system

Abstract

The system according to this embodiment aims to provide optimal music therapy tailored to the patient's condition and to visualize its effects. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the patient's vital signs and interview results. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a music program based on the analysis results obtained by the analysis unit. The provision unit provides treatment based on the music program generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an optimal music therapy according to the patient's condition, and there is room for improvement in visualizing the effect and optimizing the treatment plan.

[0005] The system according to the embodiment aims to provide an optimal music therapy according to the patient's condition and visualize its effect.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects the patient's vital signs and interview results. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates a music program based on the analysis results obtained by the analysis unit. The provision unit provides treatment based on the music program generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal music therapy tailored to the patient's condition and visualize its effects. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The music therapy support system according to an embodiment of the present invention is an AI agent that assists music therapists and medical professionals, and is a system that creates an optimal music program tailored to the patient's condition. This music therapy support system proposes music-based treatments for various symptoms such as mental stress, anxiety, and sleep disorders, and visualizes the effects with data. This service combines expertise in music therapy with AI technology to enable individually optimized treatment. It contributes to improving the patient's QOL (quality of life) and reduces the burden on medical professionals. For example, the music therapy support system collects the patient's vital signs and interview results, and the AI ​​analyzes this data. Next, based on the analysis results, it automatically generates a music program tailored to the patient's preferences and symptoms. For example, it suggests relaxing music for patients experiencing stress, and music that promotes restful sleep for patients with sleep disorders. Furthermore, the music therapy support system visualizes the effects of music therapy with data and optimizes the treatment plan. For example, it collects the patient's vital signs and subjective feedback after music therapy, and the AI ​​analyzes this data to evaluate the effects. This improves the accuracy and efficiency of treatment. The music therapy support system also supports remote therapy, providing support for online music therapy sessions. This makes it possible to provide music therapy to patients in remote locations or those with mobility difficulties. Furthermore, the music therapy support system can be integrated with medical systems, and by integrating with electronic medical records and medical databases, it can provide music therapy in conjunction with the patient's treatment history and other medical data. This allows for the provision of more consistent treatment. In this way, the music therapy support system utilizes AI technology to support music therapy, aiming to improve patients' quality of life and reduce the burden on medical staff. As a result, the music therapy support system can create and provide optimal music programs tailored to the patient's condition.

[0029] The music therapy support system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects the patient's vital signs and interview results. The data collection unit collects vital signs such as heart rate, blood pressure, and body temperature. The data collection unit can also collect interview results such as the patient's chief complaint, medical history, and lifestyle. For example, the data collection unit measures the patient's heart rate and collects the data. The data collection unit can also measure the patient's blood pressure and collect the data. The data collection unit can also measure the patient's body temperature and collect the data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using statistical analysis or machine learning algorithms, for example. For example, the analysis unit analyzes the trends in the data using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze the correlation between the data. The data generation unit generates a music program based on the analysis results obtained by the analysis unit. The data generation unit generates a music program considering, for example, the genre of music, the length of the song, the tempo, etc. For example, the data generation unit generates music with a relaxing effect. The generation unit can also generate music that promotes restful sleep. The generation unit can also generate music that reduces stress. The delivery unit provides therapy based on the music program generated by the generation unit. The delivery unit provides therapy considering, for example, the frequency, duration, and method of music therapy sessions. For example, the delivery unit provides music therapy sessions once a week. The delivery unit can also provide music therapy sessions for 30 minutes each. The delivery unit can also provide music therapy sessions online. As a result, the music therapy support system according to this embodiment can create an optimal music program tailored to the patient's condition and provide therapy.

[0030] The data collection unit collects patients' vital signs and interview results. Specifically, it uses wearable devices and medical equipment to collect vital signs such as heart rate, blood pressure, and body temperature. For example, heart rate data is acquired in real time from heart rate monitors or smartwatches worn by patients and transmitted to a central database. Blood pressure is measured using an electronic blood pressure monitor, and the results are collected automatically. Body temperature is measured using non-contact thermometers or smart thermometers, and the data is collected. The data collection unit can also collect interview results such as the patient's chief complaint, medical history, and lifestyle. This includes methods of collecting information entered by the patient themselves using electronic questionnaires or smartphone apps. For example, if a patient enters their chief complaint or symptoms through an app, that information is collected and made accessible to medical staff. Furthermore, the data collection unit also collects data on the patient's lifestyle. This includes diet, exercise frequency, and sleep quality, and this data is collected as patients record it daily. This allows the data collection unit to comprehensively understand the patient's health status and provide the information necessary for treatment. The collected data is stored on a secure cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the data using statistical analysis and machine learning algorithms to evaluate the patient's health status and treatment effectiveness. For example, it uses statistical analysis to analyze data trends and understand patterns in fluctuations in the patient's vital signs and interview results. This allows for early detection of changes in the patient's health status and the provision of appropriate treatment. Furthermore, the analysis unit can also analyze data using machine learning algorithms. For example, it uses deep learning to build a model that predicts health risks from the patient's vital signs and interview results. This model can learn from past data and evaluate risks based on newly collected data. The analysis unit can also analyze data correlations. For example, it can analyze the relationship between heart rate and stress levels and confirm that heart rate tends to increase when stress levels rise. This provides information useful for managing patient stress. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. For example, if the heart rate rises sharply, the anomaly detection algorithm will detect the anomaly and notify medical staff. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates music programs based on the analysis results obtained by the analysis unit. Specifically, it considers the genre of music, song length, tempo, etc., to generate a music program that is optimal for the patient's condition. For example, to generate music with a relaxing effect, it can combine slow-tempo classical music and nature sounds. To generate music that promotes restful sleep, it can select music with low frequencies or music that includes white noise. To generate music that reduces stress, it can select genres such as pop or jazz that suit the patient's preferences and adjust the tempo and rhythm. The generation unit can also automatically generate music programs using AI. For example, it can use generation AI to generate individually customized music based on the patient's vital signs and interview results. This AI can adjust the tempo and melody of the music in real time according to the patient's heart rate and stress level. Furthermore, the generation unit can evaluate the effectiveness of the music program based on past data and patient feedback and continuously improve it. For example, it can record music programs that the patient found relaxing and provide similar music in the next session. In this way, the generation unit can provide the optimal music program tailored to the patient's condition and maximize the therapeutic effect.

[0033] The service provider delivers therapy based on music programs generated by the production unit. Specifically, they provide therapy considering the frequency, duration, and method of music therapy sessions. For example, they may offer music therapy sessions once a week, each lasting 30 minutes. The service provider can also deliver music therapy sessions online, allowing patients to receive therapy from the comfort of their homes. Online sessions utilize video calls and streaming services to deliver music in real time and observe patient responses. The service provider can also collect patient feedback and continuously improve the content and methods of the sessions. For example, if a patient feels relaxed by a particular piece of music, that music will be used in the next session. The service provider can also reliably transmit information using multiple communication methods. For example, they can ensure important information is delivered reliably by using not only smartphone notifications but also voice calls, SMS, and email. Furthermore, the service provider can flexibly adjust the frequency and duration of sessions according to the patient's condition. For example, for patients experiencing high stress levels, they can increase the frequency of sessions and provide music that enhances relaxation. This allows the service provider to deliver therapy quickly and reliably to patients, maximizing therapeutic effects.

