system

Abstract

The system according to this embodiment aims to support the maintenance of motivation for continuing long-term treatment and the resolution of questions related to treatment. [Solution] The system according to the embodiment comprises an alert unit, a monitoring unit, a motivation maintenance unit, a question resolution unit, and a conversation unit. The alert unit provides medication reminders. The monitoring unit monitors treatment progress based on the alerts provided by the alert unit. The motivation maintenance unit maintains motivation based on the treatment progress monitored by the monitoring unit. The question resolution unit resolves questions about treatment based on the motivation maintained by the motivation maintenance unit. The conversation unit facilitates treatment through conversation with a generated AI based on the questions resolved by the question resolution 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 in 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, motivation maintenance for continuing long-term treatment and problem-solving regarding treatment have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to assist in maintaining motivation for continuing long-term treatment and solving problems regarding treatment.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an alert unit, a monitoring unit, a motivation maintenance unit, a question resolution unit, and a conversation unit. The alert unit provides medication reminders. The monitoring unit monitors treatment progress based on the alerts provided by the alert unit. The motivation maintenance unit maintains motivation based on the treatment progress monitored by the monitoring unit. The question resolution unit resolves questions about treatment based on the motivation maintained by the motivation maintenance unit. The conversation unit facilitates treatment through conversation with a generating AI based on the questions resolved by the question resolution unit. [Effects of the Invention]

[0007] The system according to this embodiment can support the maintenance of motivation for continuing long-term treatment and the resolution of questions related to treatment. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 expressing three or more matters connected 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 reception 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 reception 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 mentor AI system according to an embodiment of the present invention is a system that supports patients who continue sublingual immunotherapy for cedar pollen allergy for more than three years. This mentor AI system provides patients with medication reminders, maintains their motivation, and resolves questions about their treatment. It also creates an environment that naturally promotes the continuation of treatment through daily conversations with the generating AI. For example, the mentor AI system provides patients with medication reminders. In this case, the mentor AI system manages the patient's medication schedule and sends alerts at the appropriate time. For example, it sends alerts at a fixed time every day to encourage the patient to take their medication. Next, the mentor AI system maintains the patient's motivation. The mentor AI system monitors the patient's treatment progress and provides appropriate feedback. For example, it displays the treatment progress in a graph so that the patient can feel the effects of the treatment. Also, if the patient has questions about the treatment, the mentor AI system provides appropriate answers to those questions. For example, the mentor AI system provides detailed information in response to questions about the effects and side effects of the treatment. Furthermore, it creates an environment that naturally promotes the continuation of treatment through daily conversations with the generating AI. The generating AI increases the patient's motivation for treatment through conversations with the patient. For example, if a patient feels anxious about treatment, the generating AI will provide advice to alleviate that anxiety. The generating AI will also provide advice on the patient's lifestyle. For example, it will provide advice on diet, exercise, and stress management to support the patient in leading a healthy life. This makes it easier for patients to continue treatment and improves the success rate of long-term treatment. For example, by continuing treatment, patients may experience a reduction in the symptoms of cedar pollen allergy and an improvement in their quality of life. Furthermore, patients continuing treatment also leads to a reduction in medical costs. For example, by continuing treatment without interruption, patients can prevent their condition from worsening and keep medical costs down. In this way, the mentor AI system can make it easier for patients to continue treatment and contribute to improving the success rate of long-term treatment.

[0029] The mentor AI system according to this embodiment comprises an alert unit, a monitoring unit, a motivation maintenance unit, a question-solving unit, and a conversation unit. The alert unit provides medication reminders to the patient. The alert unit manages the patient's medication schedule and sends alerts at appropriate times. For example, the alert unit sends alerts at a fixed time each day to encourage the patient to take their medication. The alert unit can also manage the patient's medication schedule and send alerts at appropriate times. For example, the alert unit can manage the patient's medication schedule and send alerts at appropriate times. The monitoring unit monitors the progress of treatment based on the alerts provided by the alert unit. The monitoring unit can, for example, display the progress of treatment in a graph so that the patient can feel the effects of the treatment. For example, the monitoring unit can display the progress of treatment in a graph so that the patient can feel the effects of the treatment. The monitoring unit can also display the progress of treatment in a graph so that the patient can feel the effects of the treatment. The motivation maintenance unit maintains motivation based on the treatment progress monitored by the monitoring unit. The motivation maintenance unit, for example, monitors the patient's treatment progress and provides appropriate feedback. For example, the motivation maintenance unit displays treatment progress in a graph so that the patient can feel the effects of the treatment. The motivation maintenance unit can also monitor the patient's treatment progress and provide appropriate feedback. The question resolution unit resolves questions about treatment based on the motivation maintained by the motivation maintenance unit. For example, the question resolution unit provides detailed information in response to questions about the effects and side effects of treatment. For example, the question resolution unit provides detailed information in response to questions about the effects and side effects of treatment. The question resolution unit can also provide detailed information in response to questions about the effects and side effects of treatment. The conversation unit facilitates treatment through conversation with a generated AI based on the questions resolved by the question resolution unit. For example, the conversation unit increases motivation for treatment through conversation with the patient. For example, the conversation unit increases motivation for treatment through conversation with the patient.Furthermore, the conversational component can enhance the patient's motivation for treatment through conversation. This allows the mentor AI system according to this embodiment to make it easier for patients to continue treatment and contribute to improving the success rate of long-term treatment.

[0030] The alert unit provides medication reminders to patients. For example, the alert unit manages the patient's medication schedule and sends alerts at the appropriate time. Specifically, the alert unit has the function of registering the patient's medication schedule in a database and sending alerts at set times. Alerts are provided in multiple ways, such as smartphone notifications, email, and voice alerts. For example, it can send a smartphone notification at a fixed time every day to encourage the patient to take their medication. The alert unit can also record the patient's medication history and send reminders if a dose is missed. Furthermore, the alert unit can adjust the timing of alerts according to the patient's lifestyle and specific events. For example, if the patient is traveling or has a specific event, the timing of the alert can be changed to ensure that the patient does not forget to take their medication. In this way, the alert unit can support patients in taking their medication at the appropriate time and maximize the effectiveness of treatment.

[0031] The monitoring unit monitors treatment progress based on alerts provided by the alert unit. For example, the monitoring unit displays treatment progress in graphs, allowing patients to see the effects of their treatment. Specifically, the monitoring unit collects the patient's medication history and treatment data and displays it visually. For instance, it can display treatment progress in line graphs or bar graphs, allowing patients to see the effects of their treatment at a glance. The monitoring unit also monitors changes in the patient's vital signs and symptoms in real time and can issue alerts if abnormalities are detected. Furthermore, the monitoring unit evaluates the effectiveness of treatment by comparing it with past data and provides feedback to the patient. For example, comparing pre-treatment and current data and quantitatively demonstrating the treatment's effectiveness can increase patient motivation. In this way, the monitoring unit supports patients in understanding their treatment progress and experiencing its effects.

[0032] The Motivation Maintenance Unit maintains motivation based on treatment progress monitored by the Monitoring Unit. For example, the Motivation Maintenance Unit monitors the patient's treatment progress and provides appropriate feedback. Specifically, the Motivation Maintenance Unit has the function of analyzing the patient's treatment progress data and sending positive feedback and encouraging messages. For example, it can display treatment progress in a graph so that the patient can feel the effects of the treatment. The Motivation Maintenance Unit can also set treatment goals for the patient and track progress toward achieving those goals. When goals are achieved, it can provide messages and badges to enhance the sense of accomplishment. Furthermore, the Motivation Maintenance Unit can also provide information about the importance and effectiveness of the treatment to increase the patient's motivation for treatment. For example, by providing detailed information about the effects and side effects of the treatment, the patient can understand the significance of the treatment and maintain their motivation for treatment. In this way, the Motivation Maintenance Unit can make it easier for patients to continue treatment and improve the success rate of treatment.

