system

Abstract

The system according to the embodiment aims to maximize performance by providing music that matches the content of a user's task. [Solution] A system according to an embodiment includes a receiving unit, a calculation unit, a generation unit, a provision unit, and a collection unit. The receiving unit inputs task content. The calculation unit calculates a recommended tempo based on the task content input by the receiving unit. The generation unit generates music based on the recommended tempo calculated by the calculation unit. The provision unit provides the music generated by the generation unit. The collection unit collects user feedback on the music provided by the provision unit.

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] With conventional technology, it was difficult to maintain an appropriate tempo during a task, making it difficult to maximize performance.

[0005] The system according to the embodiment aims to maximize performance by providing music that matches the content of a user's task. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, a calculation unit, a generation unit, a provision unit, and a collection unit. The receiving unit inputs task content. The calculation unit calculates a recommended tempo based on the task content input by the receiving unit. The generation unit generates music based on the recommended tempo calculated by the calculation unit. The provision unit provides the music generated by the generation unit. The collection unit collects user feedback on the music provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can maximize performance by providing music that matches the content of the user's task. [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 supports a user in maintaining an appropriate tempo for daily tasks. The system inputs the details of a task to be performed by the user, calculates a recommended tempo appropriate for the task, automatically generates music based on the recommended tempo, and provides the generated music to the user's device. For example, music with a fast beat is generated for exercise, music with a rhythm that helps concentration for study, and music with a relaxing tempo for housework. This allows the user to perform tasks while maintaining an appropriate tempo, thereby maximizing performance. The system thus provides optimal music according to the user's task, thereby maximizing the user's performance. For example, when a user exercises, music with a fast beat can be provided to enhance the effectiveness of the exercise. Furthermore, when a user studies, music with a rhythm that helps concentration can be provided to improve learning effectiveness. Furthermore, when performing housework, music with a relaxing tempo can be provided to improve work efficiency.

[0029] A task assistance system according to an embodiment includes a reception unit, a calculation unit, a generation unit, a provision unit, and a collection unit. The reception unit inputs the details of a task to be performed by a user. Examples of tasks include exercise, studying, and housework. The reception unit provides an interface through which the user inputs the task details in text format. The reception unit can also input the task details using voice input. For example, when a user vocally inputs "exercise," the reception unit converts the content into text. The reception unit can also estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface to minimize the input steps. The calculation unit calculates a recommended tempo based on the task details input by the reception unit. The recommended tempo is expressed in units such as beats per minute (BPM). For example, the calculation unit recommends a fast tempo for exercise, a medium tempo for studying, and a slow tempo for housework. The calculation unit can also estimate the user's emotions and adjust the calculation method for the recommended tempo based on the estimated emotions. For example, if the user is relaxed, the calculation unit recommends a slow tempo. The generation unit generates music based on the recommended tempo calculated by the calculation unit. The generation unit generates music using a generation AI. For example, the generation AI generates music with a fast beat for exercise, music with a rhythm that helps concentration for studying, and music with a relaxing tempo for housework. The generation unit can also estimate the user's emotions and adjust the music generation method based on the estimated emotions. For example, if the user is relaxing, it generates music with a slow tempo. The provision unit provides the music generated by the generation unit to the user's device. The provision unit provides the music in streaming format, for example. The provision unit can also estimate the user's emotions and adjust the music provision method based on the estimated emotions. For example, if the user is relaxing, it provides slow music. The collection unit collects user feedback on the music provided by the provision unit. For example, the collection unit provides an interface where the user inputs an evaluation score for the music.The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback is requested. This allows the task assistance system according to the embodiment to provide optimal music according to the content of the user's task and maximize the user's performance.

[0030] The collection unit can collect user feedback and provide it to the generation unit to improve the algorithm. The collection unit, for example, provides an interface where the user inputs an evaluation score for music. For example, if the user provides feedback such as "this music was perfect for exercise," the collection unit provides that feedback to the generation unit. The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback is requested. This allows the accuracy of music generation to be improved by reflecting the user's feedback. The algorithm can be improved by, for example, retraining the machine learning model or adjusting parameters.

[0031] The reception unit can analyze the user's past task history and suggest the optimal input method. For example, the reception unit can automatically display task contents that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest tasks to be performed in a specific time period based on the user's past task history. This makes it possible to provide the optimal input method based on the past task history. The past task history includes, for example, the type of task, execution time, completion status, etc.

[0032] When the task content is input, the reception unit can filter the input based on the user's current situation and environment. For example, if the user is out, the reception unit can prioritize displaying tasks that can be performed while away from home. Also, if the user is at home, the reception unit can prioritize displaying tasks that can be performed at home. Furthermore, if the user is in a specific location, the reception unit can suggest tasks related to that location. This makes it possible to provide task content that is appropriate for the user's situation and environment. The current situation and environment include, for example, location information, ambient noise level, device status, etc.

[0033] When inputting task content, the reception unit can select the optimal input means depending on the user's input method. For example, when the user inputs task content by voice, the reception unit converts it into text using voice recognition technology. In addition, when the user inputs task content as text, the reception unit can also provide an input completion function. Furthermore, when the user inputs task content as an image, the reception unit can also analyze the task content using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method. Input methods include, for example, voice input, text input, and gesture input.

[0034] When inputting task content, the reception unit can prioritize inputting highly relevant tasks taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize displaying tasks that can be performed at that location. Furthermore, when the user is traveling, the reception unit can prioritize displaying tasks that can be performed while traveling. Furthermore, when the user is at home, the reception unit can prioritize displaying tasks that can be performed at home. This makes it possible to provide task content based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, etc.

[0035] When inputting task content, the reception unit can analyze the user's social media activity and input related tasks. For example, the reception unit automatically inputs tasks shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and suggest related tasks. Furthermore, the reception unit can also suggest related tasks by referring to the activity of the user's friends on social media. This makes it possible to provide task content based on the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc.

