music taking into account the user's heart rate variability data in addition to the user's performance data. For example, the generator can analyze the user's heart rate variability data to estimate the user's stress level and relaxation level. This allows the generator to generate background music with a relaxing effect when the user is feeling stressed, and generate energetic background music when the user is relaxed. This makes it possible to provide more appropriate background music by utilizing the user's heart rate variability data.

[0058] The playback unit can play background music taking into account the battery status of the user's device in addition to the user's performance data. For example, if the battery level of the user's device is low, the playback unit can play background music in low power consumption mode. If the battery level is sufficient, the playback unit can play background music in high quality. This makes it possible to provide an optimal playback method taking into account the battery status of the user's device.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects user performance data. For example, the collection unit can collect data such as distance, pace, run time, calories, running route, attributes, time period, and past records. The collection unit can collect data through sensors or smartphone applications. Step 2: The generator uses a generation AI to generate background music based on the collected data. For example, the generator can generate fast-paced music if the user wants to pick up the pace, or slower music if the user wants to relax. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates optimal background music according to the user's performance status. Step 3: The playback unit plays the generated background music in real time. For example, the playback unit can play the generated background music in real time through the user's earphones or speakers.

[0061] (Example 2) A system according to an embodiment of the present invention uses a generation AI to generate background music in real time based on running performance data, thereby supporting a user in improving their performance. When a user starts running, this system collects performance data in real time, inputs the data into the generation AI, generates background music, and plays it in real time. This allows the user to enjoy running while listening to music that is optimal for their performance. For example, if the user wants to increase their pace, the system generates fast-tempo music, and if the user wants to relax, the system generates slower music. This allows the user to listen to music that matches their performance, maximizing the benefits of their running.

[0062] A performance improvement system according to an embodiment includes a collection unit, a generation unit, and a playback unit. The collection unit collects a user's performance data. For example, the collection unit can collect data such as distance, pace, runtime, calories, running route, attributes, time period, and past records. The collection unit can collect data through sensors or a smartphone application. The generation unit generates background music (BGM) based on the collected data using a generation AI. For example, the generation unit can generate fast-paced music if the user wants to increase their pace and slower music if the user wants to relax. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates optimal BGM according to the user's performance status. The playback unit plays the generated BGM in real time. For example, the playback unit can play the generated BGM in real time through the user's earphones or speakers. As a result, the performance improvement system according to an embodiment can support the user's performance improvement by generating and playing BGM in real time based on the user's performance data.

[0063] The collection unit can collect data such as distance, pace, runtime, calories, running route, attributes, time period, and past records. For example, the collection unit can measure distance using GPS. Pace can be measured by calculating distance per hour. Runtime can be measured by measuring the time from the start to the end of a run. Calories can be measured based on a calorie consumption calculation method. Running route can be collected using GPS data. Attributes can be collected, such as age, gender, and fitness level. Time period can be collected in 24-hour or 12-hour format. Past records can collect past running data. This allows for the collection of diverse performance data, enabling more accurate background music generation.

[0064] The generation unit can analyze the collected data and generate background music according to the user's performance status. The generation unit can, for example, analyze the collected data using a machine learning algorithm. The purpose of the analysis is to understand the user's performance status and generate optimal background music. For example, the generation unit can analyze data such as the user's heart rate, speed, and runtime, and generate background music that is optimal for the user's current status. This can support performance improvement by generating optimal background music according to the user's performance status.

[0065] The generation unit can generate music with a fast tempo when the user wants to increase their pace, and can generate slower music when the user wants to relax. For example, if the user's pace is fast, the generation unit can generate fast music with a high BPM (beats per minute). For example, music with a BPM of 120 or more can be generated. On the other hand, if the user wants to relax, the generation unit can generate slower music with a low BPM. For example, music with a BPM of 60 or less can be generated. This allows the user to maximize the benefits of running by generating background music according to their needs.

[0066] The playback unit can play the generated background music in real time through the user's earphones or speakers. The playback unit can play the generated background music in real time using, for example, Bluetooth-enabled earphones or speakers. The playback unit can transmit the generated background music to the user's device and play it in real time. This can support the user's performance improvement by playing the generated background music in real time.

[0067] The collection unit can collect data through sensors or smartphone applications. For example, the collection unit can collect data using a heart rate sensor or an acceleration sensor. The collection unit can collect GPS data and user input data through a smartphone application. This allows for more accurate performance data to be obtained by collecting data from a variety of devices.

[0068] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice and calculate an emotion score. This allows for adjusting the timing of data collection according to the user's emotions, thereby collecting more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The collection unit can analyze the user's past performance data and select the optimal data collection method. For example, the collection unit can identify time periods in which the user demonstrated high performance in the past and concentrate data collection on those time periods. The collection unit can also optimize the data collection method under specific conditions based on the user's past performance data. For example, the collection unit can adjust the frequency and timing of data collection based on the user's past performance data. In this way, the optimal data collection method can be selected by analyzing the past performance data.