[0033] The Question Resolution Unit resolves treatment-related questions based on the motivation maintained by the Motivation Maintenance Unit. For example, the Question Resolution Unit provides detailed information in response to questions about the effectiveness and side effects of treatment. Specifically, the Question Resolution Unit receives questions from patients and uses AI to generate appropriate answers. For example, if a patient asks about the effectiveness or side effects of treatment, the Question Resolution Unit searches for relevant medical information and provides an answer in an easy-to-understand format. The Question Resolution Unit can also refer to the patient's past question history and provide answers to similar questions. Furthermore, the Question Resolution Unit can use diagrams and illustrations to explain things in a way that is easy for patients to understand. For example, it can explain the mechanism of treatment and the mechanism of side effects using diagrams, allowing patients to deepen their understanding of the treatment. In this way, the Question Resolution Unit can quickly and accurately resolve patients' questions about treatment and reduce their anxiety about treatment.

[0034] The conversation unit facilitates treatment through conversations with a generating AI based on questions resolved by the question-solving unit. For example, the conversation unit increases the patient's motivation for treatment through conversations with them. Specifically, the conversation unit has the function of enabling natural conversations with patients using a generating AI. For example, if a patient has anxieties or questions about treatment, the conversation unit uses a generating AI to empathize with the patient's feelings and provide appropriate advice and words of encouragement. The conversation unit can also provide individually customized conversations based on the patient's treatment progress and goals. For example, if a patient achieves their treatment goals, the conversation unit uses a generating AI to send a congratulatory message to enhance the patient's sense of accomplishment. Furthermore, the conversation unit can collect patient feedback and use it to improve the content of conversations. In this way, the conversation unit can build trust with patients, maintain their motivation for treatment, and improve the success rate of treatment.

[0035] The alert unit can manage the patient's medication schedule and send alerts at appropriate times. For example, the alert unit can send an alert at a fixed time each day to encourage the patient to take their medication. The alert unit can also manage the patient's medication schedule and send alerts at appropriate times. For example, the alert unit can manage the patient's medication schedule and send alerts at appropriate times. This encourages the patient to take their medication at the appropriate time. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the patient's medication schedule into the AI, which can then send an alert at an appropriate time.

[0036] The monitoring unit can display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. For example, the monitoring unit can display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. For example, the monitoring unit can display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. The monitoring unit can also display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. This allows patients to visually confirm the effects of the treatment. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the progress of treatment into AI, which can then display it in a graph.

[0037] The question-solving unit can provide detailed information in response to questions about the effectiveness and side effects of treatment. For example, the question-solving unit can provide detailed information in response to questions about the effectiveness and side effects of treatment. Furthermore, the question-solving unit can also provide detailed information in response to questions about the effectiveness and side effects of treatment. This allows patients to resolve their questions about treatment. Some or all of the above-described processes in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input questions about the effectiveness and side effects of treatment into AI, which can then provide detailed information.

[0038] The conversation unit can increase the patient's motivation for treatment through conversation. The conversation unit can increase the patient's motivation for treatment through conversation. For example, the conversation unit can increase the patient's motivation for treatment through conversation. The conversation unit can also increase the patient's motivation for treatment through conversation. This improves the patient's motivation for treatment. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input a conversation with the patient into a generative AI, which can then conduct a conversation that increases the patient's motivation for treatment.

[0039] The conversation unit can provide advice on the patient's lifestyle. For example, the conversation unit can provide advice on the patient's lifestyle. For example, the conversation unit can provide advice on the patient's lifestyle. The conversation unit can also provide advice on the patient's lifestyle. This supports the patient in leading a healthy life. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input advice on the patient's lifestyle into a generative AI, and the generative AI can provide the advice.

[0040] The alert unit can analyze the patient's past medication history and select the optimal alert method. For example, the alert unit can send alerts while avoiding times when the patient has previously ignored alerts. For example, the alert unit can analyze the patient's response to past alerts and select the most effective alert method. The alert unit can also analyze the patient's behavioral patterns when receiving past alerts and send alerts at the optimal timing. This promotes continued treatment by providing the patient with the most suitable alert method. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the patient's past medication history into AI, which can then select the optimal alert method.

[0041] The alert unit can customize alerts based on the patient's current lifestyle and activity level when sending them. For example, if the patient is at work, the alert unit can send an alert with a quiet vibration. For example, if the patient is exercising, the alert unit can send an audio alert to encourage the patient to take their medication without interrupting their exercise. The alert unit can also send an alert after the patient wakes up if they are sleeping. This promotes the continuation of treatment by providing alerts tailored to the patient's lifestyle and activity level. Some or all of the above processing in the alert unit may be performed using AI, for example, or not. For example, the alert unit can input the patient's lifestyle and activity level into the AI, which can then customize the alerts.

[0042] The alert unit can prioritize sending highly relevant alerts by considering the patient's geographical location when sending alerts. For example, the alert unit sends a medication reminder if the patient is at home. For example, if the patient is out, the alert unit sends a medication reminder and suggests medications to carry with them. The alert unit can also send alerts tailored to the time zone of the patient's travel destination if the patient is traveling. This promotes continued treatment by providing alerts based on the patient's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the patient's geographical location into the AI, which can then prioritize sending highly relevant alerts.

[0043] The alert unit can analyze the patient's social media activity and send relevant alerts when sending an alert. For example, if the patient is experiencing stress on social media, the alert unit can send an alert containing a relaxing message. For example, if the patient is active on social media, the alert unit can send an alert containing an encouraging message. The alert unit can also send relevant alerts if the patient is searching for information about their treatment on social media. This promotes treatment adherence by providing alerts based on the patient's social media activity. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input the patient's social media activity into AI, which can then send relevant alerts.

[0044] The monitoring unit can display the progress of treatment in real time during monitoring, allowing patients to immediately confirm the effectiveness of the treatment. For example, the monitoring unit displays the progress of treatment in real time when the patient opens the app. For example, the monitoring unit displays the progress in real time when the patient wants to check the effectiveness of the treatment. The monitoring unit can also display the progress periodically in real time for the patient to check the progress of treatment. This allows patients to confirm the effectiveness of the treatment in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the progress of treatment into the AI, which can then display it in real time.

[0045] The monitoring unit can evaluate the progress of treatment by considering the patient's lifestyle and activity data during monitoring. For example, the monitoring unit can evaluate the progress of treatment by considering the patient's exercise level. For example, the monitoring unit can evaluate the progress of treatment by considering the patient's diet. Furthermore, the monitoring unit can also evaluate the progress of treatment by considering the patient's sleep patterns. This makes it possible to evaluate the progress of treatment based on the patient's lifestyle and activity data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's lifestyle and activity data into AI, and the AI ​​can evaluate the progress of treatment.

[0046] The monitoring unit can evaluate the progress of treatment while considering the geographical distribution of patients during monitoring. For example, if a patient lives in an urban area, the monitoring unit evaluates the progress of treatment while considering environmental factors specific to urban areas. For example, if a patient lives in a rural area, the monitoring unit evaluates the progress of treatment while considering environmental factors specific to rural areas. Furthermore, if a patient lives overseas, the monitoring unit can evaluate the progress of treatment while considering environmental factors of that region. This makes it possible to evaluate the progress of treatment based on the geographical distribution of patients. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of patients into the AI, and the AI ​​can evaluate the progress of treatment.

[0047] The monitoring unit can evaluate the progress of treatment by referring to relevant literature on the patient during monitoring. For example, the monitoring unit can evaluate progress by referring to the latest research papers on the patient's treatment. For example, the monitoring unit can evaluate progress by referring to past literature on the patient's treatment. The monitoring unit can also evaluate progress by referring to professional books on the patient's treatment. This makes it possible to evaluate the progress of treatment based on relevant literature on the patient. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature on the patient into the AI, and the AI ​​can evaluate the progress of treatment.

[0048] The motivation maintenance unit can analyze the patient's past treatment history to provide optimal feedback when maintaining motivation. For example, the motivation maintenance unit can provide feedback based on treatment methods the patient has successfully used in the past. For example, the motivation maintenance unit can provide feedback to help the patient avoid treatment methods that have failed in the past. The motivation maintenance unit can also analyze the patient's past treatment history and provide the most effective feedback. This promotes the continuation of treatment by providing optimal feedback based on the patient's past treatment history. Some or all of the above processes in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the patient's past treatment history into AI, and the AI ​​can provide optimal feedback.

