System

Abstract

An object of a system according to an embodiment is to efficiently generate and provide an individual care plan for a dementia patient.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects personal information, medical history, and lifestyle data of a patient. The analysis unit analyzes the data collected by the collection unit and analyzes the state of the patient in detail. The generator generates a care plan based on the analysis result obtained by the analyzer. The providing unit provides the care plan generated by the generating unit to the patient, his / her family, and the healthcare professional.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently generate and provide individual care plans for dementia patients.

[0005] The system according to the embodiment aims to efficiently generate and provide individual care plans for dementia patients. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on the patient's personal information, medical history, and lifestyle habits. The analysis unit analyzes the data collected by the collection unit and analyzes the patient's condition in detail. The generation unit generates a care plan based on the analysis results obtained by the analysis unit. The provision unit provides the care plan generated by the generation unit to the patient, their family, and medical professionals. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate and provide individualized care plans for dementia patients. [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 care plan proposal system according to an embodiment of the present invention utilizes a generative AI to propose personalized care plans for dementia patients. The system collects data, such as the patient's personal information, medical history, and lifestyle habits, and the generative AI analyzes this data to generate a care plan optimized for the patient's condition. This care plan includes support for daily living, rehabilitation, medication management, and other information. The generated care plan is then provided to the patient, their family, and healthcare professionals for use in actual care. For example, the care plan proposal system collects detailed information about the patient, such as their age, gender, medical history, current symptoms, and daily life patterns. For example, the system collects information about the patient's diet, exercise routine, and medications. The system then uses a generative AI to analyze the collected data. Based on the collected data, the generative AI analyzes the patient's condition in detail and generates an optimal care plan. For example, the system analyzes the patient's eating patterns and exercise habits to propose a nutritionally balanced meal plan and an appropriate exercise program. The system also considers the patient's medical history and current symptoms to propose necessary rehabilitation and medication management methods. The care plan proposal system then provides the generated care plan to the patient, their family, and medical professionals. For example, the meal plan and exercise program proposed by the generation AI are provided so that the patient and their family can put them into practice in their daily lives. In addition, the rehabilitation and medication management methods are used to help medical professionals provide appropriate care to the patient. This allows the care plan proposal system to provide care plans tailored to each individual dementia patient, improving their quality of life. This allows the care plan proposal system to provide care plans tailored to each individual dementia patient, improving their quality of life. For example, implementing a nutritionally balanced meal plan and an appropriate exercise program may improve the patient's health. In addition, appropriate rehabilitation and medication management methods are expected to slow the progression of symptoms.

[0029] A care plan proposal system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on a patient's personal information, medical history, and lifestyle habits. The patient's personal information includes, but is not limited to, for example, name, age, gender, and address. The medical history includes, but is not limited to, for example, past diagnostic results, treatment history, and medical history. The lifestyle habits include, but are not limited to, for example, diet, exercise, sleep, smoking, and alcohol consumption. The collection unit collects detailed information, such as the patient's age, gender, medical history, current symptoms, and daily living patterns. The analysis unit analyzes the data collected by the collection unit and analyzes the patient's condition in detail. The analysis is performed based on, for example, but is not limited to, the algorithm used, the depth of analysis, and the analysis items. For example, the analysis unit analyzes the patient's condition in detail based on the collected data. The generation unit generates a care plan based on the analysis results obtained by the analysis unit. The generated care plan includes, but is not limited to, for example, a treatment plan, lifestyle guidance, and rehabilitation plan. For example, the generation unit generates a nutritionally balanced meal plan and an appropriate exercise program based on the analysis results. The provision unit provides the care plan generated by the generation unit to the patient, his / her family, and medical professionals. The provision may be in a form such as face-to-face, online, or in writing, but is not limited to these examples. For example, the provision unit provides the generated care plan to the patient, his / her family, and medical professionals. As a result, the care plan proposal system according to the embodiment can propose a care plan tailored to each individual dementia patient.

[0030] The care plan proposal system includes an instruction unit that provides instruction on how to put the care plan into practice. The instruction unit provides instruction on how to put the care plan into practice. The instruction may be provided, for example, in person, online, in writing, or in any other form, but is not limited to these examples. For example, the instruction unit provides instruction to patients, their families, and medical professionals on how to put the generated care plan into practice. The instruction unit provides instruction on, for example, how to put into practice a diet plan. The instruction unit can also provide instruction on, for example, how to put into practice an exercise program. The instruction unit can also provide instruction on, for example, how to put into practice rehabilitation. Thus, by providing instruction on how to put the care plan into practice, patients, their families, and medical professionals can properly put the care plan into practice.