[0036] The reception unit can customize the input method by reflecting the user's past feedback when inputting task content. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. This makes it possible to provide an input method based on the user's past feedback. Past feedback includes, for example, the user's ratings, comments, usage history, etc.

[0037] When calculating the recommended tempo, the calculation unit can adjust the level of detail of the calculation based on the importance of the task. For example, the calculation unit performs a detailed tempo calculation for an important task. The calculation unit can also perform a simplified tempo calculation for a less important task. Furthermore, the calculation unit can adjust the accuracy of the calculation according to the importance of the task. This makes it possible to provide a recommended tempo according to the importance of the task. The importance of a task is evaluated based on, for example, the task's deadline, scope of impact, user priority, etc.

[0038] When calculating the recommended tempo, the calculation unit can apply different calculation algorithms depending on the task category. For example, the calculation unit can apply an algorithm that recommends a fast tempo for an exercise task. The calculation unit can also apply an algorithm that recommends a medium tempo for a study task. Furthermore, the calculation unit can also apply an algorithm that recommends a slow tempo for a housework task. In this way, it is possible to provide a recommended tempo according to the task category. Task categories are classified based on, for example, work, study, hobbies, etc.

[0039] When calculating the recommended tempo, the calculation unit can improve the accuracy of the calculation by referring to the user's past calculation results. For example, the calculation unit calculates the recommended tempo by referring to tempos that the user has previously preferred. The calculation unit can also improve the calculation algorithm based on the user's past calculation results. Furthermore, the calculation unit can improve the accuracy of the calculation of the recommended tempo by reflecting the user's past feedback. This makes it possible to provide a recommended tempo based on the user's past calculation results. Past calculation results include, for example, a history of recommended tempos, user feedback, etc.

[0040] When calculating the recommended tempo, the calculation unit can determine the priority of the calculation based on the execution time of the task. For example, for a task to be executed soon, the calculation unit can calculate the tempo as a priority. Also, for a task to be executed over a long period of time, the calculation unit can postpone the calculation of the tempo. Furthermore, the calculation unit can adjust the priority of the calculation based on the execution time of the task. This makes it possible to provide a recommended tempo according to the execution time of the task. The execution time of the task is determined based on, for example, calendar information, the user's schedule, etc.

[0041] When calculating the recommended tempo, the calculation unit can adjust the order of calculation based on the relevance of the tasks. For example, the calculation unit can prioritize calculating the tempo for a highly relevant task. Also, the calculation unit can postpone calculating the tempo for a less relevant task. Furthermore, the calculation unit can adjust the order of calculation based on the relevance of the tasks. This makes it possible to provide a recommended tempo based on the relevance of the tasks. The relevance of the tasks is evaluated based on, for example, task dependency, a common goal, etc.

[0042] When calculating the recommended tempo, the calculation unit may adjust the use of technical terms in the calculation according to the user's level of expertise. For example, the calculation unit may calculate the tempo using detailed technical terms for a user with high expertise. Alternatively, the calculation unit may calculate the tempo using simple terms for a user with low expertise. Furthermore, the calculation unit may adjust the use of technical terms in the calculation according to the user's level of expertise. This makes it possible to provide a recommended tempo according to the user's level of expertise. The level of expertise may be evaluated based on, for example, qualifications, past experience, self-assessment, etc.

[0043] When generating music, the generator can adjust the level of detail of the generation based on the importance of the recommended tempo. For example, the generator generates detailed music for an important task. The generator can also generate simplified music for a less important task. Furthermore, the generator can adjust the level of detail of the generation according to the importance of the recommended tempo. This makes it possible to provide music according to the importance of the recommended tempo. The level of detail of the generation is adjusted based on, for example, the complexity of the music, the selection of timbres, the use of effects, etc.

[0044] When generating music, the generation unit can apply different generation algorithms depending on the task category. For example, for an exercise task, the generation unit applies an algorithm that generates music with a fast tempo. For a study task, the generation unit can also apply an algorithm that generates music with a medium tempo. Furthermore, for a housework task, the generation unit can also apply an algorithm that generates music with a slow tempo. This makes it possible to provide music according to the task category. The generation algorithm can be realized using technologies such as deep learning, rule-based generation, and evolutionary algorithms.

[0045] When generating music, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. The generation unit generates music by referring to, for example, music that the user has liked in the past. The generation unit can also improve the generation algorithm based on the user's past generation results. Furthermore, the generation unit can also improve the accuracy of the generation by reflecting the user's past feedback. This makes it possible to provide music based on the user's past generation results. Past generation results include, for example, a history of generated music, user feedback, etc.

[0046] When generating music, the generation unit can determine the generation priority based on the execution time of the task. For example, for a task to be executed soon, the generation unit generates music with priority. Also, for a task to be executed over a long period of time, the generation unit can postpone the generation of music. Furthermore, the generation unit can adjust the generation priority according to the execution time of the task. This makes it possible to provide music according to the execution time of the task. The generation priority is determined based on, for example, the deadline, importance, user preference, etc. of the task.

[0047] When generating music, the generation unit can adjust the order of generation based on the relevance of the tasks. For example, the generation unit can generate music preferentially for highly related tasks. Also, the generation unit can postpone the generation of music for less related tasks. Furthermore, the generation unit can adjust the order of generation based on the relevance of the tasks. This makes it possible to provide music that matches the relevance of the tasks. The order of generation is adjusted based on, for example, task dependencies, common goals, etc.

[0048] When generating music, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, for a user with high expertise, the generation unit generates music using detailed technical terminology. For a user with low expertise, the generation unit can also generate music using simple terminology. Furthermore, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. This makes it possible to provide music that suits the user's level of expertise. The use of technical terminology is adjusted based on, for example, the user's level of understanding, level of expertise, etc.