[0034] The service provider can visualize the effects of music therapy using data and optimize treatment plans. The service provider visualizes the effects of music therapy using methods such as graphs and dashboards. For example, the service provider displays the effects of music therapy in graphs. The service provider can also display the effects of music therapy on a dashboard. The service provider can also display the effects of music therapy in tabular format. By visualizing the effects of music therapy, the service provider can optimize treatment plans. The service provider optimizes treatment plans using methods such as algorithms and feedback loops. For example, the service provider optimizes treatment plans using algorithms. The service provider can also optimize treatment plans by designing feedback loops. The service provider can also optimize treatment plans based on patient feedback. This allows the service provider to visualize the effects of music therapy using data and optimize treatment plans.

[0035] The service provider can support online music therapy sessions. The service provider supports online sessions in ways such as the platform used and the format of the session. For example, the service provider can support online sessions using a video conferencing platform. The service provider can also support online sessions using an audio conferencing platform. The service provider can also support online sessions using a chat platform. By supporting online sessions, the service provider can provide music therapy to patients in remote locations or those with mobility difficulties. Some or all of the above-described processes in the service provider may be performed, for example, using a generative AI, or not using a generative AI. For example, the service provider can input the method for delivering the online session into a generative AI and have the generative AI execute the optimal delivery method. This enables the service provider to support online music therapy sessions.

[0036] The service provider can integrate with electronic medical records and medical databases. The service provider integrates with electronic medical records and medical databases, for example, through methods such as database linking methods and data formats. For example, the service provider can integrate with electronic medical records using APIs. The service provider can also integrate with medical databases using database linking methods. The service provider can also integrate with electronic medical records and medical databases by standardizing data formats. By integrating with electronic medical records and medical databases, the service provider can provide music therapy in conjunction with patient treatment history and other medical data. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input data from electronic medical records and medical databases into a generative AI and have the generative AI perform data integration. This allows for more consistent treatment through integration with electronic medical records and medical databases.

[0037] The data collection unit can collect data tailored to the patient's preferences and symptoms. For example, the data collection unit can collect data on preferences such as musical tastes and relaxation methods. For instance, it can collect the patient's favorite music genres. The data collection unit can also collect the patient's relaxation methods. It can also collect the patient's musical preferences. The data collection unit can also collect data on symptoms such as pain levels and stress levels. For example, it can collect the patient's pain levels. The data collection unit can also collect the patient's stress levels. The data collection unit can also collect the patient's sleep quality. By collecting data tailored to the patient's preferences and symptoms, the data collection unit can provide more appropriate treatment. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the patient's preferences and symptoms into a generative AI and have the generative AI perform the data collection. This allows for the collection of data tailored to the patient's preferences and symptoms, thereby providing more appropriate treatment.

[0038] The analysis unit can analyze the collected data and propose the optimal music therapy. The analysis unit analyzes the collected data using methods such as music selection criteria and methods for evaluating therapeutic effects. For example, the analysis unit analyzes the data using music selection criteria. The analysis unit can also analyze the data using methods for evaluating therapeutic effects. The analysis unit can also analyze the correlation between data. By analyzing the collected data, the analysis unit can propose the optimal music therapy. The analysis unit analyzes the data using methods such as statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the data trends using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze the correlation between data. This allows the accuracy and efficiency of treatment to be improved by analyzing the collected data and proposing the optimal music therapy. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the collected data into the generative AI and have the generative AI propose the optimal music therapy.

[0039] The data collection unit can analyze a patient's past treatment history and select the optimal data collection method. For example, the data collection unit can select the optimal method based on data collection methods the patient has used in the past. The data collection unit can also determine whether a particular data collection method is effective based on the patient's past treatment history. The data collection unit can also analyze a patient's past treatment history and adjust the frequency of data collection. This allows the optimal data collection method to be selected by analyzing the patient's past treatment history. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's past treatment history data into the generative AI and have the generative AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the patient's current living situation and environment. For example, if the patient is at work, the data collection unit can refrain from collecting data and collect it during private time. If the patient is exercising, the data collection unit can adjust the timing to collect data after the exercise. If the patient is traveling, the data collection unit can adjust the timing to collect data after they return home. This allows for the collection of more appropriate data by collecting data based on the patient's living situation and environment. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's living situation and environment data into the generative AI and have the generative AI perform the data collection filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if the patient is at home, the data collection unit will prioritize the collection of data related to the home environment. If the patient is in a hospital, the data collection unit can also prioritize the collection of data related to the hospital environment. If the patient is traveling, the data collection unit can also prioritize the collection of data related to the environment at the travel destination. In this way, by considering the patient's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's geographical location data into the generative AI and have the generative AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient posts on social media indicating stress, the data collection unit can collect stress-related data. If the patient posts on social media indicating relaxation, the data collection unit can also collect data related to relaxation. If the patient posts on social media indicating anxiety, the data collection unit can also collect data related to anxiety. In this way, relevant data can be collected by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit is performed using generative AI. For example, the data collection unit can input the patient's social media activity data into the generative AI and have the generative AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. In this way, efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical analysis algorithm to vital sign data. The analysis unit can also apply a natural language processing analysis algorithm to medical interview result data. The analysis unit can also apply a recommendation system analysis algorithm to patient preference data. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also analyze older data as needed. The analysis unit can also adjust the order of analysis based on the data collection timing. This allows for efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the data collection timing into the generation AI and have the generation AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0047] The generation unit can generate the optimal music program by referring to the patient's past music therapy history. For example, the generation unit can generate the optimal program based on music therapy programs the patient has used in the past. The generation unit can also select an effective program from the patient's past music therapy history. The generation unit can also analyze the patient's past music therapy history and generate the most effective program. In this way, the optimal music program can be generated by referring to the patient's past music therapy history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the patient's past music therapy history data into the generation AI and have the generation AI execute the generation of the optimal program.

[0048] The generation unit can customize music programs based on the patient's current lifestyle when generating them. For example, if the patient is working, the generation unit can generate a music program that enhances concentration. If the patient is relaxed, the generation unit can also generate a music program with a relaxing effect. If the patient is exercising, the generation unit can also generate a music program that enhances the effects of exercise. In this way, by customizing the music program based on the patient's current lifestyle, a more effective music program can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input patient lifestyle data into the generation AI and have the generation AI perform the program customization.

[0049] The generation unit can generate an optimal music program by considering the patient's geographical location information when generating a music program. For example, if the patient is at home, the generation unit can generate a music program suitable for the home environment. If the patient is in a hospital, the generation unit can also generate a music program suitable for the hospital environment. If the patient is traveling, the generation unit can also generate a music program suitable for the environment of the travel destination. In this way, the optimal music program can be generated by considering the patient's geographical location information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the patient's geographical location information data into the generation AI and have the generation AI execute the generation of the optimal program.