[0070] When collecting data, the collection unit can adjust the type of data to be collected based on the user's current health condition and fatigue level. For example, if the user is tired, the collection unit can prioritize collecting data such as heart rate and respiratory rate. Furthermore, if the user's health condition is good, the collection unit can focus on collecting performance data such as distance and pace. Furthermore, if the user is feeling unwell, the collection unit can temporarily suspend data collection and prioritize the user's health condition. This allows more appropriate data to be collected by adjusting the type of data to be collected according to the user's health condition and fatigue level.

[0071] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect data using voice recognition technology. If the user uses text input, the collection unit can collect data using text analysis technology. If the user uses gesture input, the collection unit can collect data using gesture recognition technology. This allows for more efficient data collection by selecting the optimal collection means depending on the user's input method.

[0072] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data such as heart rate and stress level. Furthermore, if the user is relaxed, the collection unit can prioritize collecting performance data such as distance and pace. Furthermore, if the user is excited, the collection unit can prioritize real-time data collection and immediately reflect that in background music generation. In this way, by determining the priority of data to be collected according to the user's emotions, more important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is running in a specific area, the collection unit can collect data based on the geographical information of the area. Furthermore, if the user is running in a tourist spot, the collection unit can prioritize collecting data related to tourist spots. Furthermore, if the user is running near their home, the collection unit can evaluate performance by comparing it with past data. This enables more accurate data collection by prioritized collection of highly relevant data based on the user's geographical location information.

[0074] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user posts about running on social media, the collection unit can collect data based on the content of the post. The collection unit can also collect information about events and groups related to running from the user's social media activities. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities.

[0075] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the frequency and timing of data collection based on the user's past feedback. The collection unit can also optimize a specific data collection method based on the user's past feedback. Furthermore, the collection unit can customize the data collection techniques and tools by reflecting the user's feedback. In this way, a more appropriate data collection method can be customized by reflecting the user's past feedback.

[0076] The generation unit can estimate the user's emotion and adjust the way the background music is expressed based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate background music with a slow tempo. If the user is excited, the generation unit can generate background music with a fast tempo and an energetic feel. If the user is stressed, the generation unit can generate background music with a relaxing effect. This allows the generation of more appropriate background music by adjusting the way the background music is expressed according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] When generating background music, the generation unit can adjust the level of detail of the background music based on the importance of the user's performance data. For example, if the user has a fast pace, the generation unit can generate background music with a fast tempo. Furthermore, if the user's runtime is long, the generation unit can generate background music to maintain continuous motivation. Furthermore, if the user's calorie consumption is high, the generation unit can generate energetic background music. Thus, by adjusting the level of detail of the background music based on the importance of the user's performance data, more appropriate background music can be generated.

[0078] When generating background music, the generation unit can apply different generation algorithms depending on the user's performance category. For example, if the user is sprinting, the generation unit can apply an algorithm that generates high-energy background music in a short time. If the user is running long distances, the generation unit can apply an algorithm that generates background music to maintain sustained motivation. Furthermore, if the user is doing a recovery run, the generation unit can apply an algorithm that generates background music with a relaxing effect. In this way, by applying different generation algorithms depending on the user's performance category, more appropriate background music can be generated.

[0079] When generating background music, the generation unit can improve the accuracy of the generation by referring to the user's past background music generation results. For example, the generation unit can analyze the user's favorite background music patterns in the past and generate similar background music. The generation unit can also adjust the background music generation algorithm based on the user's past feedback. Furthermore, the generation unit can compare the user's past running data with the background music generation results and generate optimal background music. This allows the generation accuracy to be improved by referring to the user's past background music generation results.

[0080] The generation unit can estimate the user's emotion and adjust the length of the background music based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate longer background music. If the user is in a hurry, the generation unit can generate shorter background music. If the user is excited, the generation unit can generate energetic background music in a short time. This allows for adjusting the length of the background music according to the user's emotion, thereby generating more appropriate background music. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] When generating background music, the generation unit can determine the priority of background music based on the time of submission of the user's performance data. For example, the generation unit can preferentially generate background music that increases motivation immediately after the user starts running. The generation unit can also generate background music that maintains continuous motivation when the user is in the middle of a run. Furthermore, the generation unit can generate background music that increases a sense of accomplishment when the user is nearing the end of a run. In this way, by determining the priority of background music based on the time of submission of the user's performance data, more appropriate background music can be generated.

[0082] When generating background music, the generation unit can adjust the order of background music based on the relevance of the user's performance data. For example, if the user has a fast pace, the generation unit can prioritize playing background music with a fast tempo. Furthermore, if the user's runtime is long, the generation unit can sequentially play background music that helps maintain continuous motivation. Furthermore, if the user's calorie consumption is high, the generation unit can prioritize playing energetic background music. In this way, by adjusting the order of background music based on the relevance of the user's performance data, more appropriate background music can be generated.