[0049] The motivation maintenance unit can customize motivation maintenance methods based on the patient's current living situation. For example, if the patient is busy, the motivation maintenance unit can provide effective motivation maintenance methods in a short amount of time. For example, if the patient is relaxed, the motivation maintenance unit can provide detailed motivation maintenance methods. Furthermore, if the patient is stressed, the motivation maintenance unit can also provide motivation maintenance methods that promote relaxation. This promotes the continuation of treatment by providing motivation maintenance methods that are tailored to the patient's current living situation. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input patient living situation data into AI, and the AI ​​can customize the motivation maintenance methods.

[0050] The motivation maintenance unit can select the optimal motivation maintenance method by considering the patient's geographical location information when maintaining motivation. For example, if the patient is at home, the motivation maintenance unit provides motivation maintenance methods that can be done at home. For example, if the patient is out, the motivation maintenance unit provides motivation maintenance methods that can be done while out. Furthermore, if the patient is traveling, the motivation maintenance unit can also provide motivation maintenance methods that can be done at the travel destination. By providing motivation maintenance methods based on the patient's geographical location information, the unit promotes the continuation of treatment. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the patient's geographical location information into the AI, and the AI ​​can select the optimal motivation maintenance method.

[0051] The motivation maintenance unit can analyze the patient's social media activity and suggest ways to maintain motivation during the motivation maintenance process. For example, if the patient is searching for information about their treatment on social media, the motivation maintenance unit can provide relevant motivation maintenance methods. For example, if the patient is feeling stressed on social media, the motivation maintenance unit can provide relaxing motivation maintenance methods. The motivation maintenance unit can also send encouraging messages if the patient is active on social media. This promotes the continuation of treatment by providing motivation maintenance methods based on the patient's social media activity. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the patient's social media activity into AI, which can then suggest ways to maintain motivation.

[0052] The question-solving unit can provide the optimal answer by analyzing the patient's past question history when resolving a question. For example, the question-solving unit can provide relevant answers based on the questions the patient has asked in the past. For example, the question-solving unit can provide the optimal answer by referring to the answers the patient has received in the past. The question-solving unit can also analyze the patient's past question history and provide the most effective answer. This promotes the continuation of treatment by providing the optimal answer based on the patient's past question history. Some or all of the above processing in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input the patient's past question history into AI, and the AI ​​can provide the optimal answer.

[0053] The question-solving unit can customize its question-solving methods based on the patient's current living situation. For example, if the patient is busy, the question-solving unit can provide a quick and effective answer. For example, if the patient is relaxed, the question-solving unit can provide a detailed answer. Furthermore, if the patient is stressed, the question-solving unit can provide an answer that includes advice to help them relax. This promotes the continuation of treatment by providing question-solving methods tailored to the patient's current living situation. Some or all of the above-described processes in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input patient living situation data into AI, which can then customize the question-solving methods.

[0054] The question-solving unit can select the optimal question-solving method when resolving a question, taking into account the patient's geographical location information. For example, if the patient is at home, the question-solving unit provides a question-solving method that can be done at home. For example, if the patient is out, the question-solving unit provides a question-solving method that can be done at their destination. Furthermore, if the patient is traveling, the question-solving unit can also provide a question-solving method that can be done at their travel destination. This promotes the continuation of treatment by providing question-solving methods based on the patient's geographical location information. Some or all of the above processing in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input the patient's geographical location information into the AI, which can then select the optimal question-solving method.

[0055] The question-solving unit can analyze the patient's social media activity and propose solutions when resolving questions. For example, if the patient is searching for information about their treatment on social media, the question-solving unit will provide relevant solutions. For example, if the patient is experiencing stress on social media, the question-solving unit will provide relaxing solutions. The question-solving unit can also send encouraging messages if the patient is active on social media. This promotes the continuation of treatment by providing solutions based on the patient's social media activity. Some or all of the above processing in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input the patient's social media activity into AI, which can then propose solutions.

[0056] The conversation unit can analyze the patient's past conversation history during a conversation to provide optimal conversation content. For example, the conversation unit can provide relevant conversation content based on what the patient has said in the past. For example, the conversation unit can provide optimal conversation content by referring to advice the patient has received in the past. The conversation unit can also analyze the patient's past conversation history to provide the most effective conversation content. This promotes the continuation of treatment by providing optimal conversation content based on the patient's past conversation history. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the patient's past conversation history into a generative AI, which can then provide optimal conversation content.

[0057] The conversation unit can customize the content of conversations based on the patient's current living situation. For example, if the patient is busy, the conversation unit can provide concise and effective conversation content. For example, if the patient is relaxed, the conversation unit can provide detailed conversation content. Furthermore, if the patient is stressed, the conversation unit can provide conversation content that includes relaxation advice. This promotes the continuation of treatment by providing conversation content tailored to the patient's current living situation. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input patient living situation data into a generative AI, which can then customize the conversation content.

[0058] The conversation unit can provide optimal conversation content while considering the patient's geographical location. For example, if the patient is at home, the conversation unit can provide advice that can be done at home. For example, if the patient is out, the conversation unit can provide advice that can be done while out. Furthermore, if the patient is traveling, the conversation unit can provide advice that can be done at their travel destination. This promotes the continuation of treatment by providing conversation content based on the patient's geographical location. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the patient's geographical location information into a generative AI, which can then provide optimal conversation content.

[0059] The conversation unit can analyze the patient's social media activity during a conversation and suggest conversation topics. For example, if the patient is searching for information about their treatment on social media, the conversation unit will provide relevant conversation topics. For example, if the patient is experiencing stress on social media, the conversation unit will provide relaxing conversation topics. The conversation unit can also send encouraging messages if the patient is active on social media. This promotes continued treatment by providing conversation topics based on the patient's social media activity. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversation unit can input the patient's social media activity into a generative AI, which can then suggest conversation topics.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The mentor AI system can monitor a patient's treatment progress and evaluate it by considering the patient's lifestyle data. For example, it can collect data such as the patient's exercise level, diet, and sleep patterns, and evaluate treatment progress based on this data. This enables treatment progress evaluation based on the patient's lifestyle, allowing for more accurate feedback. Furthermore, by analyzing the patient's lifestyle data, it can provide advice to maximize the effectiveness of treatment. For example, it can advise patients with low exercise levels to engage in moderate exercise.

[0062] The mentor AI system can evaluate treatment progress by considering the patient's geographical location when monitoring the patient's treatment progress. For example, if the patient lives in an urban area, the system can evaluate treatment progress by considering environmental factors specific to urban areas. Similarly, if the patient lives in a rural area, the system can evaluate treatment progress by considering environmental factors specific to rural areas. Furthermore, if the patient lives overseas, the system can evaluate treatment progress by considering environmental factors of that region. This enables treatment progress evaluation based on the patient's geographical location, allowing for more accurate feedback.

[0063] The mentor AI system can monitor a patient's treatment progress by referencing relevant literature. For example, it can evaluate progress by referring to the latest research papers on the patient's treatment. It can also evaluate progress by referring to past literature on the patient's treatment. Furthermore, it can evaluate progress by referring to specialized books on the patient's treatment. This enables treatment progress evaluation based on relevant literature on the patient, allowing for more accurate feedback.

[0064] The mentor AI system can monitor a patient's treatment progress, displaying treatment status in real time and allowing patients to immediately see the effectiveness of their treatment. For example, when a patient opens the app, their treatment progress can be displayed in real time. Patients can also view their progress in real time if they wish to check the effectiveness of their treatment. Furthermore, the system can display progress periodically in real time for patients to review. This allows patients to see the effectiveness of their treatment in real time, encouraging them to continue treatment.

[0065] The mentor AI system can analyze a patient's past treatment history to provide optimal feedback when monitoring their treatment progress. For example, it can provide feedback based on treatment methods the patient has successfully used in the past. It can also provide feedback to help the patient avoid treatment methods that have failed in the past. Furthermore, it can analyze the patient's past treatment history to provide the most effective feedback. This allows for better treatment adherence by providing optimal feedback based on the patient's past treatment history.