[0050] The generation unit can analyze the patient's social media activity and suggest a music program when generating it. For example, if the patient posts relaxing content on social media, the generation unit can suggest a relaxing music program. If the patient posts stressful content on social media, the generation unit can also suggest a stress-reducing music program. If the patient posts anxious content on social media, the generation unit can also suggest an anxiety-reducing music program. In this way, by analyzing the patient's social media activity, a more appropriate music program can be suggested. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the patient's social media activity data into the generation AI and have the generation AI execute the program suggestion.

[0051] The treatment unit can select the optimal treatment method by referring to the patient's past treatment history when providing treatment. For example, the treatment unit can select the optimal treatment method based on the treatment methods the patient has used in the past. The treatment unit can also select an effective treatment method from the patient's past treatment history. The treatment unit can also analyze the patient's past treatment history and select the most effective treatment method. In this way, the optimal treatment method can be selected by referring to the patient's past treatment history. Some or all of the above processing in the treatment unit is performed using a generating AI. For example, the treatment unit can input the patient's past treatment history data into the generating AI and have the generating AI perform the selection of the optimal treatment method.

[0052] The service provider can customize treatment methods based on the patient's current lifestyle when providing treatment. For example, if the patient is at work, the service provider can provide treatment methods to enhance concentration. If the patient is relaxed, the service provider can also provide treatment methods that promote relaxation. If the patient is exercising, the service provider can also provide treatment methods that enhance the effects of exercise. In this way, more effective treatment can be provided by customizing treatment methods based on the patient's current lifestyle. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input patient lifestyle data into the generative AI and have the generative AI perform the customization of treatment methods.

[0053] The service provider can select the optimal treatment method by considering the patient's geographical location when providing treatment. For example, if the patient is at home, the service provider can provide a treatment method suitable for the home environment. If the patient is in a hospital, the service provider can also provide a treatment method suitable for the hospital environment. If the patient is traveling, the service provider can also provide a treatment method suitable for the environment of the travel destination. In this way, the optimal treatment method can be selected by considering the patient's geographical location. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's geographical location data into the generative AI and have the generative AI select the optimal treatment method.

[0054] The service provider can analyze a patient's social media activity and propose treatment options when providing treatment. For example, if a patient posts relaxing content on social media, the service provider can propose treatment options with a relaxing effect. If a patient posts stressful content on social media, the service provider can also propose treatment options with a stress-reducing effect. If a patient posts anxious content on social media, the service provider can also propose treatment options with an anxiety-reducing effect. In this way, by analyzing a patient's social media activity, it is possible to propose more appropriate treatment options. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's social media activity data into the generative AI and have the generative AI propose treatment options.

[0055] The service provider can visualize the effects of music therapy using data and predict current effects by referring to past treatment data when optimizing treatment plans. For example, the service provider can predict current treatment effects based on past treatment data. The service provider can also analyze trends in effects from past treatment data and predict current effects. The service provider can also evaluate current treatment effects by referring to past treatment data. This allows the service provider to predict current effects and optimize treatment plans by referring to past treatment data. Some or all of the above processes in the service provider are performed using generative AI. For example, the service provider can input past treatment data into the generative AI and have the generative AI perform predictions of current effects.

[0056] The service provider can visualize the effects of music therapy using data and analyze changes in effects based on the timing of treatment when optimizing treatment plans. For example, the service provider can analyze changes in effects based on the timing of treatment. The service provider can also analyze trends in effects for each treatment period. The service provider can also predict changes in effects based on the timing of treatment. This allows for the provision of more effective treatment plans by analyzing changes in effects based on the timing of treatment. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input treatment timing data into the generative AI and have the generative AI perform an analysis of changes in effects.

[0057] The service provider can provide the optimal session by referring to the patient's past online treatment history when supporting online music therapy sessions. For example, the service provider can provide the optimal session based on the online treatment sessions the patient has used in the past. The service provider can also select an effective session from the patient's past online treatment history. The service provider can also analyze the patient's past online treatment history and provide the most effective session. In this way, the service provider can provide the optimal online session by referring to the patient's past online treatment history. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's past online treatment history data into the generative AI and have the generative AI perform the task of providing the optimal session.

[0058] The service provider can provide an optimal session by considering the patient's device information when supporting online music therapy sessions. For example, if the patient is using a smartphone, the service provider can provide a session optimized for the smartphone. If the patient is using a tablet, the service provider can also provide a session optimized for the tablet. If the patient is using a personal computer, the service provider can also provide a session optimized for the personal computer. In this way, the service provider can provide an optimal online session by considering the patient's device information. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's device information into the generative AI and have the generative AI perform the task of providing an optimal session.

[0059] The service provider can select the optimal integration method by referring to past medical data when integrating with electronic medical records and medical databases. For example, the service provider can select the optimal data integration method based on past medical data. The service provider can also select an effective data integration method from past medical data. The service provider can also analyze past medical data and select the most effective data integration method. In this way, the optimal data integration method can be selected by referring to past medical data. Some or all of the above processing in the service provider is performed using a generation AI. For example, the service provider can input past medical data into the generation AI and have the generation AI perform the selection of the optimal integration method.

[0060] The data delivery unit can weight data based on the patient's medical history when integrating with electronic medical records and medical databases. For example, the data delivery unit can weight important data based on the patient's medical history. The data delivery unit can also weight effective data from the patient's medical history. The data delivery unit can analyze the patient's medical history and weight the most important data. This allows for the priority integration of more important data by weighting data based on the patient's medical history. Some or all of the above processing in the data delivery unit is performed using generative AI. For example, the data delivery unit can input patient medical history data into the generative AI and have the generative AI perform the data weighting.

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

[0062] The data collection unit can analyze a patient's past treatment history and select the optimal data collection method. For example, it can select the best method based on data collection methods the patient has used in the past. It can also determine whether a particular data collection method is effective based on the patient's past treatment history. It can also analyze the patient's past treatment history and adjust the frequency of data collection. In this way, the optimal data collection method can be selected by analyzing the patient's past treatment history. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's past treatment history data into the generative AI and have the generative AI select the optimal data collection method.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0064] The treatment unit can select the optimal treatment method by referring to the patient's past treatment history when providing treatment. For example, it can select the optimal treatment method based on the treatment methods the patient has used in the past. It can also select an effective treatment method from the patient's past treatment history. It can also analyze the patient's past treatment history and select the most effective treatment method. In this way, the optimal treatment method can be selected by referring to the patient's past treatment history. Some or all of the above processing in the treatment unit is performed using a generation AI. For example, the treatment unit can input the patient's past treatment history data into the generation AI and have the generation AI perform the selection of the optimal treatment method.