[0049] When providing music, the providing unit can select the optimal providing method by referring to the user's past providing history. For example, the providing unit can provide music that the user has previously preferred preferentially. The providing unit can also improve the providing algorithm based on the user's past providing history. Furthermore, the providing unit can also optimize the providing method by reflecting the user's past feedback. This makes it possible to provide a music providing method based on the user's past providing history. The providing history includes, for example, a history of music that has been provided in the past, user feedback, etc.

[0050] When providing music, the providing unit can customize the content of the music provided depending on the user's current task. For example, if the user is exercising, the providing unit can provide music with a fast tempo. If the user is studying, the providing unit can also provide music with a medium tempo. Furthermore, if the user is doing housework, the providing unit can also provide music with a slow tempo. In this way, music can be provided according to the user's current task. The current task includes, for example, the type of task, progress, priority, etc.

[0051] The providing unit can improve the providing method by reflecting user feedback when providing music. For example, the providing unit improves the providing method based on user feedback about music provided in the past. The providing unit can also improve the providing algorithm by reflecting user feedback. Furthermore, the providing unit can customize the content to be provided based on user feedback. This makes it possible to provide a music providing method based on user feedback. The improvement of the providing method is performed by, for example, adjustments based on user feedback, technical updates, etc.

[0052] When providing music, the providing unit can select the optimal delivery method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is simple and highly visible. This makes it possible to provide a music delivery method based on the user's device information. The device information includes, for example, the device type, OS, hardware specifications, etc.

[0053] When providing music, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit automatically sets the provided content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can also provide the provided content in that language. This makes it possible to provide music provided based on the user's language setting. The language setting includes, for example, the user's device setting, application setting, etc.

[0054] When providing music, the providing unit can customize the provided content by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit can provide music related to that location. Furthermore, if the user is on the move, the providing unit can also provide music suitable for the user while on the move. Furthermore, if the user is at home, the providing unit can also provide music that is relaxing at home. In this way, music can be provided based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, etc.

[0055] When collecting feedback, the collection unit can select an optimal collection method by referring to the user's past feedback history. The collection unit can improve the collection method, for example, based on feedback provided by the user in the past. The collection unit can also improve the collection algorithm based on the user's past feedback history. Furthermore, the collection unit can customize the collection content by reflecting the user's past feedback. This makes it possible to provide a feedback collection method based on the user's past feedback history. The feedback history includes, for example, the content of past feedback, evaluation scores, comments, etc.

[0056] When collecting feedback, the collection unit can customize the collected content according to the user's current task. For example, if the user is exercising, the collection unit can request feedback on the exercise. If the user is studying, the collection unit can also request feedback on the studying. Furthermore, if the user is doing housework, the collection unit can also request feedback on the housework. This makes it possible to provide collected feedback according to the user's current task. The current task includes, for example, the type of task, progress, priority, etc.

[0057] When collecting feedback, the collection unit can improve the collection method by reflecting the user's feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also improve the collection algorithm by reflecting the user's feedback. Furthermore, the collection unit can customize the collection content based on the user's feedback. This makes it possible to provide a feedback collection method based on the user's feedback. The improvement of the collection method is performed by, for example, adjustments based on the user's feedback, technical updates, etc.

[0058] When collecting feedback, the collection unit can select the optimal collection method taking into account the user's device information. For example, if the user is using a smartphone, the collection unit can provide a collection method that matches the screen size. Furthermore, if the user is using a tablet, the collection unit can also provide a collection method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the collection unit can also provide a simple and highly visible collection method. This makes it possible to provide a feedback collection method based on the user's device information. The device information includes, for example, the device type, OS, hardware specifications, etc.

[0059] When collecting feedback, the collection unit can make the collected content multilingual in accordance with the user's language setting. The collection unit automatically sets the collected content based on, for example, the language setting of the user's device. The collection unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the collection unit can provide the collected content in that language. This makes it possible to provide the collected feedback content based on the user's language setting. The language setting includes, for example, the user's device setting, application setting, etc.

[0060] When collecting feedback, the collection unit can customize the collected content by taking into account the user's geographical location information. For example, if the user is in a specific location, the collection unit can request feedback related to the location. Furthermore, if the user is on the move, the collection unit can request feedback suitable for the user on the move. Furthermore, if the user is at home, the collection unit can request feedback that will help the user relax at home. This makes it possible to provide collected feedback based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, etc.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The reception unit can analyze the user's past task history and suggest the optimal input method. For example, task contents that the user has frequently input in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest tasks to be performed in a specific time period based on the user's past task history. This makes it possible to provide the optimal input method based on the past task history. The past task history includes, for example, the type of task, execution time, completion status, etc.

[0063] The collection unit can collect user feedback and provide it to the generation unit to improve the algorithm. For example, an interface is provided where a user can input an evaluation score for music. For example, if a user provides feedback such as "this music was perfect for exercise," the collection unit provides that feedback to the generation unit. The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback is requested. This allows the accuracy of music generation to be improved by reflecting the user's feedback. The algorithm can be improved by, for example, retraining the machine learning model or adjusting parameters.

[0064] When the task content is input, the reception unit can filter the input based on the user's current situation and environment. For example, if the user is out, the reception unit can prioritize displaying tasks that can be performed while away from home. Also, if the user is at home, the reception unit can prioritize displaying tasks that can be performed at home. Furthermore, if the user is in a specific location, the reception unit can suggest tasks related to that location. This makes it possible to provide task content that is appropriate for the user's situation and environment. The current situation and environment include, for example, location information, ambient noise level, device status, etc.