[0066] The mentor AI system can analyze a patient's social media activity and suggest monitoring methods when monitoring the patient's treatment progress. For example, if a patient is searching for information about their treatment on social media, it can provide relevant monitoring methods. It can also provide relaxing monitoring methods if a patient is experiencing stress from social media. Furthermore, if a patient is active on social media, it can provide monitoring methods that include encouraging messages. By providing monitoring methods based on the patient's social media activity, it can promote treatment adherence.

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

[0068] Step 1: The alert unit provides medication reminders to the patient. The alert unit manages the patient's medication schedule and sends alerts at appropriate times. For example, the alert unit sends alerts at a set time each day to encourage the patient to take their medication. Step 2: The monitoring unit monitors treatment progress based on alerts provided by the alert unit. The monitoring unit, for example, displays treatment progress in a graph so that patients can see the effects of the treatment. Step 3: The Motivation Maintenance Unit maintains motivation based on the treatment progress monitored by the Monitoring Unit. For example, the Motivation Maintenance Unit monitors the patient's treatment progress and provides appropriate feedback. Step 4: The Question Resolution Department resolves questions about the treatment based on the motivation maintained by the Motivation Maintenance Department. For example, the Question Resolution Department provides detailed information in response to questions about the effectiveness or side effects of the treatment. Step 5: The conversation unit facilitates treatment through conversations with the generated AI based on the questions resolved by the question-solving unit. For example, the conversation unit increases the patient's motivation for treatment through conversations with them.

[0069] (Example of form 2) The mentor AI system according to an embodiment of the present invention is a system that supports patients who continue sublingual immunotherapy for cedar pollen allergy for more than three years. This mentor AI system provides patients with medication reminders, maintains their motivation, and resolves questions about their treatment. It also creates an environment that naturally promotes the continuation of treatment through daily conversations with the generating AI. For example, the mentor AI system provides patients with medication reminders. In this case, the mentor AI system manages the patient's medication schedule and sends alerts at the appropriate time. For example, it sends alerts at a fixed time every day to encourage the patient to take their medication. Next, the mentor AI system maintains the patient's motivation. The mentor AI system monitors the patient's treatment progress and provides appropriate feedback. For example, it displays the treatment progress in a graph so that the patient can feel the effects of the treatment. Also, if the patient has questions about the treatment, the mentor AI system provides appropriate answers to those questions. For example, the mentor AI system provides detailed information in response to questions about the effects and side effects of the treatment. Furthermore, it creates an environment that naturally promotes the continuation of treatment through daily conversations with the generating AI. The generating AI increases the patient's motivation for treatment through conversations with the patient. For example, if a patient feels anxious about treatment, the generating AI will provide advice to alleviate that anxiety. The generating AI will also provide advice on the patient's lifestyle. For example, it will provide advice on diet, exercise, and stress management to support the patient in leading a healthy life. This makes it easier for patients to continue treatment and improves the success rate of long-term treatment. For example, by continuing treatment, patients may experience a reduction in the symptoms of cedar pollen allergy and an improvement in their quality of life. Furthermore, patients continuing treatment also leads to a reduction in medical costs. For example, by continuing treatment without interruption, patients can prevent their condition from worsening and keep medical costs down. In this way, the mentor AI system can make it easier for patients to continue treatment and contribute to improving the success rate of long-term treatment.

[0070] The mentor AI system according to this embodiment comprises an alert unit, a monitoring unit, a motivation maintenance unit, a question-solving unit, and a conversation unit. The alert unit provides medication reminders to the patient. The alert unit manages the patient's medication schedule and sends alerts at appropriate times. For example, the alert unit sends alerts at a fixed time each day to encourage the patient to take their medication. The alert unit can also manage the patient's medication schedule and send alerts at appropriate times. For example, the alert unit can manage the patient's medication schedule and send alerts at appropriate times. The monitoring unit monitors the progress of treatment based on the alerts provided by the alert unit. The monitoring unit can, for example, display the progress of treatment in a graph so that the patient can feel the effects of the treatment. For example, the monitoring unit can display the progress of treatment in a graph so that the patient can feel the effects of the treatment. The monitoring unit can also display the progress of treatment in a graph so that the patient can feel the effects of the treatment. The motivation maintenance unit maintains motivation based on the treatment progress monitored by the monitoring unit. The motivation maintenance unit, for example, monitors the patient's treatment progress and provides appropriate feedback. For example, the motivation maintenance unit displays treatment progress in a graph so that the patient can feel the effects of the treatment. The motivation maintenance unit can also monitor the patient's treatment progress and provide appropriate feedback. The question resolution unit resolves questions about treatment based on the motivation maintained by the motivation maintenance unit. For example, the question resolution unit provides detailed information in response to questions about the effects and side effects of treatment. For example, the question resolution unit provides detailed information in response to questions about the effects and side effects of treatment. The question resolution unit can also provide detailed information in response to questions about the effects and side effects of treatment. The conversation unit facilitates treatment through conversation with a generated AI based on the questions resolved by the question resolution unit. For example, the conversation unit increases motivation for treatment through conversation with the patient. For example, the conversation unit increases motivation for treatment through conversation with the patient.Furthermore, the conversational component can enhance the patient's motivation for treatment through conversation. This allows the mentor AI system according to this embodiment to make it easier for patients to continue treatment and contribute to improving the success rate of long-term treatment.

[0071] The alert unit provides medication reminders to patients. For example, the alert unit manages the patient's medication schedule and sends alerts at the appropriate time. Specifically, the alert unit has the function of registering the patient's medication schedule in a database and sending alerts at set times. Alerts are provided in multiple ways, such as smartphone notifications, email, and voice alerts. For example, it can send a smartphone notification at a fixed time every day to encourage the patient to take their medication. The alert unit can also record the patient's medication history and send reminders if a dose is missed. Furthermore, the alert unit can adjust the timing of alerts according to the patient's lifestyle and specific events. For example, if the patient is traveling or has a specific event, the timing of the alert can be changed to ensure that the patient does not forget to take their medication. In this way, the alert unit can support patients in taking their medication at the appropriate time and maximize the effectiveness of treatment.

[0072] The monitoring unit monitors treatment progress based on alerts provided by the alert unit. For example, the monitoring unit displays treatment progress in graphs, allowing patients to see the effects of their treatment. Specifically, the monitoring unit collects the patient's medication history and treatment data and displays it visually. For instance, it can display treatment progress in line graphs or bar graphs, allowing patients to see the effects of their treatment at a glance. The monitoring unit also monitors changes in the patient's vital signs and symptoms in real time and can issue alerts if abnormalities are detected. Furthermore, the monitoring unit evaluates the effectiveness of treatment by comparing it with past data and provides feedback to the patient. For example, comparing pre-treatment and current data and quantitatively demonstrating the treatment's effectiveness can increase patient motivation. In this way, the monitoring unit supports patients in understanding their treatment progress and experiencing its effects.

[0073] The Motivation Maintenance Unit maintains motivation based on treatment progress monitored by the Monitoring Unit. For example, the Motivation Maintenance Unit monitors the patient's treatment progress and provides appropriate feedback. Specifically, the Motivation Maintenance Unit has the function of analyzing the patient's treatment progress data and sending positive feedback and encouraging messages. For example, it can display treatment progress in a graph so that the patient can feel the effects of the treatment. The Motivation Maintenance Unit can also set treatment goals for the patient and track progress toward achieving those goals. When goals are achieved, it can provide messages and badges to enhance the sense of accomplishment. Furthermore, the Motivation Maintenance Unit can also provide information about the importance and effectiveness of the treatment to increase the patient's motivation for treatment. For example, by providing detailed information about the effects and side effects of the treatment, the patient can understand the significance of the treatment and maintain their motivation for treatment. In this way, the Motivation Maintenance Unit can make it easier for patients to continue treatment and improve the success rate of treatment.