[0065] The service provider can select the optimal treatment method by considering the patient's geographical location when providing treatment. For example, if the patient is at home, it can provide a treatment method suitable for the home environment. If the patient is in a hospital, it can also provide a treatment method suitable for the hospital environment. If the patient is traveling, it can also provide a treatment method suitable for the environment of the travel destination. In this way, the optimal treatment method can be selected by considering the patient's geographical location. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's geographical location data into the generative AI and have the generative AI select the optimal treatment method.

[0066] The service provider can provide an optimal online music therapy session by considering the patient's device information. For example, if the patient is using a smartphone, it can provide a session optimized for smartphones. If the patient is using a tablet, it can also provide a session optimized for tablets. If the patient is using a personal computer, it can also provide a session optimized for personal computers. This allows for the provision of the most optimal online session by considering the patient's device information. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's device information into the generative AI and have the generative AI execute the provision of the optimal session.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects the patient's vital signs and interview results. For example, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as interview results such as the patient's chief complaint, medical history, and lifestyle. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes data trends and correlations using statistical analysis and machine learning algorithms. Step 3: The generation unit generates a music program based on the analysis results obtained by the analysis unit. For example, it generates music with relaxing, sleep-inducing, and stress-reducing effects, taking into account the genre of music, song length, tempo, etc. Step 4: The provider unit provides treatment based on the music program generated by the generator unit. For example, the provider unit provides treatment considering the frequency, duration, and method of music therapy sessions.

[0069] (Example of form 2) The music therapy support system according to an embodiment of the present invention is an AI agent that assists music therapists and medical professionals, and is a system that creates an optimal music program tailored to the patient's condition. This music therapy support system proposes music-based treatments for various symptoms such as mental stress, anxiety, and sleep disorders, and visualizes the effects with data. This service combines expertise in music therapy with AI technology to enable individually optimized treatment. It contributes to improving the patient's QOL (quality of life) and reduces the burden on medical professionals. For example, the music therapy support system collects the patient's vital signs and interview results, and the AI ​​analyzes this data. Next, based on the analysis results, it automatically generates a music program tailored to the patient's preferences and symptoms. For example, it suggests relaxing music for patients experiencing stress, and music that promotes restful sleep for patients with sleep disorders. Furthermore, the music therapy support system visualizes the effects of music therapy with data and optimizes the treatment plan. For example, it collects the patient's vital signs and subjective feedback after music therapy, and the AI ​​analyzes this data to evaluate the effects. This improves the accuracy and efficiency of treatment. The music therapy support system also supports remote therapy, providing support for online music therapy sessions. This makes it possible to provide music therapy to patients in remote locations or those with mobility difficulties. Furthermore, the music therapy support system can be integrated with medical systems, and by integrating with electronic medical records and medical databases, it can provide music therapy in conjunction with the patient's treatment history and other medical data. This allows for the provision of more consistent treatment. In this way, the music therapy support system utilizes AI technology to support music therapy, aiming to improve patients' quality of life and reduce the burden on medical staff. As a result, the music therapy support system can create and provide optimal music programs tailored to the patient's condition.

[0070] The music therapy support system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit collects the patient's vital signs and interview results. The data collection unit collects vital signs such as heart rate, blood pressure, and body temperature. The data collection unit can also collect interview results such as the patient's chief complaint, medical history, and lifestyle. For example, the data collection unit measures the patient's heart rate and collects the data. The data collection unit can also measure the patient's blood pressure and collect the data. The data collection unit can also measure the patient's body temperature and collect the data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using statistical analysis or machine learning algorithms, for example. For example, the analysis unit analyzes the trends in the data using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze the correlation between the data. The data generation unit generates a music program based on the analysis results obtained by the analysis unit. The data generation unit generates a music program considering, for example, the genre of music, the length of the song, the tempo, etc. For example, the data generation unit generates music with a relaxing effect. The generation unit can also generate music that promotes restful sleep. The generation unit can also generate music that reduces stress. The delivery unit provides therapy based on the music program generated by the generation unit. The delivery unit provides therapy considering, for example, the frequency, duration, and method of music therapy sessions. For example, the delivery unit provides music therapy sessions once a week. The delivery unit can also provide music therapy sessions for 30 minutes each. The delivery unit can also provide music therapy sessions online. As a result, the music therapy support system according to this embodiment can create an optimal music program tailored to the patient's condition and provide therapy.

[0071] The data collection unit collects patients' vital signs and interview results. Specifically, it uses wearable devices and medical equipment to collect vital signs such as heart rate, blood pressure, and body temperature. For example, heart rate data is acquired in real time from heart rate monitors or smartwatches worn by patients and transmitted to a central database. Blood pressure is measured using an electronic blood pressure monitor, and the results are collected automatically. Body temperature is measured using non-contact thermometers or smart thermometers, and the data is collected. The data collection unit can also collect interview results such as the patient's chief complaint, medical history, and lifestyle. This includes methods of collecting information entered by the patient themselves using electronic questionnaires or smartphone apps. For example, if a patient enters their chief complaint or symptoms through an app, that information is collected and made accessible to medical staff. Furthermore, the data collection unit also collects data on the patient's lifestyle. This includes diet, exercise frequency, and sleep quality, and this data is collected as patients record it daily. This allows the data collection unit to comprehensively understand the patient's health status and provide the information necessary for treatment. The collected data is stored on a secure cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the data collection frequency and accuracy, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the data using statistical analysis and machine learning algorithms to evaluate the patient's health status and treatment effectiveness. For example, it uses statistical analysis to analyze data trends and understand patterns in fluctuations in the patient's vital signs and interview results. This allows for early detection of changes in the patient's health status and the provision of appropriate treatment. Furthermore, the analysis unit can also analyze data using machine learning algorithms. For example, it uses deep learning to build a model that predicts health risks from the patient's vital signs and interview results. This model can learn from past data and evaluate risks based on newly collected data. The analysis unit can also analyze data correlations. For example, it can analyze the relationship between heart rate and stress levels and confirm that heart rate tends to increase when stress levels rise. This provides information useful for managing patient stress. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. For example, if the heart rate rises sharply, the anomaly detection algorithm will detect the anomaly and notify medical staff. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The generation unit generates music programs based on the analysis results obtained by the analysis unit. Specifically, it considers the genre of music, song length, tempo, etc., to generate a music program that is optimal for the patient's condition. For example, to generate music with a relaxing effect, it can combine slow-tempo classical music and nature sounds. To generate music that promotes restful sleep, it can select music with low frequencies or music that includes white noise. To generate music that reduces stress, it can select genres such as pop or jazz that suit the patient's preferences and adjust the tempo and rhythm. The generation unit can also automatically generate music programs using AI. For example, it can use generation AI to generate individually customized music based on the patient's vital signs and interview results. This AI can adjust the tempo and melody of the music in real time according to the patient's heart rate and stress level. Furthermore, the generation unit can evaluate the effectiveness of the music program based on past data and patient feedback and continuously improve it. For example, it can record music programs that the patient found relaxing and provide similar music in the next session. In this way, the generation unit can provide the optimal music program tailored to the patient's condition and maximize the therapeutic effect.

