system

Abstract

The system according to the embodiment aims to support long-term treatment and maintain patient motivation. [Solution] According to an embodiment, the system includes a collection unit, a management unit, and a provision unit. The collection unit collects basic information about the patient. The management unit manages a medication schedule and sends alerts based on the information collected by the collection unit. The provision unit provides messages to maintain the patient's motivation based on the schedule managed by the management unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to continue treatment over a long period of time, and maintaining patient motivation is a challenge.

[0005] The system according to the embodiment aims to support long-term treatment and maintain patient motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a management unit, and a provision unit. The collection unit collects basic information about the patient. The management unit manages a medication schedule and sends alerts based on the information collected by the collection unit. The provision unit provides messages to maintain the patient's motivation based on the schedule managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can support long-term treatment and maintain patient motivation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention incorporates a Mentor AI to support patients' daily treatment of Japanese cedar pollinosis. When a patient begins sublingual immunotherapy, the Mentor AI collects basic patient information. The Mentor AI then manages the patient's medication schedule and sends daily dose alerts. Furthermore, the Mentor AI provides encouraging messages and treatment progress updates to maintain patient motivation. For example, it sends messages such as, "Take your medication today!" and "Treatment is progressing smoothly!" The Mentor AI also responds to patients' questions and concerns in real time. For example, it provides appropriate answers to questions such as, "What should I do if I forget to take my medication?" and "What should I do if I experience side effects?" This allows patients to continue treatment with peace of mind. Furthermore, the Mentor AI analyzes patients' treatment data and provides feedback to physicians as needed. For example, it reports medication adherence rates and the occurrence of side effects to physicians, helping them to revise their treatment plans. This system is expected to make it easier for patients to continue long-term treatment and effectively alleviate the symptoms of Japanese cedar pollinosis. This allows the system to support the patient's day-to-day care and promote long-term treatment continuity.

[0029] A treatment support system according to an embodiment includes a collection unit, a management unit, and a providing unit. The collection unit collects basic information about a patient. The basic information includes, but is not limited to, the patient's name, age, and medical history. The collection unit collects the basic information about the patient using, for example, a questionnaire. The collection unit can also collect biological data about the patient using a sensor. For example, the collection unit may measure the patient's heart rate and body temperature using a wearable device and collect the basic information. The management unit manages a medication schedule based on the information collected by the collection unit and sends an alert. The medication schedule includes, for example, medication times and dosages, but is not limited to, for example. The management unit sends an alert when the patient takes their medication. Methods for sending the alert include, for example, email, SMS, and app notification. For example, the management unit may send an app notification to the patient's smartphone to notify them of medication times. The management unit can also record the patient's medication history to help manage the medication schedule. The providing unit provides a message to maintain the patient's motivation based on the schedule managed by the management unit. The messages include, but are not limited to, words of encouragement and progress reports on treatment. The providing unit sends messages such as, "Try your best to take your medication today!" or "Treatment is progressing smoothly!" The providing unit can also analyze the patient's treatment data and provide feedback to the doctor as needed. For example, the providing unit can report the medication continuation rate and the occurrence of side effects to the doctor, which can be used to review the treatment plan. In this way, the treatment support system according to the embodiment can support the patient's daily treatment and promote the continuation of long-term treatment.

[0030] The collection unit can collect basic information about the patient. The basic information includes, but is not limited to, the patient's name, age, medical history, and the like. The collection unit can collect the basic information about the patient, for example, using a questionnaire. The collection unit can also collect biological data about the patient using a sensor. For example, the collection unit can measure the patient's heart rate and body temperature using a wearable device and collect the data as basic information. This allows the collection unit to efficiently collect the basic information about the patient.

[0031] The management unit can manage a medication schedule based on the collected information and send alerts. The medication schedule includes, but is not limited to, for example, medication times and dosages. The management unit sends alerts, for example, at the time of medication administration to the patient. Methods for sending alerts include, for example, email, SMS, and app notifications. For example, the management unit sends an app notification to the patient's smartphone to notify them of medication times. The management unit can also record the patient's medication history and use it to manage the medication schedule. This allows the management unit to efficiently manage the patient's medication schedule and send alerts.

[0032] The providing unit can provide messages to maintain the patient's motivation based on a managed schedule. Examples of messages include, but are not limited to, words of encouragement and progress reports on treatment. For example, the providing unit sends messages such as "Try your best to take your medication today!" or "Treatment is progressing smoothly!" The providing unit can also analyze the patient's treatment data and provide feedback to the doctor as needed. For example, the providing unit can report to the doctor the rate of continued medication use and the occurrence of side effects, etc., to help the doctor review the treatment plan. In this way, the providing unit can maintain the patient's motivation and support the continuation of treatment.

[0033] The system is equipped with a response unit that immediately responds to questions and concerns from patients. The response unit responds to questions and concerns from patients in real time. For example, if a patient asks, "What should I do if I forget to take my medicine?", the response unit will immediately respond, "Take your next dose at the scheduled time." Also, if a patient asks, "What should I do if I experience side effects?", the response unit can respond, "Consult your doctor." This allows the response unit to quickly respond to patients' questions and concerns and support them in continuing their treatment with peace of mind.

[0034] The system includes an analysis unit that analyzes treatment data and provides feedback to a doctor. The analysis unit analyzes the patient's treatment data and provides feedback to the doctor as necessary. For example, the analysis unit can analyze the patient's medication continuation rate and the occurrence of side effects, and report this to the doctor. This can help the doctor review the treatment plan. For example, the analysis unit analyzes the patient's medication history, and if the medication continuation rate is low, it can report this to the doctor and suggest a review of the treatment plan. The analysis unit can also analyze the occurrence of side effects, and if side effects occur frequently, it can report this to the doctor and suggest a change in the therapeutic drug. This allows the analysis unit to provide treatment data to the doctor, and help the doctor review the treatment plan.