[0065] When inputting task content, the reception unit can select the optimal input means depending on the user's input method. For example, when the user inputs task content by voice, the reception unit converts it into text using voice recognition technology. In addition, when the user inputs task content as text, the reception unit can also provide an input completion function. Furthermore, when the user inputs task content as an image, the reception unit can also analyze the task content using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method. Input methods include, for example, voice input, text input, and gesture input.

[0066] When calculating the recommended tempo, the calculation unit can adjust the level of detail of the calculation based on the importance of the task. For example, for an important task, a detailed tempo calculation is performed. The calculation unit can also perform a simplified tempo calculation for a less important task. Furthermore, the calculation unit can adjust the accuracy of the calculation according to the importance of the task. This makes it possible to provide a recommended tempo according to the importance of the task. The importance of a task is evaluated based on, for example, the task's deadline, scope of impact, user priority, etc.

[0067] When calculating the recommended tempo, the calculation unit can apply different calculation algorithms depending on the task category. For example, for an exercise task, an algorithm that recommends a fast tempo is applied. For a study task, the calculation unit can also apply an algorithm that recommends a medium tempo. Furthermore, for a housework task, the calculation unit can also apply an algorithm that recommends a slow tempo. In this way, it is possible to provide a recommended tempo according to the task category. Task categories are classified based on, for example, work, study, hobbies, etc.

[0068] When calculating the recommended tempo, the calculation unit can improve the accuracy of the calculation by referring to the user's past calculation results. For example, the calculation unit can calculate the recommended tempo by referring to tempos that the user has previously preferred. The calculation unit can also improve the calculation algorithm based on the user's past calculation results. Furthermore, the calculation unit can improve the accuracy of the calculation of the recommended tempo by reflecting the user's past feedback. This makes it possible to provide a recommended tempo based on the user's past calculation results. Past calculation results include, for example, the history of recommended tempos, user feedback, etc.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit inputs the details of the task to be performed by the user. For example, tasks such as exercise, studying, and housework can be considered. The reception unit provides an interface through which the user can input the task details in text format. The task details can also be input using voice input. Furthermore, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions. Step 2: The calculation unit calculates the recommended tempo based on the task content input by the reception unit. The recommended tempo is expressed in units such as BPM (beats per minute). The calculation unit recommends an appropriate tempo depending on the type of task. It can also estimate the user's emotions and adjust the calculation method for the recommended tempo based on the estimated emotions. Step 3: The generator generates music based on the recommended tempo calculated by the calculator. The generator uses the generation AI to generate music according to the type of task and the user's emotions. Step 4: The providing unit provides the music generated by the generating unit to the user's device. The providing unit provides the music in a streaming format and can adjust the providing method based on the user's emotions. Step 5: The collection unit collects user feedback on the music provided by the provision unit. The collection unit provides an interface for users to input evaluation scores for the music, and can also adjust the method of collecting feedback based on users' emotions.

[0071] (Example 2) A system according to an embodiment of the present invention supports a user in maintaining an appropriate tempo for daily tasks. The system inputs the details of a task to be performed by the user, calculates a recommended tempo appropriate for the task, automatically generates music based on the recommended tempo, and provides the generated music to the user's device. For example, music with a fast beat is generated for exercise, music with a rhythm that helps concentration for study, and music with a relaxing tempo for housework. This allows the user to perform tasks while maintaining an appropriate tempo, thereby maximizing performance. The system thus provides optimal music according to the user's task, thereby maximizing the user's performance. For example, when a user exercises, music with a fast beat can be provided to enhance the effectiveness of the exercise. Furthermore, when a user studies, music with a rhythm that helps concentration can be provided to improve learning effectiveness. Furthermore, when performing housework, music with a relaxing tempo can be provided to improve work efficiency.

[0072] A task assistance system according to an embodiment includes a reception unit, a calculation unit, a generation unit, a provision unit, and a collection unit. The reception unit inputs the details of a task to be performed by a user. Examples of tasks include exercise, studying, and housework. The reception unit provides an interface through which the user inputs the task details in text format. The reception unit can also input the task details using voice input. For example, when a user vocally inputs "exercise," the reception unit converts the content into text. The reception unit can also estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface to minimize the input steps. The calculation unit calculates a recommended tempo based on the task details input by the reception unit. The recommended tempo is expressed in units such as beats per minute (BPM). For example, the calculation unit recommends a fast tempo for exercise, a medium tempo for studying, and a slow tempo for housework. The calculation unit can also estimate the user's emotions and adjust the calculation method for the recommended tempo based on the estimated emotions. For example, if the user is relaxed, the calculation unit recommends a slow tempo. The generation unit generates music based on the recommended tempo calculated by the calculation unit. The generation unit generates music using a generation AI. For example, the generation AI generates music with a fast beat for exercise, music with a rhythm that helps concentration for studying, and music with a relaxing tempo for housework. The generation unit can also estimate the user's emotions and adjust the music generation method based on the estimated emotions. For example, if the user is relaxing, it generates music with a slow tempo. The provision unit provides the music generated by the generation unit to the user's device. The provision unit provides the music in streaming format, for example. The provision unit can also estimate the user's emotions and adjust the music provision method based on the estimated emotions. For example, if the user is relaxing, it provides slow music. The collection unit collects user feedback on the music provided by the provision unit. For example, the collection unit provides an interface where the user inputs an evaluation score for the music.The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback is requested. This allows the task assistance system according to the embodiment to provide optimal music according to the content of the user's task and maximize the user's performance.

[0073] The collection unit can collect user feedback and provide it to the generation unit to improve the algorithm. The collection unit, for example, provides an interface where the user inputs an evaluation score for music. For example, if the user provides feedback such as "this music was perfect for exercise," the collection unit provides that feedback to the generation unit. The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback is requested. This allows the accuracy of music generation to be improved by reflecting the user's feedback. The algorithm can be improved by, for example, retraining the machine learning model or adjusting parameters.