[0083] When generating background music, the generation unit can adjust the use of technical terms in the background music according to the user's level of expertise. For example, if the user is a beginner, the generation unit can generate simple, easy-to-understand background music. If the user is an intermediate user, the generation unit can generate background music that includes slightly more technical terms. If the user is an advanced user, the generation unit can generate background music that uses a lot of technical terms. This allows for the generation of more appropriate background music by adjusting the use of technical terms in the background music according to the user's level of expertise.

[0084] The playback unit can estimate the user's emotions and adjust the playback method of the background music based on the estimated user's emotions. For example, if the user is relaxed, the playback unit can play background music with a slow tempo. If the user is excited, the playback unit can play background music with a fast tempo and an energetic mood. If the user is stressed, the playback unit can play background music with a relaxing effect. In this way, by adjusting the playback method of the background music according to the user's emotions, more appropriate background music can be played. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] When playing background music, the playback unit can select the optimal playback method by referring to the user's past playback history. For example, the playback unit can analyze the user's past favorite background music patterns and play similar background music. The playback unit can also adjust the background music playback algorithm based on the user's past feedback. Furthermore, the playback unit can compare the user's past running data with the background music playback results and play the optimal background music. This allows a more appropriate playback method to be selected by referring to the user's past playback history.

[0086] When playing background music, the playback unit can customize the playback content according to the user's current task. For example, if the user is sprinting, the playback unit can play fast-paced, energetic background music. If the user is doing a recovery run, the playback unit can play relaxing background music. Furthermore, if the user is doing long-distance running, the playback unit can play background music to maintain sustained motivation. In this way, by customizing the playback content according to the user's current task, more appropriate background music can be played.

[0087] The playback unit can improve the playback method by reflecting user feedback when playing background music. For example, the playback unit can adjust the frequency and timing of background music playback based on feedback provided by the user in the past. The playback unit can also optimize a specific background music playback method based on user feedback. Furthermore, the playback unit can customize background music playback techniques and tools by reflecting user feedback. This makes it possible to provide a more appropriate playback method by reflecting user feedback.

[0088] The playback unit can estimate the user's emotions and adjust the playback order of background music based on the estimated user's emotions. For example, if the user is relaxed, the playback unit can prioritize playing background music with a slow tempo. Furthermore, if the user is excited, the playback unit can prioritize playing background music with a fast tempo and an energetic feel. Furthermore, if the user is stressed, the playback unit can prioritize playing background music with a relaxing effect. In this way, by adjusting the playback order of background music according to the user's emotions, more appropriate background music can be played. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When playing background music, the playback unit can select the optimal playback method by taking into account the user's device information. For example, if the user is using a smartphone, the playback unit can provide a playback method that matches the screen size. Furthermore, if the user is using a tablet, the playback unit can provide a playback method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the playback unit can provide a simple and highly visible playback method. This allows for more appropriate background music to be played by selecting the optimal playback method based on the user's device information.

[0090] When playing background music, the playback unit can make the playback content multilingual according to the user's language setting. For example, the playback unit can automatically set the language of the background music based on the language setting of the user's device. The playback unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the playback unit can play background music in that language. This makes it possible to play more appropriate background music by making the playback content multilingual according to the user's language setting.

[0091] The playback unit can adjust the playback volume during background music playback, taking into account the user's environmental sounds. For example, the playback unit can set the volume lower when the user is in a quiet environment. Furthermore, the playback unit can set the volume higher when the user is in a noisy environment. Furthermore, when the user is running outdoors, the playback unit can automatically adjust the volume according to the surrounding environmental sounds. This allows for more appropriate background music to be played by adjusting the playback volume according to the user's environmental sounds. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and playback unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user performance data through sensors and applications of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background music using a generation AI based on the collected data. The playback unit is realized by the control unit 46A of the smart device 14 and plays the generated background music in real time through the user's earphones or speakers. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and playback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user performance data through sensors and applications of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background music using a generation AI based on the collected data. The playback unit is realized by the control unit 46A of the smart glasses 214 and plays the generated background music in real time through the user's earphones or speakers. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and playback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user performance data through sensors and applications of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background music using a generation AI based on the collected data. The playback unit is realized by the control unit 46A of the headset type terminal 314 and plays the generated background music in real time through the user's earphones or speakers. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, and playback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user performance data through sensors and applications of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates background music using a generation AI based on the collected data. The playback unit is realized by the control unit 46A of the robot 414 and plays the generated background music in real time through the user's earphones or speakers.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit can collect the user's dietary data in addition to the user's performance data. For example, the collection unit can collect data such as the user's calorie intake, nutrients, and meal timing. This allows the generation unit to generate appropriate background music taking the user's dietary data into consideration when energy replenishment is needed. Furthermore, the playback unit can play background music that promotes relaxation after meals based on the user's dietary data. This makes it possible to utilize the user's dietary data to support more personalized performance improvement.