[0035] In the system, the collection unit analyzes the patient's past treatment history and selects the optimal information collection method. The collection unit analyzes the patient's past treatment history and selects the optimal information collection method. For example, the collection unit suggests the optimal information collection method based on treatment methods used by the patient in the past. The collection unit can also select the effective timing for information collection from the patient's past treatment history. Furthermore, the collection unit can analyze the patient's past treatment history and efficiently collect necessary information. As a result, the collection unit can select the optimal information collection method based on the past treatment history and achieve efficient information collection.

[0036] In the system, when the collection unit collects basic information, the collection unit filters the information based on the patient's lifestyle and environment. When collecting basic information, the collection unit filters the information based on the patient's lifestyle and environment. For example, the collection unit prioritizes collecting highly relevant information based on the patient's lifestyle. The collection unit can also filter and collect necessary information based on the patient's environment. Furthermore, the collection unit can determine the priority of the information to be collected, taking into account the patient's lifestyle and environment. This allows the collection unit to filter information based on the patient's lifestyle and environment and collect highly relevant information.

[0037] In the system, when the collection unit collects basic information, it prioritizes collecting highly relevant information by taking into account the patient's geographical location information. When collecting basic information, the collection unit prioritizes collecting highly relevant information by taking into account the patient's geographical location information. For example, the collection unit prioritizes collecting highly relevant information based on the patient's current location. The collection unit can also collect necessary information by referring to the patient's past location information. Furthermore, the collection unit can also determine the priority of information to be collected by taking into account the patient's geographical location information. This allows the collection unit to prioritize collecting highly relevant information by taking into account the patient's geographical location information.

[0038] In the system, when the collection unit collects basic information, the collection unit analyzes the patient's social media activity and collects related information. When the collection unit collects basic information, the collection unit analyzes the patient's social media activity and collects related information. For example, the collection unit collects related health information from the patient's social media activity. The collection unit can also analyze the content of the patient's social media posts and collect necessary information. Furthermore, the collection unit can determine the priority of information to be collected with reference to the patient's social media activity. This allows the collection unit to analyze the patient's social media activity and collect related information.

[0039] In the system, when the management unit manages medication schedules, it sets an optimal schedule by referring to the patient's past medication history. When managing medication schedules, the management unit sets an optimal schedule by referring to the patient's past medication history. For example, the management unit sets an optimal medication schedule based on the patient's past medication history. The management unit can also select effective medication timing from the patient's past medication history. Furthermore, the management unit can analyze the patient's past medication history and efficiently set the necessary schedule. This allows the management unit to set an optimal schedule based on the patient's past medication history, thereby achieving efficient medication management.

[0040] In the system, when the management unit manages a medication schedule, the management unit customizes the schedule based on the patient's lifestyle rhythm. When the management unit manages a medication schedule, the management unit customizes the schedule based on the patient's lifestyle rhythm. For example, the management unit sets an optimal medication schedule based on the patient's lifestyle rhythm. The management unit can also customize the medication schedule taking the patient's lifestyle rhythm into consideration. Furthermore, the management unit can also adjust the medication schedule to match the patient's lifestyle rhythm. In this way, the management unit can customize the schedule based on the patient's lifestyle rhythm and promote continued medication administration.

[0041] In the system, when the management unit manages a medication schedule, the management unit selects the optimal alert transmission method taking into account the patient's geographical location information. When managing a medication schedule, the management unit selects the optimal alert transmission method taking into account the patient's geographical location information. For example, the management unit selects the optimal alert transmission method based on the patient's current location. The management unit can also select the alert transmission method by referring to the patient's past location information. Furthermore, the management unit can also determine the alert transmission method by taking into account the patient's geographical location information. This allows the management unit to select the optimal alert transmission method by taking into account the patient's geographical location information.

[0042] In the system, when the management unit manages a medication schedule, the management unit analyzes the patient's social media activity and customizes the content of the alert. When the management unit manages a medication schedule, the management unit analyzes the patient's social media activity and customizes the content of the alert. For example, the management unit customizes the relevant alert content based on the patient's social media activity. The management unit can also analyze the patient's social media posts and adjust the alert content. Furthermore, the management unit can also determine the alert content based on the patient's social media activity. This allows the management unit to analyze the patient's social media activity and customize the relevant alert content.

[0043] In the system, when the providing unit provides a message, it adjusts the level of detail of the message based on the patient's treatment progress status. When the providing unit provides a message, it adjusts the level of detail of the message based on the patient's treatment progress status. For example, the providing unit provides a detailed message based on the patient's treatment progress status. The providing unit can also adjust the level of detail of the message taking into account the patient's treatment progress status. Furthermore, the providing unit can also determine the content of the message in accordance with the patient's treatment progress status. In this way, the providing unit can adjust the level of detail of the message based on the patient's treatment progress status and provide appropriate information.

[0044] In the system, when the providing unit provides a message, it applies different message algorithms depending on the patient's treatment category. When providing a message, the providing unit applies different message algorithms depending on the patient's treatment category. For example, the providing unit applies an optimal message algorithm based on the patient's treatment category. The providing unit can also adjust the message algorithm taking the patient's treatment category into consideration. Furthermore, the providing unit can also determine a message algorithm in accordance with the patient's treatment category. This allows the providing unit to apply the optimal message algorithm depending on the patient's treatment category and provide an effective message.

[0045] In the system, when the providing unit provides a message, it determines the priority of the message based on the start date of the patient's treatment. When the providing unit provides a message, it determines the priority of the message based on the start date of the patient's treatment. For example, the providing unit provides an optimal message based on the start date of the patient's treatment. The providing unit can also determine the priority of the message taking into account the start date of the patient's treatment. Furthermore, the providing unit can also determine the content of the message in accordance with the start date of the patient's treatment. In this way, the providing unit can determine the priority of the message based on the start date of the patient's treatment and provide appropriate information.