[0074] The Question Resolution Unit resolves treatment-related questions based on the motivation maintained by the Motivation Maintenance Unit. For example, the Question Resolution Unit provides detailed information in response to questions about the effectiveness and side effects of treatment. Specifically, the Question Resolution Unit receives questions from patients and uses AI to generate appropriate answers. For example, if a patient asks about the effectiveness or side effects of treatment, the Question Resolution Unit searches for relevant medical information and provides an answer in an easy-to-understand format. The Question Resolution Unit can also refer to the patient's past question history and provide answers to similar questions. Furthermore, the Question Resolution Unit can use diagrams and illustrations to explain things in a way that is easy for patients to understand. For example, it can explain the mechanism of treatment and the mechanism of side effects using diagrams, allowing patients to deepen their understanding of the treatment. In this way, the Question Resolution Unit can quickly and accurately resolve patients' questions about treatment and reduce their anxiety about treatment.

[0075] The conversation unit facilitates treatment through conversations with a generating AI based on questions resolved by the question-solving unit. For example, the conversation unit increases the patient's motivation for treatment through conversations with them. Specifically, the conversation unit has the function of enabling natural conversations with patients using a generating AI. For example, if a patient has anxieties or questions about treatment, the conversation unit uses a generating AI to empathize with the patient's feelings and provide appropriate advice and words of encouragement. The conversation unit can also provide individually customized conversations based on the patient's treatment progress and goals. For example, if a patient achieves their treatment goals, the conversation unit uses a generating AI to send a congratulatory message to enhance the patient's sense of accomplishment. Furthermore, the conversation unit can collect patient feedback and use it to improve the content of conversations. In this way, the conversation unit can build trust with patients, maintain their motivation for treatment, and improve the success rate of treatment.

[0076] The alert unit can manage the patient's medication schedule and send alerts at appropriate times. For example, the alert unit can send an alert at a fixed time each day to encourage the patient to take their medication. The alert unit can also manage the patient's medication schedule and send alerts at appropriate times. For example, the alert unit can manage the patient's medication schedule and send alerts at appropriate times. This encourages the patient to take their medication at the appropriate time. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the patient's medication schedule into the AI, which can then send an alert at an appropriate time.

[0077] The monitoring unit can display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. For example, the monitoring unit can display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. For example, the monitoring unit can display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. The monitoring unit can also display the progress of treatment in a graph, allowing patients to feel the effects of the treatment. This allows patients to visually confirm the effects of the treatment. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the progress of treatment into AI, which can then display it in a graph.

[0078] The question-solving unit can provide detailed information in response to questions about the effectiveness and side effects of treatment. For example, the question-solving unit can provide detailed information in response to questions about the effectiveness and side effects of treatment. Furthermore, the question-solving unit can also provide detailed information in response to questions about the effectiveness and side effects of treatment. This allows patients to resolve their questions about treatment. Some or all of the above-described processes in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input questions about the effectiveness and side effects of treatment into AI, which can then provide detailed information.

[0079] The conversation unit can increase the patient's motivation for treatment through conversation. The conversation unit can increase the patient's motivation for treatment through conversation. For example, the conversation unit can increase the patient's motivation for treatment through conversation. The conversation unit can also increase the patient's motivation for treatment through conversation. This improves the patient's motivation for treatment. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input a conversation with the patient into a generative AI, which can then conduct a conversation that increases the patient's motivation for treatment.

[0080] The conversation unit can provide advice on the patient's lifestyle. For example, the conversation unit can provide advice on the patient's lifestyle. For example, the conversation unit can provide advice on the patient's lifestyle. The conversation unit can also provide advice on the patient's lifestyle. This supports the patient in leading a healthy life. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input advice on the patient's lifestyle into a generative AI, and the generative AI can provide the advice.

[0081] The alert unit can estimate the patient's emotions and adjust the timing and content of alerts based on the estimated emotions. For example, if the patient is stressed, the alert unit can send an alert containing a relaxing message. For example, if the patient is tired, the alert unit can lower the volume of the alert and notify with a gentle sound. The alert unit can also send an alert containing an encouraging message if the patient is feeling well. This promotes continued treatment by providing alerts that are tailored to 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 alert unit may be performed using AI or not using AI. For example, the alert unit can input patient emotion data into the generative AI, which can then adjust the timing and content of the alerts.

[0082] The alert unit can analyze the patient's past medication history and select the optimal alert method. For example, the alert unit can send alerts while avoiding times when the patient has previously ignored alerts. For example, the alert unit can analyze the patient's response to past alerts and select the most effective alert method. The alert unit can also analyze the patient's behavioral patterns when receiving past alerts and send alerts at the optimal timing. This promotes continued treatment by providing the patient with the most suitable alert method. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the patient's past medication history into AI, which can then select the optimal alert method.

[0083] The alert unit can customize alerts based on the patient's current lifestyle and activity level when sending them. For example, if the patient is at work, the alert unit can send an alert with a quiet vibration. For example, if the patient is exercising, the alert unit can send an audio alert to encourage the patient to take their medication without interrupting their exercise. The alert unit can also send an alert after the patient wakes up if they are sleeping. This promotes the continuation of treatment by providing alerts tailored to the patient's lifestyle and activity level. Some or all of the above processing in the alert unit may be performed using AI, for example, or not. For example, the alert unit can input the patient's lifestyle and activity level into the AI, which can then customize the alerts.

[0084] The alert unit can estimate the patient's emotions and determine the priority of alerts based on the estimated emotions. For example, if the patient is feeling anxious, the alert unit will prioritize sending important alerts. For example, if the patient is relaxed, the alert unit will send normal alerts. The alert unit can also prioritize sending urgent alerts if the patient is in a hurry. This promotes the continuation of treatment by prioritizing alerts according to 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 alert unit may be performed using AI or not using AI. For example, the alert unit can input patient emotion data into a generative AI, which can then determine the priority of alerts.

[0085] The alert unit can prioritize sending highly relevant alerts by considering the patient's geographical location when sending alerts. For example, the alert unit sends a medication reminder if the patient is at home. For example, if the patient is out, the alert unit sends a medication reminder and suggests medications to carry with them. The alert unit can also send alerts tailored to the time zone of the patient's travel destination if the patient is traveling. This promotes continued treatment by providing alerts based on the patient's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the patient's geographical location into the AI, which can then prioritize sending highly relevant alerts.

[0086] The alert unit can analyze the patient's social media activity and send relevant alerts when sending an alert. For example, if the patient is experiencing stress on social media, the alert unit can send an alert containing a relaxing message. For example, if the patient is active on social media, the alert unit can send an alert containing an encouraging message. The alert unit can also send relevant alerts if the patient is searching for information about their treatment on social media. This promotes treatment adherence by providing alerts based on the patient's social media activity. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input the patient's social media activity into AI, which can then send relevant alerts.

[0087] The monitoring unit can estimate the patient's emotions and adjust the monitoring method based on the estimated emotions. For example, if the patient is feeling anxious, the monitoring unit can provide detailed monitoring results to reassure them. For example, if the patient is relaxed, the monitoring unit can provide concise monitoring results. The monitoring unit can also display monitoring results in a visually easy-to-understand manner if the patient is feeling stressed. This makes it easier for patients to perceive the effectiveness of treatment by providing a monitoring method that is tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient emotion data into the generative AI, which can then adjust the monitoring method.

[0088] The monitoring unit can display the progress of treatment in real time during monitoring, allowing patients to immediately confirm the effectiveness of the treatment. For example, the monitoring unit displays the progress of treatment in real time when the patient opens the app. For example, the monitoring unit displays the progress in real time when the patient wants to check the effectiveness of the treatment. The monitoring unit can also display the progress periodically in real time for the patient to check the progress of treatment. This allows patients to confirm the effectiveness of the treatment in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the progress of treatment into the AI, which can then display it in real time.

[0089] The monitoring unit can evaluate the progress of treatment by considering the patient's lifestyle and activity data during monitoring. For example, the monitoring unit can evaluate the progress of treatment by considering the patient's exercise level. For example, the monitoring unit can evaluate the progress of treatment by considering the patient's diet. Furthermore, the monitoring unit can also evaluate the progress of treatment by considering the patient's sleep patterns. This makes it possible to evaluate the progress of treatment based on the patient's lifestyle and activity data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's lifestyle and activity data into AI, and the AI ​​can evaluate the progress of treatment.