[0074] The service provider delivers therapy based on music programs generated by the production unit. Specifically, they provide therapy considering the frequency, duration, and method of music therapy sessions. For example, they may offer music therapy sessions once a week, each lasting 30 minutes. The service provider can also deliver music therapy sessions online, allowing patients to receive therapy from the comfort of their homes. Online sessions utilize video calls and streaming services to deliver music in real time and observe patient responses. The service provider can also collect patient feedback and continuously improve the content and methods of the sessions. For example, if a patient feels relaxed by a particular piece of music, that music will be used in the next session. The service provider can also reliably transmit information using multiple communication methods. For example, they can ensure important information is delivered reliably by using not only smartphone notifications but also voice calls, SMS, and email. Furthermore, the service provider can flexibly adjust the frequency and duration of sessions according to the patient's condition. For example, for patients experiencing high stress levels, they can increase the frequency of sessions and provide music that enhances relaxation. This allows the service provider to deliver therapy quickly and reliably to patients, maximizing therapeutic effects.

[0075] The service provider can visualize the effects of music therapy using data and optimize treatment plans. The service provider visualizes the effects of music therapy using methods such as graphs and dashboards. For example, the service provider displays the effects of music therapy in graphs. The service provider can also display the effects of music therapy on a dashboard. The service provider can also display the effects of music therapy in tabular format. By visualizing the effects of music therapy, the service provider can optimize treatment plans. The service provider optimizes treatment plans using methods such as algorithms and feedback loops. For example, the service provider optimizes treatment plans using algorithms. The service provider can also optimize treatment plans by designing feedback loops. The service provider can also optimize treatment plans based on patient feedback. This allows the service provider to visualize the effects of music therapy using data and optimize treatment plans.

[0076] The service provider can support online music therapy sessions. The service provider supports online sessions in ways such as the platform used and the format of the session. For example, the service provider can support online sessions using a video conferencing platform. The service provider can also support online sessions using an audio conferencing platform. The service provider can also support online sessions using a chat platform. By supporting online sessions, the service provider can provide music therapy to patients in remote locations or those with mobility difficulties. Some or all of the above-described processes in the service provider may be performed, for example, using a generative AI, or not using a generative AI. For example, the service provider can input the method for delivering the online session into a generative AI and have the generative AI execute the optimal delivery method. This enables the service provider to support online music therapy sessions.

[0077] The service provider can integrate with electronic medical records and medical databases. The service provider integrates with electronic medical records and medical databases, for example, through methods such as database linking methods and data formats. For example, the service provider can integrate with electronic medical records using APIs. The service provider can also integrate with medical databases using database linking methods. The service provider can also integrate with electronic medical records and medical databases by standardizing data formats. By integrating with electronic medical records and medical databases, the service provider can provide music therapy in conjunction with patient treatment history and other medical data. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input data from electronic medical records and medical databases into a generative AI and have the generative AI perform data integration. This allows for more consistent treatment through integration with electronic medical records and medical databases.

[0078] The data collection unit can collect data tailored to the patient's preferences and symptoms. For example, the data collection unit can collect data on preferences such as musical tastes and relaxation methods. For instance, it can collect the patient's favorite music genres. The data collection unit can also collect the patient's relaxation methods. It can also collect the patient's musical preferences. The data collection unit can also collect data on symptoms such as pain levels and stress levels. For example, it can collect the patient's pain levels. The data collection unit can also collect the patient's stress levels. The data collection unit can also collect the patient's sleep quality. By collecting data tailored to the patient's preferences and symptoms, the data collection unit can provide more appropriate treatment. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the patient's preferences and symptoms into a generative AI and have the generative AI perform the data collection. This allows for the collection of data tailored to the patient's preferences and symptoms, thereby providing more appropriate treatment.

[0079] The analysis unit can analyze the collected data and propose the optimal music therapy. The analysis unit analyzes the collected data using methods such as music selection criteria and methods for evaluating therapeutic effects. For example, the analysis unit analyzes the data using music selection criteria. The analysis unit can also analyze the data using methods for evaluating therapeutic effects. The analysis unit can also analyze the correlation between data. By analyzing the collected data, the analysis unit can propose the optimal music therapy. The analysis unit analyzes the data using methods such as statistical analysis and machine learning algorithms. For example, the analysis unit analyzes the data trends using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze the correlation between data. This allows the accuracy and efficiency of treatment to be improved by analyzing the collected data and proposing the optimal music therapy. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the collected data into the generative AI and have the generative AI propose the optimal music therapy.

[0080] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data when the patient is relaxed to obtain accurate vital signs. The data collection unit can also avoid collecting data when the patient is stressed and try again later. If the patient is sleeping, the data collection unit can adjust the timing to collect data after they wake up. This allows for the collection of more accurate data by adjusting the timing of data collection based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit is performed using generative AI. For example, the data collection unit can input patient emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0081] The data collection unit can analyze a patient's past treatment history and select the optimal data collection method. For example, the data collection unit can select the optimal method based on data collection methods the patient has used in the past. The data collection unit can also determine whether a particular data collection method is effective based on the patient's past treatment history. The data collection unit can also analyze a patient's past treatment history and adjust the frequency of data collection. This allows the optimal data collection method to be selected by analyzing the patient's past treatment history. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's past treatment history data into the generative AI and have the generative AI select the optimal data collection method.

[0082] The data collection unit can filter data based on the patient's current living situation and environment. For example, if the patient is at work, the data collection unit can refrain from collecting data and collect it during private time. If the patient is exercising, the data collection unit can adjust the timing to collect data after the exercise. If the patient is traveling, the data collection unit can adjust the timing to collect data after they return home. This allows for the collection of more appropriate data by collecting data based on the patient's living situation and environment. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's living situation and environment data into the generative AI and have the generative AI perform the data collection filtering.

[0083] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting stress-related data. If the patient is relaxed, the data collection unit may also prioritize collecting data related to relaxation. If the patient is anxious, the data collection unit may also prioritize collecting data related to anxiety. This allows for the priority collection of more important data by prioritizing data based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit is performed using generative AI. For example, the data collection unit can input patient emotion data into the generative AI and have the generative AI determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if the patient is at home, the data collection unit will prioritize the collection of data related to the home environment. If the patient is in a hospital, the data collection unit can also prioritize the collection of data related to the hospital environment. If the patient is traveling, the data collection unit can also prioritize the collection of data related to the environment at the travel destination. In this way, by considering the patient's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's geographical location data into the generative AI and have the generative AI perform the collection of highly relevant data.

[0085] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient posts on social media indicating stress, the data collection unit can collect stress-related data. If the patient posts on social media indicating relaxation, the data collection unit can also collect data related to relaxation. If the patient posts on social media indicating anxiety, the data collection unit can also collect data related to anxiety. In this way, relevant data can be collected by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit is performed using generative AI. For example, the data collection unit can input the patient's social media activity data into the generative AI and have the generative AI perform the collection of relevant data.