[0046] In the system, when the providing unit provides messages, the providing unit adjusts the order of messages based on the patient's treatment-related information. When providing messages, the providing unit adjusts the order of messages based on the patient's treatment-related information. For example, the providing unit provides an optimal message order based on the patient's treatment-related information. The providing unit can also adjust the order of messages taking into account the patient's treatment-related information. Furthermore, the providing unit can determine the order of messages in accordance with the patient's treatment-related information. This allows the providing unit to adjust the order of messages based on the patient's treatment-related information and provide appropriate information.

[0047] In the system, when the answering unit provides an answer, it refers to the patient's past question history to provide the most appropriate answer. When the answering unit provides an answer, it refers to the patient's past question history to provide the most appropriate answer. For example, the answering unit provides the most appropriate answer based on the patient's past question history. The answering unit can also provide a relevant answer from the patient's past question history. Furthermore, the answering unit can analyze the patient's past question history and efficiently provide the necessary answer. This allows the answering unit to provide the most appropriate answer based on the patient's past question history, thereby achieving efficient information provision.

[0048] The system adjusts the level of detail of the answer based on the patient's treatment status when the answering unit provides an answer. The answering unit adjusts the level of detail of the answer based on the patient's treatment status when providing an answer. For example, the answering unit provides a detailed answer based on the patient's treatment status. The answering unit can also adjust the level of detail of the answer taking the patient's treatment status into consideration. Furthermore, the answering unit can determine the content of the answer in accordance with the patient's treatment status. This allows the answering unit to adjust the level of detail of the answer based on the patient's treatment status and provide appropriate information.

[0049] The system provides an optimal answer by the answering unit taking into consideration the geographical location information of the patient when providing an answer. The answering unit provides an optimal answer by taking into consideration the geographical location information of the patient when providing an answer. For example, the answering unit provides an optimal answer based on the patient's current location. The answering unit can also provide an answer by referring to the patient's past location information. Furthermore, the answering unit can also determine the content of the answer by taking into consideration the geographical location information of the patient. This allows the answering unit to provide an optimal answer by taking into consideration the geographical location information of the patient.

[0050] The system analyzes the patient's social media activity and provides a relevant answer when the answering unit provides an answer. When the answering unit provides an answer, the system analyzes the patient's social media activity and provides a relevant answer. For example, the answering unit provides an answer to a related question from the patient's social media activity. The answering unit can also analyze the content of the patient's social media posts and provide the necessary answer. Furthermore, the answering unit can also determine the content of the answer with reference to the patient's social media activity. This allows the answering unit to analyze the patient's social media activity and provide an answer to a related question.

[0051] In the system, when the analysis unit analyzes treatment data, it refers to past treatment data and applies the optimal analysis algorithm. When analyzing treatment data, the analysis unit refers to past treatment data and applies the optimal analysis algorithm. For example, the analysis unit applies the optimal analysis algorithm based on past treatment data. The analysis unit can also select an effective analysis algorithm from past treatment data. Furthermore, the analysis unit can analyze past treatment data and efficiently apply the necessary algorithm. This allows the analysis unit to apply the optimal analysis algorithm based on past treatment data, thereby achieving efficient data analysis.

[0052] In the system, when the analysis unit analyzes the treatment data, it applies different analysis methods depending on the patient's treatment category. When analyzing the treatment data, the analysis unit applies different analysis methods depending on the patient's treatment category. For example, the analysis unit applies an optimal analysis method based on the patient's treatment category. The analysis unit can also adjust the analysis method taking into account the patient's treatment category. Furthermore, the analysis unit can determine the analysis method in accordance with the patient's treatment category. This allows the analysis unit to apply the optimal analysis method depending on the patient's treatment category, thereby achieving effective data analysis.

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

[0054] In this system, the collection unit collects the patient's dietary data, and the management unit adjusts the medication schedule based on that data. For example, the collection unit records the time and content of the patient's meals, and the management unit optimizes medication times based on that data. The collection unit can also analyze the nutritional balance from the patient's dietary content, and the management unit can adjust the medication schedule based on the results. Furthermore, the collection unit can provide the patient's dietary data to the doctor, which can use it as a reference when reviewing the treatment plan. This allows the system to utilize the patient's dietary data to provide more effective treatment support.

[0055] In this system, the collection unit collects the patient's exercise data, and the management unit adjusts the medication schedule based on that data. For example, the collection unit records the type and duration of exercise performed by the patient, and the management unit optimizes medication times based on that data. The collection unit can also analyze changes in physical strength from the patient's exercise data, and the management unit can adjust the medication schedule based on the results. Furthermore, the collection unit can provide the patient's exercise data to the doctor, which the doctor can use as a reference when reviewing the treatment plan. This allows the system to utilize the patient's exercise data to provide more effective treatment support.

[0056] The system's management unit can adjust medication schedules based on the patient's sleep data. For example, the management unit can record the patient's sleep duration and quality and optimize medication times based on that data. The management unit can also analyze the patient's fatigue level from the sleep data and adjust medication schedules based on the results. Furthermore, the management unit can provide the patient's sleep data to doctors, who can use it as a reference when reviewing treatment plans. This allows the system to utilize the patient's sleep data to provide more effective treatment support.

[0057] The system allows the providing unit to customize the content of the message based on the patient's hobbies and interests. For example, the providing unit sends a message related to the patient's favorite sports or music. The providing unit can also customize an encouraging message based on the patient's hobbies. Furthermore, the providing unit can provide information about treatment tailored to the patient's interests. This allows the system to provide messages that take the patient's hobbies and interests into consideration and maintain their motivation.

[0058] The system allows the provider to adjust the content of the message based on the patient's treatment goals. For example, the provider sends an appropriate message depending on the short-term and long-term goals set by the patient. The provider can also customize encouraging messages based on the patient's goal achievement status. Furthermore, the provider can provide treatment advice in line with the patient's goals. This allows the system to provide messages that take into account the patient's treatment goals and maintain motivation.