[0074] The reception unit can estimate the user's emotions and adjust the task content input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of task content. This makes it possible to provide the optimal input method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0075] The reception unit can analyze the user's past task history and suggest the optimal input method. For example, the reception unit can automatically display task contents that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest tasks to be performed in a specific time period based on the user's past task history. This makes it possible to provide the optimal input method based on the past task history. The past task history includes, for example, the type of task, execution time, completion status, etc.

[0076] When the task content is input, the reception unit can filter the input based on the user's current situation and environment. For example, if the user is out, the reception unit can prioritize displaying tasks that can be performed while away from home. Also, if the user is at home, the reception unit can prioritize displaying tasks that can be performed at home. Furthermore, if the user is in a specific location, the reception unit can suggest tasks related to that location. This makes it possible to provide task content that is appropriate for the user's situation and environment. The current situation and environment include, for example, location information, ambient noise level, device status, etc.

[0077] When inputting task content, the reception unit can select the optimal input means depending on the user's input method. For example, when the user inputs task content by voice, the reception unit converts it into text using voice recognition technology. In addition, when the user inputs task content as text, the reception unit can also provide an input completion function. Furthermore, when the user inputs task content as an image, the reception unit can also analyze the task content using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method. Input methods include, for example, voice input, text input, and gesture input.

[0078] The reception unit can estimate the user's emotions and determine the priority of task contents to be input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can prioritize displaying tasks that allow the user to relax. Furthermore, if the user is relaxed, the reception unit can also prioritize displaying tasks that require concentration. Furthermore, if the user is in a hurry, the reception unit can also prioritize displaying tasks that can be completed quickly. This makes it possible to provide a priority order of task contents according to the user's emotions. The priority order of task contents is determined based on, for example, urgency, importance, user preferences, etc.

[0079] When inputting task content, the reception unit can prioritize inputting highly relevant tasks taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize displaying tasks that can be performed at that location. Furthermore, when the user is traveling, the reception unit can prioritize displaying tasks that can be performed while traveling. Furthermore, when the user is at home, the reception unit can prioritize displaying tasks that can be performed at home. This makes it possible to provide task content based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, etc.

[0080] When inputting task content, the reception unit can analyze the user's social media activity and input related tasks. For example, the reception unit automatically inputs tasks shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and suggest related tasks. Furthermore, the reception unit can also suggest related tasks by referring to the activity of the user's friends on social media. This makes it possible to provide task content based on the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when inputting task content. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. This makes it possible to provide an input method based on the user's past feedback. Past feedback includes, for example, the user's ratings, comments, usage history, etc.

[0082] The calculation unit can estimate the user's emotions and adjust the calculation method for the recommended tempo based on the estimated emotions. For example, if the user is relaxed, the calculation unit can recommend a slow tempo. If the user is in a hurry, the calculation unit can also recommend a fast tempo. Furthermore, if the user is concentrating, the calculation unit can also recommend a medium tempo. This makes it possible to provide a recommended tempo according to the user's emotions. The calculation method for the recommended tempo can be adjusted based on, for example, the user's heart rate, the type of task, past data, etc.

[0083] When calculating the recommended tempo, the calculation unit can adjust the level of detail of the calculation based on the importance of the task. For example, the calculation unit performs a detailed tempo calculation for an important task. The calculation unit can also perform a simplified tempo calculation for a less important task. Furthermore, the calculation unit can adjust the accuracy of the calculation according to the importance of the task. This makes it possible to provide a recommended tempo according to the importance of the task. The importance of a task is evaluated based on, for example, the task's deadline, scope of impact, user priority, etc.

[0084] When calculating the recommended tempo, the calculation unit can apply different calculation algorithms depending on the task category. For example, the calculation unit can apply an algorithm that recommends a fast tempo for an exercise task. The calculation unit can also apply an algorithm that recommends a medium tempo for a study task. Furthermore, the calculation unit can also apply an algorithm that recommends a slow tempo for a housework task. In this way, it is possible to provide a recommended tempo according to the task category. Task categories are classified based on, for example, work, study, hobbies, etc.

[0085] When calculating the recommended tempo, the calculation unit can improve the accuracy of the calculation by referring to the user's past calculation results. For example, the calculation unit calculates the recommended tempo by referring to tempos that the user has previously preferred. The calculation unit can also improve the calculation algorithm based on the user's past calculation results. Furthermore, the calculation unit can improve the accuracy of the calculation of the recommended tempo by reflecting the user's past feedback. This makes it possible to provide a recommended tempo based on the user's past calculation results. Past calculation results include, for example, a history of recommended tempos, user feedback, etc.

[0086] The calculation unit can estimate the user's emotion and adjust the recommended tempo length based on the estimated emotion. For example, the calculation unit can recommend a longer tempo if the user is relaxed. The calculation unit can also recommend a shorter tempo if the user is in a hurry. Furthermore, the calculation unit can also recommend a medium-length tempo if the user is concentrating. This makes it possible to provide a recommended tempo length according to the user's emotion. The recommended tempo length is adjusted based on, for example, the time required for the task, the user's level of concentration, etc.

[0087] When calculating the recommended tempo, the calculation unit can determine the priority of the calculation based on the execution time of the task. For example, for a task to be executed soon, the calculation unit can calculate the tempo as a priority. Also, for a task to be executed over a long period of time, the calculation unit can postpone the calculation of the tempo. Furthermore, the calculation unit can adjust the priority of the calculation based on the execution time of the task. This makes it possible to provide a recommended tempo according to the execution time of the task. The execution time of the task is determined based on, for example, calendar information, the user's schedule, etc.