[0094] The collection unit can collect the user's sleep data in addition to the user's performance data. For example, the collection unit can collect data such as the user's sleep time, sleep quality, and sleep cycle. This allows the generation unit to generate background music that promotes fatigue recovery by taking the user's sleep data into consideration. Furthermore, the playback unit can play background music that has a relaxing effect based on the user's sleep data. This makes it possible to utilize the user's sleep data to more effectively support performance improvement.

[0095] The generator can generate background music taking into account the user's weather data in addition to the user's performance data. For example, the generator can collect data such as the weather, temperature, and humidity of the area where the user runs and generate optimal background music based on this data. For example, the generator can generate background music that gives a feeling of coolness on a hot day and background music that gives a warm feeling on a cold day. This makes it possible to provide a more comfortable running experience by utilizing the user's weather data.

[0096] The generator can generate background music taking into account the user's heart rate variability data in addition to the user's performance data. For example, the generator can analyze the user's heart rate variability data to estimate the user's stress level and relaxation level. This allows the generator to generate background music with a relaxing effect when the user is feeling stressed, and generate energetic background music when the user is relaxed. This makes it possible to provide more appropriate background music by utilizing the user's heart rate variability data.

[0097] The playback unit can play background music taking into account the battery status of the user's device in addition to the user's performance data. For example, if the battery level of the user's device is low, the playback unit can play background music in low power consumption mode. If the battery level is sufficient, the playback unit can play background music in high quality. This makes it possible to provide an optimal playback method taking into account the battery status of the user's device.

[0098] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced, and if the user is relaxed, the frequency of data collection can be increased. Also, if the user is excited, real-time data collection can be prioritized. In this way, by adjusting the frequency of data collection according to the user's emotions, more appropriate data can be collected.

[0099] The collection unit can estimate the user's emotion and customize the data collection method based on the estimated user's emotion. For example, if the user is relaxed, data can be collected using voice input, and if the user is stressed, data can be collected using gesture input. Also, if the user is excited, data can be collected using text input. This allows for more efficient data collection by customizing the data collection method according to the user's emotion.

[0100] The generation unit can estimate the user's emotions and select a genre of background music based on the estimated user emotions. For example, if the user is relaxed, classical music or ambient music can be generated, and if the user is excited, rock or electronic music can be generated. Also, if the user is feeling stressed, soothing music can be generated. In this way, by selecting a genre of background music according to the user's emotions, more appropriate background music can be provided.

[0101] The generation unit can estimate the user's emotion and adjust the volume of the background music based on the estimated user's emotion. For example, if the user is relaxed, the volume can be set low, and if the user is excited, the volume can be set high. Also, if the user is feeling stressed, the volume can be set to a medium level. In this way, by adjusting the volume of the background music according to the user's emotion, more appropriate background music can be provided.

[0102] The playback unit can estimate the user's emotions and adjust the timing of background music playback based on the estimated user emotions. For example, if the user is relaxed, slow background music can be played, and if the user is excited, fast-paced background music can be played. Also, if the user is feeling stressed, background music with a relaxing effect can be played. In this way, by adjusting the timing of background music playback according to the user's emotions, more appropriate background music can be provided.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects user performance data. For example, the collection unit can collect data such as distance, pace, run time, calories, running route, attributes, time period, and past records. The collection unit can collect data through sensors or smartphone applications. Step 2: The generator uses a generation AI to generate background music based on the collected data. For example, the generator can generate fast-paced music if the user wants to pick up the pace, or slower music if the user wants to relax. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates optimal background music according to the user's performance status. Step 3: The playback unit plays the generated background music in real time. For example, the playback unit can play the generated background music in real time through the user's earphones or speakers.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0133] 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.

[0134] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 collection unit that collects user performance data; a generation unit that generates background music based on the data collected by the collection unit; a playback unit that plays the background music generated by the generation unit in real time. A system characterized by:

2. The collecting unit Collects data on distance, pace, time, calories, route, attributes, time zone, and past records 2. The system of claim 1.

3. The generation unit Analyzes collected data and generates background music according to the user's performance.

2. The system of claim 1.

4. The generation unit Generate fast-paced music when the user wants to pick up the pace, or slower music when the user wants to relax 2. The system of claim 1.

5. The playback unit The generated background music is played in real time through the user's earphones or speakers.

2. The system of claim 1.

6. The collecting unit Collect data through sensors or smartphone applications 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past performance data and select the best data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, adjust the type of data collected based on the user's current health and fatigue level.

2. The system of claim 1.

10. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

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