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

[0060] Step 1: The collection unit collects basic information about the patient. The basic information includes the patient's name, age, medical history, etc. The collection unit collects the basic information about the patient using a questionnaire. The collection unit can also collect biological data about the patient using sensors. For example, the collection unit measures the patient's heart rate and body temperature using a wearable device and collects this as basic information. Step 2: The management unit manages the medication schedule based on the information collected by the collection unit and sends alerts. The medication schedule includes medication times and dosages. The management unit sends alerts to the patient when it's time to take the medication. Methods for sending alerts include email, SMS, and app notifications. For example, the management unit sends an app notification to the patient's smartphone to notify them of medication times. The management unit can also record the patient's medication history to help manage the medication schedule. Step 3: The delivery department provides messages to maintain the patient's motivation based on the schedule managed by the administration department. These messages include words of encouragement and information on the progress of treatment. For example, messages such as "Let's try our best to take the medication today!" or "Treatment is going well!" are sent. The delivery department can also analyze the patient's treatment data and provide feedback to the doctor as needed. For example, the delivery department can report to the doctor the rate of continued medication use and the occurrence of side effects, which can be used to review the treatment plan.

[0061] (Example 2) A system according to an embodiment of the present invention incorporates a Mentor AI to support patients' daily treatment of Japanese cedar pollinosis. When a patient begins sublingual immunotherapy, the Mentor AI collects basic patient information. The Mentor AI then manages the patient's medication schedule and sends daily dose alerts. Furthermore, the Mentor AI provides encouraging messages and treatment progress updates to maintain patient motivation. For example, it sends messages such as, "Take your medication today!" and "Treatment is progressing smoothly!" The Mentor AI also responds to patients' questions and concerns in real time. For example, it provides appropriate answers to questions such as, "What should I do if I forget to take my medication?" and "What should I do if I experience side effects?" This allows patients to continue treatment with peace of mind. Furthermore, the Mentor AI analyzes patients' treatment data and provides feedback to physicians as needed. For example, it reports medication adherence rates and the occurrence of side effects to physicians, helping them to revise their treatment plans. This system is expected to make it easier for patients to continue long-term treatment and effectively alleviate the symptoms of Japanese cedar pollinosis. This allows the system to support the patient's day-to-day care and promote long-term treatment continuity.

[0062] A treatment support system according to an embodiment includes a collection unit, a management unit, and a providing unit. The collection unit collects basic information about a patient. The basic information includes, but is not limited to, the patient's name, age, and medical history. The collection unit collects the basic information about the patient using, for example, a questionnaire. The collection unit can also collect biological data about the patient using a sensor. For example, the collection unit may measure the patient's heart rate and body temperature using a wearable device and collect the basic information. The management unit manages a medication schedule based on the information collected by the collection unit and sends an alert. The medication schedule includes, for example, medication times and dosages, but is not limited to, for example. The management unit sends an alert when the patient takes their medication. Methods for sending the alert include, for example, email, SMS, and app notification. For example, the management unit may send an app notification to the patient's smartphone to notify them of medication times. The management unit can also record the patient's medication history to help manage the medication schedule. The providing unit provides a message to maintain the patient's motivation based on the schedule managed by the management unit. The messages include, but are not limited to, words of encouragement and progress reports on treatment. The providing unit sends messages such as, "Try your best to take your medication today!" or "Treatment is progressing smoothly!" The providing unit can also analyze the patient's treatment data and provide feedback to the doctor as needed. For example, the providing unit can report the medication continuation rate and the occurrence of side effects to the doctor, which can be used to review the treatment plan. In this way, the treatment support system according to the embodiment can support the patient's daily treatment and promote the continuation of long-term treatment.

[0063] The collection unit can collect basic information about the patient. The basic information includes, but is not limited to, the patient's name, age, medical history, and the like. The collection unit can collect the basic information about the patient, for example, using a questionnaire. The collection unit can also collect biological data about the patient using a sensor. For example, the collection unit can measure the patient's heart rate and body temperature using a wearable device and collect the data as basic information. This allows the collection unit to efficiently collect the basic information about the patient.

[0064] The management unit can manage a medication schedule based on the collected information and send alerts. The medication schedule includes, but is not limited to, for example, medication times and dosages. The management unit sends alerts, for example, at the time of medication administration to the patient. Methods for sending alerts include, for example, email, SMS, and app notifications. For example, the management unit sends an app notification to the patient's smartphone to notify them of medication times. The management unit can also record the patient's medication history and use it to manage the medication schedule. This allows the management unit to efficiently manage the patient's medication schedule and send alerts.

[0065] The providing unit can provide messages to maintain the patient's motivation based on a managed schedule. Examples of messages include, but are not limited to, words of encouragement and progress reports on treatment. For example, the providing unit sends messages such as "Try your best to take your medication today!" or "Treatment is progressing smoothly!" The providing unit can also analyze the patient's treatment data and provide feedback to the doctor as needed. For example, the providing unit can report to the doctor the rate of continued medication use and the occurrence of side effects, etc., to help the doctor review the treatment plan. In this way, the providing unit can maintain the patient's motivation and support the continuation of treatment.

[0066] The system is equipped with a response unit that immediately responds to questions and concerns from patients. The response unit responds to questions and concerns from patients in real time. For example, if a patient asks, "What should I do if I forget to take my medicine?", the response unit will immediately respond, "Take your next dose at the scheduled time." Also, if a patient asks, "What should I do if I experience side effects?", the response unit can respond, "Consult your doctor." This allows the response unit to quickly respond to patients' questions and concerns and support them in continuing their treatment with peace of mind.