[0090] The monitoring unit can estimate the patient's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if the patient is tense, the monitoring unit provides a simple and highly visible display method. For example, if the patient is relaxed, the monitoring unit provides a display method that includes detailed information. The monitoring unit can also provide a concise display method if the patient is in a hurry. By providing a display method of monitoring results that is tailored to the patient's emotions, the effectiveness of the treatment can be more easily perceived. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient emotion data into the generative AI, and the generative AI can adjust the display method of the monitoring results.

[0091] The monitoring unit can evaluate the progress of treatment while considering the geographical distribution of patients during monitoring. For example, if a patient lives in an urban area, the monitoring unit evaluates the progress of treatment while considering environmental factors specific to urban areas. For example, if a patient lives in a rural area, the monitoring unit evaluates the progress of treatment while considering environmental factors specific to rural areas. Furthermore, if a patient lives overseas, the monitoring unit can evaluate the progress of treatment while considering environmental factors of that region. This makes it possible to evaluate the progress of treatment based on the geographical distribution of patients. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of patients into the AI, and the AI ​​can evaluate the progress of treatment.

[0092] The monitoring unit can evaluate the progress of treatment by referring to relevant literature on the patient during monitoring. For example, the monitoring unit can evaluate progress by referring to the latest research papers on the patient's treatment. For example, the monitoring unit can evaluate progress by referring to past literature on the patient's treatment. The monitoring unit can also evaluate progress by referring to professional books on the patient's treatment. This makes it possible to evaluate the progress of treatment based on relevant literature on the patient. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature on the patient into the AI, and the AI ​​can evaluate the progress of treatment.

[0093] The motivation maintenance unit can estimate the patient's emotions and adjust its motivation maintenance methods based on the estimated emotions. For example, if the patient is feeling anxious, the motivation maintenance unit can send an encouraging message. For example, if the patient is relaxed, the motivation maintenance unit can send a message praising the progress of treatment. The motivation maintenance unit can also provide relaxation advice if the patient is feeling stressed. This promotes the continuation of treatment by providing motivation maintenance methods that are tailored to 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 motivation maintenance unit may be performed using AI or not using AI. For example, the motivation maintenance unit can input patient emotion data into the generative AI, which can then adjust its motivation maintenance methods.

[0094] The motivation maintenance unit can analyze the patient's past treatment history to provide optimal feedback when maintaining motivation. For example, the motivation maintenance unit can provide feedback based on treatment methods the patient has successfully used in the past. For example, the motivation maintenance unit can provide feedback to help the patient avoid treatment methods that have failed in the past. The motivation maintenance unit can also analyze the patient's past treatment history and provide the most effective feedback. This promotes the continuation of treatment by providing optimal feedback based on the patient's past treatment history. Some or all of the above processes in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the patient's past treatment history into AI, and the AI ​​can provide optimal feedback.

[0095] The motivation maintenance unit can customize motivation maintenance methods based on the patient's current living situation. For example, if the patient is busy, the motivation maintenance unit can provide effective motivation maintenance methods in a short amount of time. For example, if the patient is relaxed, the motivation maintenance unit can provide detailed motivation maintenance methods. Furthermore, if the patient is stressed, the motivation maintenance unit can also provide motivation maintenance methods that promote relaxation. This promotes the continuation of treatment by providing motivation maintenance methods that are tailored to the patient's current living situation. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input patient living situation data into AI, and the AI ​​can customize the motivation maintenance methods.

[0096] The motivation maintenance unit can estimate the patient's emotions and determine the priority of motivation maintenance based on the estimated emotions. For example, if the patient is feeling anxious, the motivation maintenance unit will set a higher priority for motivation maintenance. For example, if the patient is relaxed, the motivation maintenance unit will provide normal motivation maintenance methods. The motivation maintenance unit can also provide rapid motivation maintenance methods if the patient is in a hurry. This promotes the continuation of treatment by determining the priority of motivation maintenance according to 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 motivation maintenance unit may be performed using AI or not using AI. For example, the motivation maintenance unit can input patient emotion data into a generative AI, and the generative AI can determine the priority of motivation maintenance.

[0097] The motivation maintenance unit can select the optimal motivation maintenance method by considering the patient's geographical location information when maintaining motivation. For example, if the patient is at home, the motivation maintenance unit provides motivation maintenance methods that can be done at home. For example, if the patient is out, the motivation maintenance unit provides motivation maintenance methods that can be done while out. Furthermore, if the patient is traveling, the motivation maintenance unit can also provide motivation maintenance methods that can be done at the travel destination. By providing motivation maintenance methods based on the patient's geographical location information, the unit promotes the continuation of treatment. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the patient's geographical location information into the AI, and the AI ​​can select the optimal motivation maintenance method.

[0098] The motivation maintenance unit can analyze the patient's social media activity and suggest ways to maintain motivation during the motivation maintenance process. For example, if the patient is searching for information about their treatment on social media, the motivation maintenance unit can provide relevant motivation maintenance methods. For example, if the patient is feeling stressed on social media, the motivation maintenance unit can provide relaxing motivation maintenance methods. The motivation maintenance unit can also send encouraging messages if the patient is active on social media. This promotes the continuation of treatment by providing motivation maintenance methods based on the patient's social media activity. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the patient's social media activity into AI, which can then suggest ways to maintain motivation.

[0099] The question-solving unit can estimate the patient's emotions and adjust the question-solving method based on the estimated emotions. For example, if the patient is feeling anxious, the question-solving unit provides a detailed and reassuring answer. For example, if the patient is relaxed, the question-solving unit provides a concise and to-the-point answer. The question-solving unit can also provide an answer that includes relaxing advice if the patient is feeling stressed. This promotes the continuation of treatment by providing a question-solving method that is appropriate to 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 question-solving unit may be performed using AI, for example, or not using AI. For example, the question-solving unit can input patient emotion data into the generative AI, which can then adjust the question-solving method.

[0100] The question-solving unit can provide the optimal answer by analyzing the patient's past question history when resolving a question. For example, the question-solving unit can provide relevant answers based on the questions the patient has asked in the past. For example, the question-solving unit can provide the optimal answer by referring to the answers the patient has received in the past. The question-solving unit can also analyze the patient's past question history and provide the most effective answer. This promotes the continuation of treatment by providing the optimal answer based on the patient's past question history. Some or all of the above processing in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input the patient's past question history into AI, and the AI ​​can provide the optimal answer.

[0101] The question-solving unit can customize its question-solving methods based on the patient's current living situation. For example, if the patient is busy, the question-solving unit can provide a quick and effective answer. For example, if the patient is relaxed, the question-solving unit can provide a detailed answer. Furthermore, if the patient is stressed, the question-solving unit can provide an answer that includes advice to help them relax. This promotes the continuation of treatment by providing question-solving methods tailored to the patient's current living situation. Some or all of the above-described processes in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input patient living situation data into AI, which can then customize the question-solving methods.

[0102] The question-solving unit can estimate the patient's emotions and determine the priority of question-solving based on the estimated emotions. For example, if the patient is feeling anxious, the question-solving unit will set a higher priority for question-solving. For example, if the patient is relaxed, the question-solving unit will provide a standard question-solving method. The question-solving unit can also provide a rapid question-solving method if the patient is in a hurry. This promotes the continuation of treatment by determining the priority of question-solving according to 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 question-solving unit may be performed using AI, for example, or not using AI. For example, the question-solving unit can input patient emotion data into a generative AI, which can then determine the priority of question-solving.

[0103] The question-solving unit can select the optimal question-solving method when resolving a question, taking into account the patient's geographical location information. For example, if the patient is at home, the question-solving unit provides a question-solving method that can be done at home. For example, if the patient is out, the question-solving unit provides a question-solving method that can be done at their destination. Furthermore, if the patient is traveling, the question-solving unit can also provide a question-solving method that can be done at their travel destination. This promotes the continuation of treatment by providing question-solving methods based on the patient's geographical location information. Some or all of the above processing in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input the patient's geographical location information into the AI, which can then select the optimal question-solving method.