[0088] When calculating the recommended tempo, the calculation unit can adjust the order of calculation based on the relevance of the tasks. For example, the calculation unit can prioritize calculating the tempo for a highly relevant task. Also, the calculation unit can postpone calculating the tempo for a less relevant task. Furthermore, the calculation unit can adjust the order of calculation based on the relevance of the tasks. This makes it possible to provide a recommended tempo based on the relevance of the tasks. The relevance of the tasks is evaluated based on, for example, task dependency, a common goal, etc.

[0089] When calculating the recommended tempo, the calculation unit may adjust the use of technical terms in the calculation according to the user's level of expertise. For example, the calculation unit may calculate the tempo using detailed technical terms for a user with high expertise. Alternatively, the calculation unit may calculate the tempo using simple terms for a user with low expertise. Furthermore, the calculation unit may adjust the use of technical terms in the calculation according to the user's level of expertise. This makes it possible to provide a recommended tempo according to the user's level of expertise. The level of expertise may be evaluated based on, for example, qualifications, past experience, self-assessment, etc.

[0090] The generation unit can estimate the user's emotion and adjust the music generation method based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate music with a slow tempo. If the user is in a hurry, the generation unit can also generate music with a fast tempo. Furthermore, if the user is concentrating, the generation unit can also generate music with a medium tempo. This makes it possible to provide music that corresponds to the user's emotion. The music generation method is adjusted based on, for example, music theory, machine learning models, templates, etc.

[0091] When generating music, the generator can adjust the level of detail of the generation based on the importance of the recommended tempo. For example, the generator generates detailed music for an important task. The generator can also generate simplified music for a less important task. Furthermore, the generator can adjust the level of detail of the generation according to the importance of the recommended tempo. This makes it possible to provide music according to the importance of the recommended tempo. The level of detail of the generation is adjusted based on, for example, the complexity of the music, the selection of timbres, the use of effects, etc.

[0092] When generating music, the generation unit can apply different generation algorithms depending on the task category. For example, for an exercise task, the generation unit applies an algorithm that generates music with a fast tempo. For a study task, the generation unit can also apply an algorithm that generates music with a medium tempo. Furthermore, for a housework task, the generation unit can also apply an algorithm that generates music with a slow tempo. This makes it possible to provide music according to the task category. The generation algorithm can be realized using technologies such as deep learning, rule-based generation, and evolutionary algorithms.

[0093] When generating music, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. The generation unit generates music by referring to, for example, music that the user has liked in the past. The generation unit can also improve the generation algorithm based on the user's past generation results. Furthermore, the generation unit can also improve the accuracy of the generation by reflecting the user's past feedback. This makes it possible to provide music based on the user's past generation results. Past generation results include, for example, a history of generated music, user feedback, etc.

[0094] The generation unit can estimate the user's emotion and adjust the length of the music to be generated based on the estimated emotion. For example, the generation unit can generate longer music when the user is relaxed. The generation unit can also generate shorter music when the user is in a hurry. Furthermore, the generation unit can also generate medium-length music when the user is concentrating. This makes it possible to provide music of a length that corresponds to the user's emotion. The length of the music is adjusted based on, for example, the time required for the task, the user's level of concentration, etc.

[0095] When generating music, the generation unit can determine the generation priority based on the execution time of the task. For example, for a task to be executed soon, the generation unit generates music with priority. Also, for a task to be executed over a long period of time, the generation unit can postpone the generation of music. Furthermore, the generation unit can adjust the generation priority according to the execution time of the task. This makes it possible to provide music according to the execution time of the task. The generation priority is determined based on, for example, the deadline, importance, user preference, etc. of the task.

[0096] When generating music, the generation unit can adjust the order of generation based on the relevance of the tasks. For example, the generation unit can generate music preferentially for highly related tasks. Also, the generation unit can postpone the generation of music for less related tasks. Furthermore, the generation unit can adjust the order of generation based on the relevance of the tasks. This makes it possible to provide music that matches the relevance of the tasks. The order of generation is adjusted based on, for example, task dependencies, common goals, etc.

[0097] When generating music, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, for a user with high expertise, the generation unit generates music using detailed technical terminology. For a user with low expertise, the generation unit can also generate music using simple terminology. Furthermore, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. This makes it possible to provide music that suits the user's level of expertise. The use of technical terminology is adjusted based on, for example, the user's level of understanding, level of expertise, etc.

[0098] The providing unit can estimate the user's emotion and adjust the music providing method based on the estimated emotion. For example, if the user is relaxed, the providing unit can provide slow music. If the user is in a hurry, the providing unit can also provide music with a fast tempo. Furthermore, if the user is concentrating, the providing unit can also provide music with a medium tempo. This makes it possible to provide a music providing method that corresponds to the user's emotion. The music providing method is adjusted based on, for example, streaming, downloading, real-time generation, etc.

[0099] When providing music, the providing unit can select the optimal providing method by referring to the user's past providing history. For example, the providing unit can provide music that the user has previously preferred preferentially. The providing unit can also improve the providing algorithm based on the user's past providing history. Furthermore, the providing unit can also optimize the providing method by reflecting the user's past feedback. This makes it possible to provide a music providing method based on the user's past providing history. The providing history includes, for example, a history of music that has been provided in the past, user feedback, etc.

[0100] When providing music, the providing unit can customize the content of the music provided depending on the user's current task. For example, if the user is exercising, the providing unit can provide music with a fast tempo. If the user is studying, the providing unit can also provide music with a medium tempo. Furthermore, if the user is doing housework, the providing unit can also provide music with a slow tempo. In this way, music can be provided according to the user's current task. The current task includes, for example, the type of task, progress, priority, etc.

[0101] The providing unit can improve the providing method by reflecting user feedback when providing music. For example, the providing unit improves the providing method based on user feedback about music provided in the past. The providing unit can also improve the providing algorithm by reflecting user feedback. Furthermore, the providing unit can customize the content to be provided based on user feedback. This makes it possible to provide a music providing method based on user feedback. The improvement of the providing method is performed by, for example, adjustments based on user feedback, technical updates, etc.