[0067] The system includes an analysis unit that analyzes treatment data and provides feedback to a doctor. The analysis unit analyzes the patient's treatment data and provides feedback to the doctor as necessary. For example, the analysis unit can analyze the patient's medication continuation rate and the occurrence of side effects, and report this to the doctor. This can help the doctor review the treatment plan. For example, the analysis unit analyzes the patient's medication history, and if the medication continuation rate is low, it can report this to the doctor and suggest a review of the treatment plan. The analysis unit can also analyze the occurrence of side effects, and if side effects occur frequently, it can report this to the doctor and suggest a change in the therapeutic drug. This allows the analysis unit to provide treatment data to the doctor, and help the doctor review the treatment plan.

[0068] In the system, the collection unit estimates the patient's emotions and adjusts the timing of collecting basic information based on the estimated patient emotions. The collection unit estimates the patient's emotions and adjusts the timing of collecting basic information based on the estimated patient emotions. For example, if the patient is feeling stressed, the collection unit collects basic information during a time when the patient is able to relax. Also, if the patient is relaxed, the collection unit can collect detailed basic information. Furthermore, if the patient is busy, the collection unit can collect the minimum necessary basic information in a short period of time. This allows the collection unit to adjust the timing of information collection according to the patient's emotions and reduce stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] In the system, the collection unit analyzes the patient's past treatment history and selects the optimal information collection method. The collection unit analyzes the patient's past treatment history and selects the optimal information collection method. For example, the collection unit suggests the optimal information collection method based on treatment methods used by the patient in the past. The collection unit can also select the effective timing for information collection from the patient's past treatment history. Furthermore, the collection unit can analyze the patient's past treatment history and efficiently collect necessary information. As a result, the collection unit can select the optimal information collection method based on the past treatment history and achieve efficient information collection.

[0070] In the system, when the collection unit collects basic information, the collection unit filters the information based on the patient's lifestyle and environment. When collecting basic information, the collection unit filters the information based on the patient's lifestyle and environment. For example, the collection unit prioritizes collecting highly relevant information based on the patient's lifestyle. The collection unit can also filter and collect necessary information based on the patient's environment. Furthermore, the collection unit can determine the priority of the information to be collected, taking into account the patient's lifestyle and environment. This allows the collection unit to filter information based on the patient's lifestyle and environment and collect highly relevant information.

[0071] In the system, the collection unit estimates the patient's emotions and determines the priority of information to be collected based on the estimated patient emotions. The collection unit estimates the patient's emotions and determines the priority of information to be collected based on the estimated patient emotions. For example, if the patient is feeling stressed, the collection unit prioritizes collecting important information. Also, if the patient is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the patient is busy, the collection unit can prioritize collecting the minimum amount of information necessary. In this way, the collection unit can prioritize information according to the patient's emotions and prioritize collecting important information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0072] In the system, when the collection unit collects basic information, it prioritizes collecting highly relevant information by taking into account the patient's geographical location information. When collecting basic information, the collection unit prioritizes collecting highly relevant information by taking into account the patient's geographical location information. For example, the collection unit prioritizes collecting highly relevant information based on the patient's current location. The collection unit can also collect necessary information by referring to the patient's past location information. Furthermore, the collection unit can also determine the priority of information to be collected by taking into account the patient's geographical location information. This allows the collection unit to prioritize collecting highly relevant information by taking into account the patient's geographical location information.

[0073] In the system, when the collection unit collects basic information, the collection unit analyzes the patient's social media activity and collects related information. When the collection unit collects basic information, the collection unit analyzes the patient's social media activity and collects related information. For example, the collection unit collects related health information from the patient's social media activity. The collection unit can also analyze the content of the patient's social media posts and collect necessary information. Furthermore, the collection unit can determine the priority of information to be collected with reference to the patient's social media activity. This allows the collection unit to analyze the patient's social media activity and collect related information.

[0074] In the system, the management unit estimates the patient's emotions and adjusts the medication schedule management method based on the estimated patient emotions. The management unit estimates the patient's emotions and adjusts the medication schedule management method based on the estimated patient emotions. For example, if the patient is feeling stressed, the management unit sets the medication schedule to a time when the patient is relaxed. If the patient is relaxed, the management unit can provide a detailed medication schedule. Furthermore, if the patient is busy, the management unit can provide a concise medication schedule. This allows the management unit to adjust the medication schedule management method according to the patient's emotions and reduce stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] In the system, when the management unit manages medication schedules, it sets an optimal schedule by referring to the patient's past medication history. When managing medication schedules, the management unit sets an optimal schedule by referring to the patient's past medication history. For example, the management unit sets an optimal medication schedule based on the patient's past medication history. The management unit can also select effective medication timing from the patient's past medication history. Furthermore, the management unit can analyze the patient's past medication history and efficiently set the necessary schedule. This allows the management unit to set an optimal schedule based on the patient's past medication history, thereby achieving efficient medication management.

[0076] In the system, when the management unit manages a medication schedule, the management unit customizes the schedule based on the patient's lifestyle rhythm. When the management unit manages a medication schedule, the management unit customizes the schedule based on the patient's lifestyle rhythm. For example, the management unit sets an optimal medication schedule based on the patient's lifestyle rhythm. The management unit can also customize the medication schedule taking the patient's lifestyle rhythm into consideration. Furthermore, the management unit can also adjust the medication schedule to match the patient's lifestyle rhythm. In this way, the management unit can customize the schedule based on the patient's lifestyle rhythm and promote continued medication administration.

[0077] In the system, the management unit estimates the patient's emotions and adjusts the timing of alert transmission based on the estimated patient emotions. The management unit estimates the patient's emotions and adjusts the timing of alert transmission based on the estimated patient emotions. For example, if the patient is feeling stressed, the management unit sends an alert at a time when the patient is able to relax. The management unit can also send a detailed alert if the patient is relaxed. Furthermore, the management unit can also send a concise alert if the patient is busy. This allows the management unit to adjust the timing of alert transmission according to the patient's emotions and reduce stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] In the system, when the management unit manages a medication schedule, the management unit selects the optimal alert transmission method taking into account the patient's geographical location information. When managing a medication schedule, the management unit selects the optimal alert transmission method taking into account the patient's geographical location information. For example, the management unit selects the optimal alert transmission method based on the patient's current location. The management unit can also select the alert transmission method by referring to the patient's past location information. Furthermore, the management unit can also determine the alert transmission method by taking into account the patient's geographical location information. This allows the management unit to select the optimal alert transmission method by taking into account the patient's geographical location information.