[0086] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit can display the analysis results in a visually easy-to-understand manner. If the patient is stressed, the analysis unit can also display the analysis results concisely. If the patient is anxious, the analysis unit can also display the analysis results in detail. By adjusting the presentation of the analysis based on the patient's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. In this way, efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical analysis algorithm to vital sign data. The analysis unit can also apply a natural language processing analysis algorithm to medical interview result data. The analysis unit can also apply a recommendation system analysis algorithm to patient preference data. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0089] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. If the patient is stressed, the analysis unit can also perform a concise analysis. If the patient is anxious, the analysis unit can also perform an analysis of an appropriate length. By adjusting the length of the analysis based on the patient's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also analyze older data as needed. The analysis unit can also adjust the order of analysis based on the data collection timing. This allows for efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the data collection timing into the generation AI and have the generation AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the analysis order.

[0092] The generation unit can estimate the patient's emotions and adjust the music program generation method based on the estimated emotions. For example, if the patient is relaxed, the generation unit will generate relaxing music. If the patient is stressed, the generation unit can also generate stress-reducing music. If the patient is anxious, the generation unit can also generate anxiety-reducing music. By adjusting the music program generation method based on the patient's emotions, a more effective music program can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input patient emotion data into the generation AI and have the generation AI adjust the music program generation method.

[0093] The generation unit can generate the optimal music program by referring to the patient's past music therapy history. For example, the generation unit can generate the optimal program based on music therapy programs the patient has used in the past. The generation unit can also select an effective program from the patient's past music therapy history. The generation unit can also analyze the patient's past music therapy history and generate the most effective program. In this way, the optimal music program can be generated by referring to the patient's past music therapy history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the patient's past music therapy history data into the generation AI and have the generation AI execute the generation of the optimal program.

[0094] The generation unit can customize music programs based on the patient's current lifestyle when generating them. For example, if the patient is working, the generation unit can generate a music program that enhances concentration. If the patient is relaxed, the generation unit can also generate a music program with a relaxing effect. If the patient is exercising, the generation unit can also generate a music program that enhances the effects of exercise. In this way, by customizing the music program based on the patient's current lifestyle, a more effective music program can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input patient lifestyle data into the generation AI and have the generation AI perform the program customization.

[0095] The generation unit can estimate the patient's emotions and determine the priority of music programs based on the estimated emotions. For example, if the patient is stressed, the generation unit will prioritize generating music programs with stress-reducing effects. If the patient is relaxed, the generation unit can also prioritize generating music programs with relaxing effects. If the patient is anxious, the generation unit can also prioritize generating music programs with anxiety-reducing effects. This allows for the provision of more effective music programs by prioritizing music programs based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generative AI. For example, the generation unit can input patient emotion data into the generative AI and have the generative AI determine the priority of music programs.

[0096] The generation unit can generate an optimal music program by considering the patient's geographical location information when generating a music program. For example, if the patient is at home, the generation unit can generate a music program suitable for the home environment. If the patient is in a hospital, the generation unit can also generate a music program suitable for the hospital environment. If the patient is traveling, the generation unit can also generate a music program suitable for the environment of the travel destination. In this way, the optimal music program can be generated by considering the patient's geographical location information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the patient's geographical location information data into the generation AI and have the generation AI execute the generation of the optimal program.

[0097] The generation unit can analyze the patient's social media activity and suggest a music program when generating it. For example, if the patient posts relaxing content on social media, the generation unit can suggest a relaxing music program. If the patient posts stressful content on social media, the generation unit can also suggest a stress-reducing music program. If the patient posts anxious content on social media, the generation unit can also suggest an anxiety-reducing music program. In this way, by analyzing the patient's social media activity, a more appropriate music program can be suggested. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the patient's social media activity data into the generation AI and have the generation AI execute the program suggestion.

[0098] The service provider can estimate the patient's emotions and adjust the treatment delivery method based on the estimated emotions. For example, if the patient is relaxed, the service provider can provide relaxing music. If the patient is stressed, the service provider can also provide stress-reducing music. If the patient is anxious, the service provider can also provide anxiety-reducing music. This allows for more effective treatment by adjusting the treatment delivery method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI adjust the treatment delivery method.

[0099] The treatment unit can select the optimal treatment method by referring to the patient's past treatment history when providing treatment. For example, the treatment unit can select the optimal treatment method based on the treatment methods the patient has used in the past. The treatment unit can also select an effective treatment method from the patient's past treatment history. The treatment unit can also analyze the patient's past treatment history and select the most effective treatment method. In this way, the optimal treatment method can be selected by referring to the patient's past treatment history. Some or all of the above processing in the treatment unit is performed using a generating AI. For example, the treatment unit can input the patient's past treatment history data into the generating AI and have the generating AI perform the selection of the optimal treatment method.

[0100] The service provider can customize treatment methods based on the patient's current lifestyle when providing treatment. For example, if the patient is at work, the service provider can provide treatment methods to enhance concentration. If the patient is relaxed, the service provider can also provide treatment methods that promote relaxation. If the patient is exercising, the service provider can also provide treatment methods that enhance the effects of exercise. In this way, more effective treatment can be provided by customizing treatment methods based on the patient's current lifestyle. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input patient lifestyle data into the generative AI and have the generative AI perform the customization of treatment methods.

[0101] The service provider can estimate the patient's emotions and determine treatment priorities based on those estimated emotions. For example, if the patient is stressed, the service provider will prioritize treatments that reduce stress. If the patient is relaxed, the service provider can also prioritize treatments that promote relaxation. If the patient is anxious, the service provider can also prioritize treatments that reduce anxiety. By prioritizing treatments based on the patient's emotions, more effective treatments can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider are performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI determine treatment priorities.

[0102] The service provider can select the optimal treatment method by considering the patient's geographical location when providing treatment. For example, if the patient is at home, the service provider can provide a treatment method suitable for the home environment. If the patient is in a hospital, the service provider can also provide a treatment method suitable for the hospital environment. If the patient is traveling, the service provider can also provide a treatment method suitable for the environment of the travel destination. In this way, the optimal treatment method can be selected by considering the patient's geographical location. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's geographical location data into the generative AI and have the generative AI select the optimal treatment method.

[0103] The service provider can analyze a patient's social media activity and propose treatment options when providing treatment. For example, if a patient posts relaxing content on social media, the service provider can propose treatment options with a relaxing effect. If a patient posts stressful content on social media, the service provider can also propose treatment options with a stress-reducing effect. If a patient posts anxious content on social media, the service provider can also propose treatment options with an anxiety-reducing effect. In this way, by analyzing a patient's social media activity, it is possible to propose more appropriate treatment options. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's social media activity data into the generative AI and have the generative AI propose treatment options.