[0104] The question-solving unit can analyze the patient's social media activity and propose solutions when resolving questions. For example, if the patient is searching for information about their treatment on social media, the question-solving unit will provide relevant solutions. For example, if the patient is experiencing stress on social media, the question-solving unit will provide relaxing solutions. The question-solving unit can also send encouraging messages if the patient is active on social media. This promotes the continuation of treatment by providing solutions based on the patient's social media activity. Some or all of the above processing in the question-solving unit may be performed using AI, for example, or without AI. For example, the question-solving unit can input the patient's social media activity into AI, which can then propose solutions.

[0105] The conversation unit can estimate the patient's emotions and adjust the content and tone of the conversation based on the estimated emotions. For example, if the patient is feeling anxious, the conversation unit will use a reassuring tone. For example, if the patient is relaxed, the conversation unit will use a cheerful tone. The conversation unit can also use a relaxing tone if the patient is feeling stressed. This promotes the continuation of treatment by providing conversation content and tone that are appropriate to 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 conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input patient emotion data into a generative AI, which can then adjust the content and tone of the conversation.

[0106] The conversation unit can analyze the patient's past conversation history during a conversation to provide optimal conversation content. For example, the conversation unit can provide relevant conversation content based on what the patient has said in the past. For example, the conversation unit can provide optimal conversation content by referring to advice the patient has received in the past. The conversation unit can also analyze the patient's past conversation history to provide the most effective conversation content. This promotes the continuation of treatment by providing optimal conversation content based on the patient's past conversation history. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the patient's past conversation history into a generative AI, which can then provide optimal conversation content.

[0107] The conversation unit can customize the content of conversations based on the patient's current living situation. For example, if the patient is busy, the conversation unit can provide concise and effective conversation content. For example, if the patient is relaxed, the conversation unit can provide detailed conversation content. Furthermore, if the patient is stressed, the conversation unit can provide conversation content that includes relaxation advice. This promotes the continuation of treatment by providing conversation content tailored to the patient's current living situation. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input patient living situation data into a generative AI, which can then customize the conversation content.

[0108] The conversation unit can estimate the patient's emotions and determine the priority of conversations based on the estimated emotions. For example, if the patient is feeling anxious, the conversation unit will set a higher priority for that conversation. For example, if the patient is relaxed, the conversation unit will provide normal conversation content. The conversation unit can also provide conversation content quickly if the patient is in a hurry. This promotes the continuation of treatment by determining the priority of conversations according to 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 conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input patient emotion data into a generative AI, and the generative AI can determine the priority of conversations.

[0109] The conversation unit can provide optimal conversation content while considering the patient's geographical location. For example, if the patient is at home, the conversation unit can provide advice that can be done at home. For example, if the patient is out, the conversation unit can provide advice that can be done while out. Furthermore, if the patient is traveling, the conversation unit can provide advice that can be done at their travel destination. This promotes the continuation of treatment by providing conversation content based on the patient's geographical location. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the patient's geographical location information into a generative AI, which can then provide optimal conversation content.

[0110] The conversation unit can analyze the patient's social media activity during a conversation and suggest conversation topics. For example, if the patient is searching for information about their treatment on social media, the conversation unit will provide relevant conversation topics. For example, if the patient is experiencing stress on social media, the conversation unit will provide relaxing conversation topics. The conversation unit can also send encouraging messages if the patient is active on social media. This promotes continued treatment by providing conversation topics based on the patient's social media activity. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversation unit can input the patient's social media activity into a generative AI, which can then suggest conversation topics.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The mentor AI system can estimate a patient's emotions when monitoring their treatment progress and adjust the content of feedback based on those emotions. For example, if a patient is feeling anxious about their treatment, the monitoring unit can provide reassuring, positive feedback. If a patient is losing motivation for treatment, the monitoring unit can also provide feedback that includes encouraging messages. Furthermore, if a patient has positive feelings towards their treatment, the monitoring unit can provide feedback that celebrates their progress. This allows the system to provide feedback tailored to the patient's emotions, thereby promoting continued treatment.

[0113] The mentor AI system can monitor a patient's treatment progress and evaluate it by considering the patient's lifestyle data. For example, it can collect data such as the patient's exercise level, diet, and sleep patterns, and evaluate treatment progress based on this data. This enables treatment progress evaluation based on the patient's lifestyle, allowing for more accurate feedback. Furthermore, by analyzing the patient's lifestyle data, it can provide advice to maximize the effectiveness of treatment. For example, it can advise patients with low exercise levels to engage in moderate exercise.

[0114] The mentor AI system can evaluate treatment progress by considering the patient's geographical location when monitoring the patient's treatment progress. For example, if the patient lives in an urban area, the system can evaluate treatment progress by considering environmental factors specific to urban areas. Similarly, if the patient lives in a rural area, the system can evaluate treatment progress by considering environmental factors specific to rural areas. Furthermore, if the patient lives overseas, the system can evaluate treatment progress by considering environmental factors of that region. This enables treatment progress evaluation based on the patient's geographical location, allowing for more accurate feedback.

[0115] The mentor AI system can monitor a patient's treatment progress by referencing relevant literature. For example, it can evaluate progress by referring to the latest research papers on the patient's treatment. It can also evaluate progress by referring to past literature on the patient's treatment. Furthermore, it can evaluate progress by referring to specialized books on the patient's treatment. This enables treatment progress evaluation based on relevant literature on the patient, allowing for more accurate feedback.

[0116] The mentor AI system can estimate a patient's emotions when monitoring their treatment progress and adjust the display of monitoring results based on those emotions. For example, if a patient is anxious, it can provide a simple and easy-to-read display. If a patient is relaxed, it can provide a display with more detailed information. Furthermore, if a patient is in a hurry, it can provide a concise display. By providing monitoring results that are tailored to the patient's emotions, it can help them more easily perceive the effectiveness of their treatment.

[0117] The mentor AI system can monitor a patient's treatment progress, displaying treatment status in real time and allowing patients to immediately see the effectiveness of their treatment. For example, when a patient opens the app, their treatment progress can be displayed in real time. Patients can also view their progress in real time if they wish to check the effectiveness of their treatment. Furthermore, the system can display progress periodically in real time for patients to review. This allows patients to see the effectiveness of their treatment in real time, encouraging them to continue treatment.

[0118] The mentor AI system can estimate a patient's emotions when monitoring their treatment progress and adjust the monitoring method based on those emotions. For example, if a patient is feeling anxious, it can provide detailed monitoring results to reassure them. If a patient is relaxed, it can provide concise monitoring results. Furthermore, if a patient is feeling stressed, it can display monitoring results in a visually easy-to-understand format. By providing monitoring methods tailored to the patient's emotions, it can help them more easily perceive the effectiveness of their treatment.

[0119] The mentor AI system can analyze a patient's past treatment history to provide optimal feedback when monitoring their treatment progress. For example, it can provide feedback based on treatment methods the patient has successfully used in the past. It can also provide feedback to help the patient avoid treatment methods that have failed in the past. Furthermore, it can analyze the patient's past treatment history to provide the most effective feedback. This allows for better treatment adherence by providing optimal feedback based on the patient's past treatment history.

[0120] The mentor AI system can estimate a patient's emotions when monitoring their treatment progress and prioritize monitoring results based on those emotions. For example, if a patient is feeling anxious, important monitoring results can be displayed preferentially. If the patient is relaxed, normal monitoring results can be displayed. Furthermore, if the patient is in a hurry, urgent monitoring results can be displayed preferentially. By prioritizing monitoring results according to the patient's emotions, this system can promote continued treatment.