[0079] In the system, when the management unit manages a medication schedule, the management unit analyzes the patient's social media activity and customizes the content of the alert. When the management unit manages a medication schedule, the management unit analyzes the patient's social media activity and customizes the content of the alert. For example, the management unit customizes the relevant alert content based on the patient's social media activity. The management unit can also analyze the patient's social media posts and adjust the alert content. Furthermore, the management unit can also determine the alert content based on the patient's social media activity. This allows the management unit to analyze the patient's social media activity and customize the relevant alert content.

[0080] In the system, the providing unit estimates the patient's emotions and adjusts the way the message is expressed based on the estimated patient's emotions. The providing unit estimates the patient's emotions and adjusts the way the message is expressed based on the estimated patient's emotions. For example, if the patient is feeling stressed, the providing unit can send an encouraging message. If the patient is relaxed, the providing unit can also send a detailed message. Furthermore, if the patient is busy, the providing unit can also send a concise message. This allows the providing unit to adjust the way the message is expressed depending on the patient's emotions and maintain their motivation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] In the system, when the providing unit provides a message, it adjusts the level of detail of the message based on the patient's treatment progress status. When the providing unit provides a message, it adjusts the level of detail of the message based on the patient's treatment progress status. For example, the providing unit provides a detailed message based on the patient's treatment progress status. The providing unit can also adjust the level of detail of the message taking into account the patient's treatment progress status. Furthermore, the providing unit can also determine the content of the message in accordance with the patient's treatment progress status. In this way, the providing unit can adjust the level of detail of the message based on the patient's treatment progress status and provide appropriate information.

[0082] In the system, when the providing unit provides a message, it applies different message algorithms depending on the patient's treatment category. When providing a message, the providing unit applies different message algorithms depending on the patient's treatment category. For example, the providing unit applies an optimal message algorithm based on the patient's treatment category. The providing unit can also adjust the message algorithm taking the patient's treatment category into consideration. Furthermore, the providing unit can also determine a message algorithm in accordance with the patient's treatment category. This allows the providing unit to apply the optimal message algorithm depending on the patient's treatment category and provide an effective message.

[0083] In the system, the providing unit estimates the patient's emotions and adjusts the length of the message based on the estimated patient's emotions. The providing unit estimates the patient's emotions and adjusts the length of the message based on the estimated patient's emotions. For example, if the patient is feeling stressed, the providing unit can send a short, to-the-point message. If the patient is relaxed, the providing unit can also send a detailed message. Furthermore, if the patient is busy, the providing unit can also send a concise message. This allows the providing unit to adjust the length of the message according to the patient's emotions and provide appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] In the system, when the providing unit provides a message, it determines the priority of the message based on the start date of the patient's treatment. When the providing unit provides a message, it determines the priority of the message based on the start date of the patient's treatment. For example, the providing unit provides an optimal message based on the start date of the patient's treatment. The providing unit can also determine the priority of the message taking into account the start date of the patient's treatment. Furthermore, the providing unit can also determine the content of the message in accordance with the start date of the patient's treatment. In this way, the providing unit can determine the priority of the message based on the start date of the patient's treatment and provide appropriate information.

[0085] In the system, when the providing unit provides messages, the providing unit adjusts the order of messages based on the patient's treatment-related information. When providing messages, the providing unit adjusts the order of messages based on the patient's treatment-related information. For example, the providing unit provides an optimal message order based on the patient's treatment-related information. The providing unit can also adjust the order of messages taking into account the patient's treatment-related information. Furthermore, the providing unit can determine the order of messages in accordance with the patient's treatment-related information. This allows the providing unit to adjust the order of messages based on the patient's treatment-related information and provide appropriate information.

[0086] In the system, the answering unit estimates the patient's emotions and adjusts the way the answer is expressed based on the estimated patient's emotions. The answering unit estimates the patient's emotions and adjusts the way the answer is expressed based on the estimated patient's emotions. For example, if the patient is stressed, the answering unit answers in gentle words. If the patient is relaxed, the answering unit can provide a detailed answer. Furthermore, if the patient is busy, the answering unit can provide a concise answer. This allows the answering unit to adjust the way the answer is expressed depending on the patient's emotions and provide appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] In the system, when the answering unit provides an answer, it refers to the patient's past question history to provide the most appropriate answer. When the answering unit provides an answer, it refers to the patient's past question history to provide the most appropriate answer. For example, the answering unit provides the most appropriate answer based on the patient's past question history. The answering unit can also provide a relevant answer from the patient's past question history. Furthermore, the answering unit can analyze the patient's past question history and efficiently provide the necessary answer. This allows the answering unit to provide the most appropriate answer based on the patient's past question history, thereby achieving efficient information provision.

[0088] The system adjusts the level of detail of the answer based on the patient's treatment status when the answering unit provides an answer. The answering unit adjusts the level of detail of the answer based on the patient's treatment status when providing an answer. For example, the answering unit provides a detailed answer based on the patient's treatment status. The answering unit can also adjust the level of detail of the answer taking the patient's treatment status into consideration. Furthermore, the answering unit can determine the content of the answer in accordance with the patient's treatment status. This allows the answering unit to adjust the level of detail of the answer based on the patient's treatment status and provide appropriate information.