[0102] The providing unit can estimate the user's emotions and adjust the order in which music is provided based on the estimated emotions. For example, if the user is relaxed, the providing unit can provide slow music with priority. Furthermore, if the user is in a hurry, the providing unit can also provide fast-tempo music with priority. Furthermore, if the user is concentrating, the providing unit can also provide medium-tempo music with priority. In this way, it is possible to provide the order in which music is provided according to the user's emotions. The order in which music is provided is adjusted based on, for example, the user's emotions, task priorities, etc.

[0103] When providing music, the providing unit can select the optimal delivery method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is simple and highly visible. This makes it possible to provide a music delivery method based on the user's device information. The device information includes, for example, the device type, OS, hardware specifications, etc.

[0104] When providing music, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit automatically sets the provided content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can also provide the provided content in that language. This makes it possible to provide music provided based on the user's language setting. The language setting includes, for example, the user's device setting, application setting, etc.

[0105] When providing music, the providing unit can customize the provided content by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit can provide music related to that location. Furthermore, if the user is on the move, the providing unit can also provide music suitable for the user while on the move. Furthermore, if the user is at home, the providing unit can also provide music that is relaxing at home. In this way, music can be provided based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, etc.

[0106] The collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, the collection unit can request detailed feedback when the user is relaxed. The collection unit can also request brief feedback when the user is in a hurry. Furthermore, the collection unit can also request specific feedback when the user is concentrating. This makes it possible to provide a feedback collection method that corresponds to the user's emotions. The feedback collection method can be adjusted based on, for example, a questionnaire, an interview, sensor data, etc.

[0107] When collecting feedback, the collection unit can select an optimal collection method by referring to the user's past feedback history. The collection unit can improve the collection method, for example, based on feedback provided by the user in the past. The collection unit can also improve the collection algorithm based on the user's past feedback history. Furthermore, the collection unit can customize the collection content by reflecting the user's past feedback. This makes it possible to provide a feedback collection method based on the user's past feedback history. The feedback history includes, for example, the content of past feedback, evaluation scores, comments, etc.

[0108] When collecting feedback, the collection unit can customize the collected content according to the user's current task. For example, if the user is exercising, the collection unit can request feedback on the exercise. If the user is studying, the collection unit can also request feedback on the studying. Furthermore, if the user is doing housework, the collection unit can also request feedback on the housework. This makes it possible to provide collected feedback according to the user's current task. The current task includes, for example, the type of task, progress, priority, etc.

[0109] When collecting feedback, the collection unit can improve the collection method by reflecting the user's feedback. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also improve the collection algorithm by reflecting the user's feedback. Furthermore, the collection unit can customize the collection content based on the user's feedback. This makes it possible to provide a feedback collection method based on the user's feedback. The improvement of the collection method is performed by, for example, adjustments based on the user's feedback, technical updates, etc.

[0110] The collection unit can estimate the user's emotions and adjust the feedback collection order based on the estimated emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed feedback. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting concise feedback. Furthermore, when the user is concentrating, the collection unit can also prioritize collecting specific feedback. This makes it possible to provide a feedback collection order according to the user's emotions. The collection order is adjusted based on, for example, the user's emotions, task priority, etc.

[0111] When collecting feedback, the collection unit can select the optimal collection method taking into account the user's device information. For example, if the user is using a smartphone, the collection unit can provide a collection method that matches the screen size. Furthermore, if the user is using a tablet, the collection unit can also provide a collection method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the collection unit can also provide a simple and highly visible collection method. This makes it possible to provide a feedback collection method based on the user's device information. The device information includes, for example, the device type, OS, hardware specifications, etc.

[0112] When collecting feedback, the collection unit can make the collected content multilingual in accordance with the user's language setting. The collection unit automatically sets the collected content based on, for example, the language setting of the user's device. The collection unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the collection unit can provide the collected content in that language. This makes it possible to provide the collected feedback content based on the user's language setting. The language setting includes, for example, the user's device setting, application setting, etc.

[0113] When collecting feedback, the collection unit can customize the collected content by taking into account the user's geographical location information. For example, if the user is in a specific location, the collection unit can request feedback related to the location. Furthermore, if the user is on the move, the collection unit can request feedback suitable for the user on the move. Furthermore, if the user is at home, the collection unit can request feedback that will help the user relax at home. This makes it possible to provide collected feedback based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, etc. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, calculation unit, generation unit, provision unit, and collection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface through which the user inputs task details. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a recommended tempo based on the input task details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the recommended tempo. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated music to the user. The collection unit is realized by the reception device 38 of the smart device 14 and collects user feedback. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, generation unit, provision unit, and collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to input task content by voice. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a recommended tempo based on the input task content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the recommended tempo. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated music to the user. The collection unit is realized by the microphone 238 of the smart glasses 214 and collects user feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, generation unit, provision unit, and collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and provides an interface through which the user inputs task details by voice. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a recommended tempo based on the input task details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the recommended tempo. The provision unit is realized by the speaker 240 of the headset type terminal 314 and provides the generated music to the user. The collection unit is realized by the microphone 238 of the headset type terminal 314 and collects user feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, generation unit, provision unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface through which the user inputs task details by voice. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a recommended tempo based on the input task details. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the recommended tempo. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated music to the user. The collection unit is realized by the microphone 238 of the robot 414 and collects user feedback.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The reception unit can analyze the user's past task history and suggest the optimal input method. For example, task contents that the user has frequently input in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest tasks to be performed in a specific time period based on the user's past task history. This makes it possible to provide the optimal input method based on the past task history. The past task history includes, for example, the type of task, execution time, completion status, etc.