[0121] The mentor AI system can analyze a patient's social media activity and suggest monitoring methods when monitoring the patient's treatment progress. For example, if a patient is searching for information about their treatment on social media, it can provide relevant monitoring methods. It can also provide relaxing monitoring methods if a patient is experiencing stress from social media. Furthermore, if a patient is active on social media, it can provide monitoring methods that include encouraging messages. By providing monitoring methods based on the patient's social media activity, it can promote treatment adherence.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The alert unit provides medication reminders to the patient. The alert unit manages the patient's medication schedule and sends alerts at appropriate times. For example, the alert unit sends alerts at a set time each day to encourage the patient to take their medication. Step 2: The monitoring unit monitors treatment progress based on alerts provided by the alert unit. The monitoring unit, for example, displays treatment progress in a graph so that patients can see the effects of the treatment. Step 3: The Motivation Maintenance Unit maintains motivation based on the treatment progress monitored by the Monitoring Unit. For example, the Motivation Maintenance Unit monitors the patient's treatment progress and provides appropriate feedback. Step 4: The Question Resolution Department resolves questions about the treatment based on the motivation maintained by the Motivation Maintenance Department. For example, the Question Resolution Department provides detailed information in response to questions about the effectiveness or side effects of the treatment. Step 5: The conversation unit facilitates treatment through conversations with the generated AI based on the questions resolved by the question-solving unit. For example, the conversation unit increases the patient's motivation for treatment through conversations with them.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the alert unit, monitoring unit, motivation maintenance unit, question resolution unit, and conversation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the alert unit is implemented by the control unit 46A of the smart device 14, which manages the patient's medication schedule and sends alerts at appropriate times. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which displays the progress of treatment in a graph. The motivation maintenance unit is implemented by the control unit 46A of the smart device 14, which monitors the patient's treatment progress and provides appropriate feedback. The question resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides detailed information in response to questions about the effectiveness and side effects of treatment. The conversation unit is implemented by the control unit 46A of the smart device 14, which facilitates treatment through conversation with the generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the alert unit, monitoring unit, motivation maintenance unit, question resolution unit, and conversation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the alert unit is implemented by the control unit 46A of the smart glasses 214, which manages the patient's medication schedule and sends alerts at appropriate times. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which displays the progress of treatment in a graph. The motivation maintenance unit is implemented by the control unit 46A of the smart glasses 214, which monitors the patient's treatment progress and provides appropriate feedback. The question resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides detailed information in response to questions about the effectiveness and side effects of treatment. The conversation unit is implemented by the control unit 46A of the smart glasses 214, which facilitates treatment through conversation with a generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the alert unit, monitoring unit, motivation maintenance unit, question resolution unit, and conversation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the alert unit is implemented by the control unit 46A of the headset terminal 314, which manages the patient's medication schedule and sends alerts at appropriate times. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which displays the progress of treatment in a graph. The motivation maintenance unit is implemented by the control unit 46A of the headset terminal 314, which monitors the patient's treatment progress and provides appropriate feedback. The question resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides detailed information in response to questions about the effectiveness and side effects of treatment. The conversation unit is implemented by the control unit 46A of the headset terminal 314, which facilitates treatment through conversation with the generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the alert unit, monitoring unit, motivation maintenance unit, question resolution unit, and conversation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the alert unit is implemented by the control unit 46A of the robot 414, which manages the patient's medication schedule and sends alerts at appropriate times. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which displays the progress of treatment in a graph. The motivation maintenance unit is implemented by, for example, the control unit 46A of the robot 414, which monitors the patient's treatment progress and provides appropriate feedback. The question resolution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides detailed information in response to questions about the effectiveness and side effects of treatment. The conversation unit is implemented by, for example, the control unit 46A of the robot 414, which facilitates treatment through conversation with the generating AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) An alert unit that provides medication reminders, A monitoring unit that monitors treatment progress based on alerts provided by the aforementioned alert unit, A motivation maintenance unit that maintains motivation based on the treatment progress monitored by the aforementioned monitoring unit, A question-solving unit that resolves questions about treatment based on the motivation maintained by the aforementioned motivation maintenance unit, The system comprises a conversation unit that facilitates treatment through conversation with a generating AI based on the questions resolved by the question-solving unit. A system characterized by the following features. (Note 2) The alert unit is, Manage the patient's medication schedule and send alerts at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, The progress of treatment is displayed in a graph, allowing patients to see the effects of the treatment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned question resolution unit is, We provide detailed information in response to questions about the effectiveness and side effects of the treatment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned conversation section is, Through conversations with patients, we aim to increase their motivation for treatment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned conversation section is, Providing advice on patients' lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 7) The alert unit is, The system estimates the patient's emotions and adjusts the timing and content of alerts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The alert unit is, Analyze the patient's past medication history to select the most appropriate alert method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The alert unit is, When sending an alert, customize the alert based on the patient's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The alert unit is, The system estimates the patient's emotions and prioritizes alerts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The alert unit is, When sending alerts, the system prioritizes sending highly relevant alerts by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The alert unit is, When sending an alert, the system analyzes the patient's social media activity and sends relevant alerts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, Estimate the patient's emotions and adjust the monitoring method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The monitoring unit, During monitoring, the progress of treatment is displayed in real time, allowing patients to immediately see the effectiveness of the treatment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The monitoring unit, During monitoring, the progress of treatment is evaluated by considering the patient's lifestyle and activity data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The monitoring unit, The system estimates the patient's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The monitoring unit, During monitoring, the geographical distribution of patients is taken into consideration when evaluating the progress of treatment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, During monitoring, the progress of treatment is evaluated by referring to relevant patient literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned motivation maintenance unit is The system estimates the patient's emotions and adjusts motivation maintenance methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned motivation maintenance unit is To maintain motivation, we analyze the patient's past treatment history and provide optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned motivation maintenance unit is When maintaining motivation, customize the means of maintaining motivation based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned motivation maintenance unit is The system estimates the patient's emotions and determines priorities for maintaining motivation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned motivation maintenance unit is When maintaining motivation, the most suitable motivation maintenance method is selected by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned motivation maintenance unit is When it comes to maintaining motivation, we analyze patients' social media activity and propose ways to maintain motivation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question resolution unit is, We estimate the patient's emotions and adjust the question-solving method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned question resolution unit is, When resolving a question, we analyze the patient's past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question resolution unit is, When resolving questions, customize the methods of resolving them based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question resolution unit is, Estimate the patient's emotions and determine the priority of question resolution based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned question resolution unit is, When resolving a question, the most appropriate method of resolving it will be selected, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned question resolution unit is, When resolving a patient's questions, we analyze their social media activity and propose solutions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned conversation section is, The system estimates the patient's emotions and adjusts the content and tone of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned conversation section is, During conversations, the system analyzes the patient's past conversation history to provide the most appropriate conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned conversation section is, During conversations, customize the content of the conversation based on the patient's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned conversation section is, The system estimates the patient's emotions and determines the priority of conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned conversation section is, When conversing, we provide optimal conversation content while taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned conversation section is, During conversations, we analyze the patient's social media activity and suggest topics for discussion. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 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. An alert unit that provides medication reminders, A monitoring unit that monitors treatment progress based on alerts provided by the aforementioned alert unit, A motivation maintenance unit that maintains motivation based on the treatment progress monitored by the aforementioned monitoring unit, A question-solving unit that resolves questions about treatment based on the motivation maintained by the aforementioned motivation maintenance unit, The system includes a conversation unit that facilitates treatment through conversation with a generating AI based on the questions resolved by the question-solving unit. A system characterized by the following features.

2. The alert unit is, Manage the patient's medication schedule and send alerts at the appropriate time. The system according to feature 1.

3. The monitoring unit, The progress of treatment is displayed in a graph, allowing patients to see the effects of the treatment. The system according to feature 1.

4. The aforementioned question resolution unit is, We provide detailed information in response to questions about the effectiveness and side effects of the treatment. The system according to feature 1.

5. The aforementioned conversation section is, Through conversations with patients, we aim to increase their motivation for treatment. The system according to feature 1.

6. The aforementioned conversation section is, Providing advice on patients' lifestyle habits. The system according to feature 1.

7. The alert unit is, The system estimates the patient's emotions and adjusts the timing and content of alerts based on those estimated emotions. The system according to feature 1.

8. The alert unit is, Analyze the patient's past medication history to select the most appropriate alert method. The system according to feature 1.

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