[0089] In the system, the answering unit estimates the patient's emotions and determines the priority of answers based on the estimated patient's emotions. The answering unit estimates the patient's emotions and determines the priority of answers based on the estimated patient's emotions. For example, if the patient is stressed, the answering unit can prioritize answers to important questions. Also, if the patient is relaxed, the answering unit can prioritize answers to detailed questions. Furthermore, if the patient is busy, the answering unit can prioritize answers to concise questions. In this way, the answering unit can prioritize answers according to the patient's emotions and prioritize answers to important questions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The system provides an optimal answer by the answering unit taking into consideration the geographical location information of the patient when providing an answer. The answering unit provides an optimal answer by taking into consideration the geographical location information of the patient when providing an answer. For example, the answering unit provides an optimal answer based on the patient's current location. The answering unit can also provide an answer by referring to the patient's past location information. Furthermore, the answering unit can also determine the content of the answer by taking into consideration the geographical location information of the patient. This allows the answering unit to provide an optimal answer by taking into consideration the geographical location information of the patient.

[0091] The system analyzes the patient's social media activity and provides a relevant answer when the answering unit provides an answer. When the answering unit provides an answer, the system analyzes the patient's social media activity and provides a relevant answer. For example, the answering unit provides an answer to a related question from the patient's social media activity. The answering unit can also analyze the content of the patient's social media posts and provide the necessary answer. Furthermore, the answering unit can also determine the content of the answer with reference to the patient's social media activity. This allows the answering unit to analyze the patient's social media activity and provide an answer to a related question.

[0092] In the system, the analysis unit estimates the patient's emotions and adjusts the analysis method of the treatment data based on the estimated patient emotions. The analysis unit estimates the patient's emotions and adjusts the analysis method of the treatment data based on the estimated patient emotions. For example, if the patient is feeling stressed, the analysis unit analyzes the treatment data during a time when the patient is able to relax. Furthermore, if the patient is relaxed, the analysis unit can perform a detailed analysis of the treatment data. Furthermore, if the patient is busy, the analysis unit can perform a concise analysis of the treatment data. This allows the analysis unit to adjust the analysis method of the treatment data according to the patient's emotions and provide appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] In the system, when the analysis unit analyzes treatment data, it refers to past treatment data and applies the optimal analysis algorithm. When analyzing treatment data, the analysis unit refers to past treatment data and applies the optimal analysis algorithm. For example, the analysis unit applies the optimal analysis algorithm based on past treatment data. The analysis unit can also select an effective analysis algorithm from past treatment data. Furthermore, the analysis unit can analyze past treatment data and efficiently apply the necessary algorithm. This allows the analysis unit to apply the optimal analysis algorithm based on past treatment data, thereby achieving efficient data analysis.

[0094] In the system, when the analysis unit analyzes the treatment data, it applies different analysis methods depending on the patient's treatment category. When analyzing the treatment data, the analysis unit applies different analysis methods depending on the patient's treatment category. For example, the analysis unit applies an optimal analysis method based on the patient's treatment category. The analysis unit can also adjust the analysis method taking into account the patient's treatment category. Furthermore, the analysis unit can determine the analysis method in accordance with the patient's treatment category. This allows the analysis unit to apply the optimal analysis method depending on the patient's treatment category, thereby achieving effective data analysis. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, management unit, provision unit, response unit, and analysis unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects basic information about the patient using sensors and a questionnaire function of the smart device 14. The management unit manages the medication schedule and sends alerts using the specific processing unit 290 of the data processing device 12. The provision unit provides encouraging messages and treatment progress reports using the control unit 46A of the smart device 14. The response unit answers the patient's questions and concerns in real time using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes treatment data using the specific processing unit 290 of the data processing device 12 and provides feedback to the doctor. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, management unit, provision unit, response unit, and analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects basic information about the patient using sensors or a questionnaire function of the smart glasses 214. The management unit manages medication schedules and sends alerts via the specific processing unit 290 of the data processing device 12. The provision unit provides encouraging messages and treatment progress reports via the control unit 46A of the smart glasses 214. The response unit answers the patient's questions and concerns in real time via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes treatment data via the specific processing unit 290 of the data processing device 12 and provides feedback to the doctor. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, management unit, provision unit, response unit, and analysis unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects basic information about the patient using sensors or a questionnaire function of the headset terminal 314. The management unit manages the medication schedule and sends alerts using the specific processing unit 290 of the data processing device 12. The provision unit provides encouraging messages and treatment progress reports using the control unit 46A of the headset terminal 314. The response unit answers the patient's questions and concerns in real time using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes treatment data using the specific processing unit 290 of the data processing device 12 and provides feedback to the doctor. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, management unit, provision unit, response unit, and analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects basic information about the patient using sensors or a questionnaire function of the robot 414. The management unit manages the medication schedule and sends alerts using the specific processing unit 290 of the data processing device 12. The provision unit provides encouraging messages and treatment progress reports using the control unit 46A of the robot 414. The response unit answers the patient's questions and concerns in real time using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes treatment data using the specific processing unit 290 of the data processing device 12 and provides feedback to the doctor.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] In this system, the collection unit collects the patient's dietary data, and the management unit adjusts the medication schedule based on that data. For example, the collection unit records the time and content of the patient's meals, and the management unit optimizes medication times based on that data. The collection unit can also analyze the nutritional balance from the patient's dietary content, and the management unit can adjust the medication schedule based on the results. Furthermore, the collection unit can provide the patient's dietary data to the doctor, which can use it as a reference when reviewing the treatment plan. This allows the system to utilize the patient's dietary data to provide more effective treatment support.

[0097] In this system, the collection unit collects the patient's exercise data, and the management unit adjusts the medication schedule based on that data. For example, the collection unit records the type and duration of exercise performed by the patient, and the management unit optimizes medication times based on that data. The collection unit can also analyze changes in physical strength from the patient's exercise data, and the management unit can adjust the medication schedule based on the results. Furthermore, the collection unit can provide the patient's exercise data to the doctor, which the doctor can use as a reference when reviewing the treatment plan. This allows the system to utilize the patient's exercise data to provide more effective treatment support.