[0116] The collection unit can collect user feedback and provide it to the generation unit to improve the algorithm. For example, an interface is provided where a user can input an evaluation score for music. For example, if a user provides feedback such as "this music was perfect for exercise," the collection unit provides that feedback to the generation unit. The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback is requested. This allows the accuracy of music generation to be improved by reflecting the user's feedback. The algorithm can be improved by, for example, retraining the machine learning model or adjusting parameters.

[0117] The reception unit can estimate the user's emotions and adjust the task content input method based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of task content. This makes it possible to provide the optimal input method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis.

[0118] When the task content is input, the reception unit can filter the input based on the user's current situation and environment. For example, if the user is out, the reception unit can prioritize displaying tasks that can be performed while away from home. Also, if the user is at home, the reception unit can prioritize displaying tasks that can be performed at home. Furthermore, if the user is in a specific location, the reception unit can suggest tasks related to that location. This makes it possible to provide task content that is appropriate for the user's situation and environment. The current situation and environment include, for example, location information, ambient noise level, device status, etc.

[0119] When inputting task content, the reception unit can select the optimal input means depending on the user's input method. For example, when the user inputs task content by voice, the reception unit converts it into text using voice recognition technology. In addition, when the user inputs task content as text, the reception unit can also provide an input completion function. Furthermore, when the user inputs task content as an image, the reception unit can also analyze the task content using image recognition technology. This makes it possible to provide the optimal input means depending on the user's input method. Input methods include, for example, voice input, text input, and gesture input.

[0120] The reception unit can estimate the user's emotions and determine the priority of the task contents to be input based on the estimated emotions. For example, if the user is feeling stressed, tasks that allow the user to relax are displayed with priority. Furthermore, if the user is relaxed, the reception unit can also display tasks that require concentration with priority. Furthermore, if the user is in a hurry, the reception unit can also display tasks that can be completed quickly with priority. This makes it possible to provide the priority of task contents according to the user's emotions. The priority of task contents is determined based on, for example, urgency, importance, user preferences, etc.

[0121] When calculating the recommended tempo, the calculation unit can adjust the level of detail of the calculation based on the importance of the task. For example, for an important task, a detailed tempo calculation is performed. The calculation unit can also perform a simplified tempo calculation for a less important task. Furthermore, the calculation unit can adjust the accuracy of the calculation according to the importance of the task. This makes it possible to provide a recommended tempo according to the importance of the task. The importance of a task is evaluated based on, for example, the task's deadline, scope of impact, user priority, etc.

[0122] When calculating the recommended tempo, the calculation unit can apply different calculation algorithms depending on the task category. For example, for an exercise task, an algorithm that recommends a fast tempo is applied. For a study task, the calculation unit can also apply an algorithm that recommends a medium tempo. Furthermore, for a housework task, the calculation unit can also apply an algorithm that recommends a slow tempo. In this way, it is possible to provide a recommended tempo according to the task category. Task categories are classified based on, for example, work, study, hobbies, etc.

[0123] When calculating the recommended tempo, the calculation unit can improve the accuracy of the calculation by referring to the user's past calculation results. For example, the calculation unit can calculate the recommended tempo by referring to tempos that the user has previously preferred. The calculation unit can also improve the calculation algorithm based on the user's past calculation results. Furthermore, the calculation unit can improve the accuracy of the calculation of the recommended tempo by reflecting the user's past feedback. This makes it possible to provide a recommended tempo based on the user's past calculation results. Past calculation results include, for example, the history of recommended tempos, user feedback, etc.

[0124] The calculation unit can estimate the user's emotion and adjust the recommended tempo length based on the estimated emotion. For example, if the user is relaxed, the calculation unit can recommend a longer tempo. If the user is in a hurry, the calculation unit can also recommend a shorter tempo. Furthermore, if the user is concentrating, the calculation unit can also recommend a medium-length tempo. In this way, it is possible to provide a recommended tempo length according to the user's emotion. The recommended tempo length is adjusted based on, for example, the time required for the task, the user's level of concentration, etc.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The reception unit inputs the details of the task to be performed by the user. For example, tasks such as exercise, studying, and housework can be considered. The reception unit provides an interface through which the user can input the task details in text format. The task details can also be input using voice input. Furthermore, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions. Step 2: The calculation unit calculates the recommended tempo based on the task content input by the reception unit. The recommended tempo is expressed in units such as BPM (beats per minute). The calculation unit recommends an appropriate tempo depending on the type of task. It can also estimate the user's emotions and adjust the calculation method for the recommended tempo based on the estimated emotions. Step 3: The generator generates music based on the recommended tempo calculated by the calculator. The generator uses the generation AI to generate music according to the type of task and the user's emotions. Step 4: The providing unit provides the music generated by the generating unit to the user's device. The providing unit provides the music in a streaming format and can adjust the providing method based on the user's emotions. Step 5: The collection unit collects user feedback on the music provided by the provision unit. The collection unit provides an interface for users to input evaluation scores for the music, and can also adjust the method of collecting feedback based on users' emotions.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 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 reception section for inputting task details; a calculation unit that calculates a recommended tempo based on the task content input by the reception unit; a generation unit that generates music based on the recommended tempo calculated by the calculation unit; a providing unit that provides the music generated by the generating unit; a collection unit that collects user feedback on the music provided by the providing unit. A system characterized by:

2. The collecting unit Collecting user feedback and providing it to the generator to improve the algorithm 2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the task input method based on the estimated user emotions.

2. The system of claim 1.

4. The reception unit Analyzes the user's past task history and suggests the optimal input method 2. The system of claim 1.

5. The reception unit Filter task input based on the user's current situation and environment 2. The system of claim 1.

6. The reception unit When entering task content, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize the task content to be input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When entering task details, the system takes into account the user's geographic location information and prioritizes the most relevant tasks.

2. The system of claim 1.

Images