[0104] The service provider can visualize the effects of music therapy using data and optimize treatment plans by estimating the patient's emotions and adjusting the visualization method based on the estimated emotions. For example, if the patient is relaxed, the service provider can visualize the effects with a visually easy-to-understand graph. If the patient is stressed, the service provider can also visualize the effects with concise text. If the patient is anxious, the service provider can also visualize the effects with detailed data. This allows for more easily understandable visualization of effects by adjusting the visualization method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI adjust the visualization method of the effects.

[0105] The service provider can visualize the effects of music therapy using data and predict current effects by referring to past treatment data when optimizing treatment plans. For example, the service provider can predict current treatment effects based on past treatment data. The service provider can also analyze trends in effects from past treatment data and predict current effects. The service provider can also evaluate current treatment effects by referring to past treatment data. This allows the service provider to predict current effects and optimize treatment plans by referring to past treatment data. Some or all of the above processes in the service provider are performed using generative AI. For example, the service provider can input past treatment data into the generative AI and have the generative AI perform predictions of current effects.

[0106] The service provider can visualize the effects of music therapy using data and optimize treatment plans by estimating the patient's emotions and adjusting the importance of effects based on those estimated emotions. For example, if the patient is relaxed, the service provider can increase the importance of relaxation effects. If the patient is stressed, the service provider can also increase the importance of stress reduction effects. If the patient is anxious, the service provider can also increase the importance of anxiety reduction effects. By adjusting the importance of effects based on the patient's emotions, a more effective treatment plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI adjust the importance of effects.

[0107] The service provider can visualize the effects of music therapy using data and analyze changes in effects based on the timing of treatment when optimizing treatment plans. For example, the service provider can analyze changes in effects based on the timing of treatment. The service provider can also analyze trends in effects for each treatment period. The service provider can also predict changes in effects based on the timing of treatment. This allows for the provision of more effective treatment plans by analyzing changes in effects based on the timing of treatment. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input treatment timing data into the generative AI and have the generative AI perform an analysis of changes in effects.

[0108] The service provider can estimate the patient's emotions when supporting online music therapy sessions and adjust the delivery method of the online session based on the estimated emotions. For example, if the patient is relaxed, the service provider can provide relaxing music. If the patient is stressed, the service provider can also provide stress-reducing music. If the patient is anxious, the service provider can also provide anxiety-reducing music. This allows for more effective online sessions by adjusting the delivery method of the online session based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI adjust the delivery method of the online session.

[0109] The service provider can provide the optimal session by referring to the patient's past online treatment history when supporting online music therapy sessions. For example, the service provider can provide the optimal session based on the online treatment sessions the patient has used in the past. The service provider can also select an effective session from the patient's past online treatment history. The service provider can also analyze the patient's past online treatment history and provide the most effective session. In this way, the service provider can provide the optimal online session by referring to the patient's past online treatment history. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's past online treatment history data into the generative AI and have the generative AI perform the task of providing the optimal session.

[0110] The service provider can estimate the patient's emotions when supporting online music therapy sessions and prioritize online sessions based on the estimated emotions. For example, if the patient is stressed, the service provider will prioritize online sessions with stress-reducing effects. If the patient is relaxed, the service provider can also prioritize online sessions with relaxing effects. If the patient is anxious, the service provider can also prioritize online sessions with anxiety-reducing effects. This allows for more effective online sessions by prioritizing them based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI determine the priority of online sessions.

[0111] The service provider can provide an optimal session by considering the patient's device information when supporting online music therapy sessions. For example, if the patient is using a smartphone, the service provider can provide a session optimized for the smartphone. If the patient is using a tablet, the service provider can also provide a session optimized for the tablet. If the patient is using a personal computer, the service provider can also provide a session optimized for the personal computer. In this way, the service provider can provide an optimal online session by considering the patient's device information. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's device information into the generative AI and have the generative AI perform the task of providing an optimal session.

[0112] The service provider can estimate a patient's emotions when integrating with electronic medical records and medical databases, and adjust the data integration method based on the estimated patient emotions. For example, if the patient is relaxed, the service provider can provide a data integration method that promotes relaxation. If the patient is stressed, the service provider can also provide a data integration method that reduces stress. If the patient is anxious, the service provider can also provide a data integration method that reduces anxiety. By adjusting the data integration method based on the patient's emotions, more effective data integration can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI perform the adjustment of the data integration method.

[0113] The service provider can select the optimal integration method by referring to past medical data when integrating with electronic medical records and medical databases. For example, the service provider can select the optimal data integration method based on past medical data. The service provider can also select an effective data integration method from past medical data. The service provider can also analyze past medical data and select the most effective data integration method. In this way, the optimal data integration method can be selected by referring to past medical data. Some or all of the above processing in the service provider is performed using a generation AI. For example, the service provider can input past medical data into the generation AI and have the generation AI perform the selection of the optimal integration method.

[0114] The service provider can estimate a patient's emotions when integrating with electronic medical records and medical databases, and determine the priority of data integration based on the estimated patient emotions. For example, if a patient is stressed, the service provider will prioritize data integration that has a stress-reducing effect. If a patient is relaxed, the service provider can also prioritize data integration that has a relaxing effect. If a patient is anxious, the service provider can also prioritize data integration that has an anxiety-reducing effect. This allows for more effective data integration by determining the priority of data integration based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI determine the priority of data integration.

[0115] The data delivery unit can weight data based on the patient's medical history when integrating with electronic medical records and medical databases. For example, the data delivery unit can weight important data based on the patient's medical history. The data delivery unit can also weight effective data from the patient's medical history. The data delivery unit can analyze the patient's medical history and weight the most important data. This allows for the priority integration of more important data by weighting data based on the patient's medical history. Some or all of the above processing in the data delivery unit is performed using generative AI. For example, the data delivery unit can input patient medical history data into the generative AI and have the generative AI perform the data weighting.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. For example, if the patient is stressed, it can prioritize the analysis of data related to stress reduction. If the patient is relaxed, it can also prioritize the analysis of data related to relaxation effects. If the patient is anxious, it can also prioritize the analysis of data related to anxiety reduction. This allows for more effective analysis by prioritizing analysis based on the patient's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI determine the priority of analysis.

[0118] The service provider can estimate the patient's emotions and adjust the treatment delivery method based on the estimated emotions. For example, if the patient is relaxed, it can provide relaxing music. If the patient is stressed, it can provide stress-reducing music. If the patient is anxious, it can provide anxiety-reducing music. By adjusting the treatment delivery method based on the patient's emotions, more effective treatment can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI adjust the treatment delivery method.

[0119] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, it can collect data when the patient is relaxed to obtain accurate vital signs. It can also avoid collecting data when the patient is stressed and try again later. If the patient is sleeping, it can adjust the timing to collect data after they wake up. This allows for the collection of more accurate data by adjusting the timing of data collection based on the patient's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the data collection unit is performed using generative AI. For example, the data collection unit can input patient emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0120] The generation unit can estimate the patient's emotions and adjust the music program generation method based on the estimated emotions. For example, if the patient is relaxed, it can generate relaxing music. If the patient is stressed, it can also generate stress-reducing music. If the patient is anxious, it can also generate anxiety-reducing music. By adjusting the music program generation method based on the patient's emotions, a more effective music program can be provided. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit can input patient emotion data into the generative AI and have the generative AI adjust the music program generation method.