[0098] The system's management unit can adjust medication schedules based on the patient's sleep data. For example, the management unit can record the patient's sleep duration and quality and optimize medication times based on that data. The management unit can also analyze the patient's fatigue level from the sleep data and adjust medication schedules based on the results. Furthermore, the management unit can provide the patient's sleep data to doctors, who can use it as a reference when reviewing treatment plans. This allows the system to utilize the patient's sleep data to provide more effective treatment support.

[0099] The system allows the providing unit to customize the content of the message based on the patient's hobbies and interests. For example, the providing unit sends a message related to the patient's favorite sports or music. The providing unit can also customize an encouraging message based on the patient's hobbies. Furthermore, the providing unit can provide information about treatment tailored to the patient's interests. This allows the system to provide messages that take the patient's hobbies and interests into consideration and maintain their motivation.

[0100] The system allows the provider to adjust the content of the message based on the patient's treatment goals. For example, the provider sends an appropriate message depending on the short-term and long-term goals set by the patient. The provider can also customize encouraging messages based on the patient's goal achievement status. Furthermore, the provider can provide treatment advice in line with the patient's goals. This allows the system to provide messages that take into account the patient's treatment goals and maintain motivation.

[0101] In the system, the collection unit can estimate the patient's emotions and adjust the method of collecting basic information based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can collect basic information using simple questions. If the patient is relaxed, the collection unit can also collect basic information using detailed questions. Furthermore, if the patient is busy, the collection unit can collect the minimum amount of basic information necessary in a short amount of time. In this way, the collection unit can adjust the method of collecting information according to the patient's emotions and reduce stress.

[0102] The system allows the management unit to estimate the patient's emotions and adjust the method of reminding the patient about their medication schedule based on the estimated patient emotions. For example, if the patient is feeling stressed, the management unit can send a gentle reminder. If the patient is feeling relaxed, the management unit can also send a detailed reminder. Furthermore, if the patient is busy, the management unit can also send a brief reminder. In this way, the management unit can adjust the reminder method according to the patient's emotions and reduce stress.

[0103] In the system, the providing unit can estimate the patient's emotions and adjust the tone of the message based on the estimated patient's emotions. For example, if the patient is feeling stressed, the providing unit can send the message in a gentle tone. If the patient is relaxed, the providing unit can also send the message in an encouraging tone. Furthermore, if the patient is busy, the providing unit can send the message in a concise tone. In this way, the providing unit can adjust the tone of the message according to the patient's emotions and maintain motivation.

[0104] The system allows the answering unit to estimate the patient's emotions and adjust the timing of the answer based on the estimated patient's emotions. For example, if the patient is feeling stressed, the answering unit can provide a quick answer. If the patient is relaxed, the answering unit can also provide a detailed answer. Furthermore, if the patient is busy, the answering unit can also provide a concise answer. This allows the answering unit to adjust the timing of the answer according to the patient's emotions and provide appropriate information.

[0105] In the system, the analysis unit can estimate the patient's emotions and adjust the method of feedback of treatment data based on the estimated patient's emotions. For example, the analysis unit can provide brief feedback if the patient is stressed. The analysis unit can also provide detailed feedback if the patient is relaxed. Furthermore, the analysis unit can provide feedback that is concise if the patient is busy. In this way, the analysis unit can adjust the feedback method according to the patient's emotions and provide appropriate information.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The collection unit collects basic information about the patient. The basic information includes the patient's name, age, medical history, etc. The collection unit collects the basic information about the patient using a questionnaire. The collection unit can also collect biological data about the patient using sensors. For example, the collection unit measures the patient's heart rate and body temperature using a wearable device and collects this as basic information. Step 2: The management unit manages the medication schedule based on the information collected by the collection unit and sends alerts. The medication schedule includes medication times and dosages. The management unit sends alerts to the patient when it's time to take the medication. Methods for sending alerts include email, SMS, and app notifications. For example, the management unit sends an app notification to the patient's smartphone to notify them of medication times. The management unit can also record the patient's medication history to help manage the medication schedule. Step 3: The delivery department provides messages to maintain the patient's motivation based on the schedule managed by the administration department. These messages include words of encouragement and information on the progress of treatment. For example, messages such as "Let's try our best to take the medication today!" or "Treatment is going well!" are sent. The delivery department can also analyze the patient's treatment data and provide feedback to the doctor as needed. For example, the delivery department can report to the doctor the rate of continued medication use and the occurrence of side effects, which can be used to review the treatment plan.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0164] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0169] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0170] 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.

[0171] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0172] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0173] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0174] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0177] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department for collecting basic patient information; a management unit that manages a dosing schedule based on the information collected by the collection unit and sends alerts; a providing unit that provides a message to maintain the patient's motivation based on the schedule managed by the managing unit. A system characterized by:

2. Equipped with a response section that immediately responds to questions and concerns from patients The system of claim 1 .

3. Equipped with an analysis unit that analyzes treatment data and provides feedback to doctors The system of claim 1 .

4. The collecting unit Estimate the patient's emotions and adjust the timing of collecting basic information based on the estimated patient emotions. The system of claim 1 .

5. The collecting unit Analyze the patient's past medical history and select the appropriate method of collecting information The system of claim 1 .

6. The collecting unit When collecting basic information, filter it based on the patient's lifestyle and environment. The system of claim 1 .

7. The collecting unit Estimate the patient's feelings and prioritize the information to be collected based on the estimated patient's feelings. The system of claim 1 .

8. The collecting unit When collecting basic information, prioritize collecting relevant information based on the patient's geographic location. The system of claim 1 .

9. The collecting unit When collecting basic information, analyze the patient's social media activity and collect relevant information. The system of claim 1 .

10. The management unit Estimate the patient's feelings and adjust the administration of the medication schedule based on the estimated patient feelings. The system of claim 1 .

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