[0121] The service provider can estimate the patient's emotions and determine treatment priorities based on those estimated emotions. For example, if the patient is stressed, treatments that reduce stress will be prioritized. If the patient is relaxed, treatments that promote relaxation may be prioritized. If the patient is anxious, treatments that reduce anxiety may be prioritized. By prioritizing treatments based on the patient's emotions, more effective treatments can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the service provider are performed using generative AI. For example, the service provider can input patient emotion data into the generative AI and have the generative AI determine treatment priorities.

[0122] The data collection unit can analyze a patient's past treatment history and select the optimal data collection method. For example, it can select the best method based on data collection methods the patient has used in the past. It can also determine whether a particular data collection method is effective based on the patient's past treatment history. It can also analyze the patient's past treatment history and adjust the frequency of data collection. In this way, the optimal data collection method can be selected by analyzing the patient's past treatment history. Some or all of the above processing in the data collection unit is performed using a generative AI. For example, the data collection unit can input the patient's past treatment history data into the generative AI and have the generative AI select the optimal data collection method.

[0123] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0124] The treatment unit can select the optimal treatment method by referring to the patient's past treatment history when providing treatment. For example, it can select the optimal treatment method based on the treatment methods the patient has used in the past. It can also select an effective treatment method from the patient's past treatment history. It can also analyze the patient's past treatment history and select the most effective treatment method. In this way, the optimal treatment method can be selected by referring to the patient's past treatment history. Some or all of the above processing in the treatment unit is performed using a generation AI. For example, the treatment unit can input the patient's past treatment history data into the generation AI and have the generation AI perform the selection of the optimal treatment method.

[0125] The service provider can select the optimal treatment method by considering the patient's geographical location when providing treatment. For example, if the patient is at home, it can provide a treatment method suitable for the home environment. If the patient is in a hospital, it can also provide a treatment method suitable for the hospital environment. If the patient is traveling, it can also provide a treatment method suitable for the environment of the travel destination. In this way, the optimal treatment method can be selected by considering the patient's geographical location. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's geographical location data into the generative AI and have the generative AI select the optimal treatment method.

[0126] The service provider can provide an optimal online music therapy session by considering the patient's device information. For example, if the patient is using a smartphone, it can provide a session optimized for smartphones. If the patient is using a tablet, it can also provide a session optimized for tablets. If the patient is using a personal computer, it can also provide a session optimized for personal computers. This allows for the provision of the most optimal online session by considering the patient's device information. Some or all of the above processing in the service provider is performed using a generative AI. For example, the service provider can input the patient's device information into the generative AI and have the generative AI execute the provision of the optimal session.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects the patient's vital signs and interview results. For example, it collects vital signs such as heart rate, blood pressure, and body temperature, as well as interview results such as the patient's chief complaint, medical history, and lifestyle. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes data trends and correlations using statistical analysis and machine learning algorithms. Step 3: The generation unit generates a music program based on the analysis results obtained by the analysis unit. For example, it generates music with relaxing, sleep-inducing, and stress-reducing effects, taking into account the genre of music, song length, tempo, etc. Step 4: The provider unit provides treatment based on the music program generated by the generator unit. For example, the provider unit provides treatment considering the frequency, duration, and method of music therapy sessions.

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the patient's vital signs and interview results using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a music program based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides treatment based on the generated music program. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the patient's vital signs and interview results using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates a music program based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides treatment based on the generated music program. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the patient's vital signs and interview results using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a music program based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides treatment based on the generated music program. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the patient's vital signs and interview results using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a music program based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides treatment based on the generated music program. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A collection unit that collects patients' vital signs and interview results, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a music program based on the analysis results obtained by the analysis unit, A providing unit that provides treatment based on the music program generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned supply unit is, Visualize the effects of music therapy with data and optimize treatment plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Supporting online music therapy sessions The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Integrate with electronic medical records and medical databases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect data tailored to the patient's preferences and symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze the collected data and propose the most suitable music therapy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past treatment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the patient's emotions and adjusts the music program generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a music program, the program is optimized by referencing the patient's past music therapy history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a music program, customize the program based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the patient's emotions and prioritizes the music program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a music program, the program is optimized by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating a music program, the program is proposed by analyzing the patient's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the patient's emotions and adjusts the treatment delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing treatment, the optimal treatment method is selected by referring to the patient's past treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing treatment, customize the treatment plan based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the patient's emotions and determines treatment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing treatment, the optimal treatment method will be selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing treatment, we analyze the patient's social media activity and propose treatment options. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When visualizing the effects of music therapy with data and optimizing treatment plans, the patient's emotions are estimated, and the method of visualizing the effects is adjusted based on the estimated patient emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supply unit is, Visualizing the effects of music therapy with data and optimizing treatment plans allows us to predict current effects by referring to past treatment data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When visualizing the effects of music therapy with data and optimizing treatment plans, the patient's emotions are estimated, and the importance of the effects is adjusted based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When visualizing the effects of music therapy with data and optimizing treatment plans, we analyze how the effects change based on the timing of treatment. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When supporting online music therapy sessions, we estimate the patient's emotions and adjust the way the online session is delivered based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When supporting online music therapy sessions, we refer to the patient's past online treatment history to provide the most suitable session. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When supporting online music therapy sessions, we estimate the patient's emotions and prioritize online sessions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When supporting online music therapy sessions, we take the patient's device information into consideration to provide the optimal session. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When integrating with electronic medical records and medical databases, the system estimates patient emotions and adjusts the data integration method based on these estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When integrating with electronic medical records and medical databases, the optimal integration method is selected by referring to past medical data. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned supply unit is, When integrating with electronic medical records and medical databases, patient emotions are estimated, and data integration priorities are determined based on these estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When integrating with electronic medical records and medical databases, data is weighted based on the patient's medical history. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects patients' vital signs and interview results, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a music program based on the analysis results obtained by the analysis unit, A providing unit that provides treatment based on the music program generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned supply unit is, Visualize the effects of music therapy with data and optimize treatment plans. The system according to feature 1.

3. The aforementioned supply unit is, Supporting online music therapy sessions The system according to feature 1.

4. The aforementioned supply unit is, Integrate with electronic medical records and medical databases. The system according to feature 1.

5. The aforementioned collection unit is Collect data tailored to the patient's preferences and symptoms. The system according to feature 1.

6. The aforementioned analysis unit, We analyze the collected data and propose the most suitable music therapy. The system according to feature 1.

7. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the patient's past treatment history and select the optimal data collection method. The system according to feature 1.

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