[0031] The care plan proposal system includes a feedback unit that accepts feedback. The feedback unit accepts feedback. The feedback may be in the form of, for example, a questionnaire, an interview, an online form, or the like, but is not limited to these examples. For example, the feedback unit accepts feedback from patients, their families, and medical professionals regarding the generated care plan. For example, the feedback unit accepts feedback regarding the results of implementing the care plan. The feedback unit can also accept feedback regarding areas for improvement in the care plan. The feedback unit can also accept feedback regarding the effectiveness of the care plan. As a result, accepting feedback makes it possible to improve or adjust the care plan.

[0032] The collection unit can analyze the patient's past medical history and select the optimal data collection method. The collection unit, for example, prioritizes the collection of important data items based on the patient's past medical records. The collection unit can also, for example, refer to the patient's past treatment history to focus on collecting data related to a specific symptom. The collection unit can also, for example, collect additional necessary data based on the patient's past test results. In this way, important data can be collected preferentially by selecting the optimal data collection method based on the patient's past medical history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0033] The collection unit can filter data based on the patient's current living situation and areas of interest when collecting data. For example, the collection unit prioritizes collecting data related to the patient's current hobbies and activities. The collection unit can also select an appropriate data collection method depending on the patient's living environment (home, facility, etc.). The collection unit can also collect related data based on the patient's areas of interest (music, sports, etc.). This allows for filtering data based on the patient's living situation and areas of interest, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0034] When collecting data, the collection unit can select an appropriate collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit collects data using voice recognition technology. Alternatively, if the patient prefers text input, the collection unit can collect data by providing a simple input form. Alternatively, if the patient prefers image input, the collection unit can collect data using photos or videos. This improves the efficiency of data collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0035] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the patient. For example, when the patient is at home, the collection unit prioritizes collecting lifestyle habit data at home. Furthermore, for example, when the patient is out, the collection unit can prioritize collecting activity data while away from home. Furthermore, for example, when the patient is in a specific facility, the collection unit can prioritize collecting lifestyle data at the facility. In this way, by collecting data by taking into account the geographical location information of the patient, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The collection unit can analyze the patient's social media activity and collect relevant data when collecting data. The collection unit can collect relevant data, for example, based on information shared by the patient on social media. The collection unit can also analyze the patient's social media activity patterns and collect lifestyle habit data. The collection unit can also collect social data, for example, by referring to the patient's social media friendships. This allows for the collection of more relevant data by analyzing the patient's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI.

[0037] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting data. The collection unit can improve the data collection method, for example, based on feedback provided by the patient in the past. The collection unit can also adjust the type of data to be collected, for example, based on the patient's past feedback. The collection unit can also adjust the frequency of data collection, for example, by referring to the patient's feedback. In this way, the data collection method can be improved by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, performs a detailed analysis of important data items. The analysis unit can also perform a concise analysis of general data items. The analysis unit can also perform a particularly detailed analysis of data directly related to the patient's condition, for example. By adjusting the level of detail of the analysis based on the importance of the data, detailed analysis can be performed on important data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data. The analysis unit can also apply a fitness analysis algorithm to exercise data. The analysis unit can also apply a drug management analysis algorithm to medication data. By applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also extract specific patterns from the patient's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also focus on analysis of data collected during a specific period, for example. The analysis unit can also compare past data with current data for analysis, for example. This allows the latest data to be analyzed preferentially by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of directly related data. The analysis unit can also postpone analysis of indirectly related data, for example. The analysis unit can also evaluate the relevance of the data and perform analysis in an optimal order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0043] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has specialized knowledge, the analysis unit can use detailed technical terms. Furthermore, for example, if the patient has only general knowledge, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can provide the analysis results by selecting appropriate technical terms according to the patient's level of knowledge. In this way, by adjusting the technical terms in the analysis according to the patient's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0044] When generating a care plan, the generation unit can adjust the level of detail of the generation based on the importance of the analysis results. The generation unit generates a detailed care plan based on, for example, important analysis results. The generation unit can also generate a concise care plan based on, for example, general analysis results. The generation unit can also generate a particularly detailed care plan based on, for example, analysis results directly related to the patient's condition. In this way, by adjusting the level of detail of the generation based on the importance of the analysis results, it is possible to provide a detailed care plan based on important analysis results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0045] When generating a care plan, the generation unit can apply different generation algorithms depending on the patient's lifestyle habits. The generation unit can generate a nutritionally balanced meal plan based on, for example, eating habits. The generation unit can also generate an appropriate exercise program based on, for example, exercise habits. The generation unit can also generate a medication management plan based on, for example, medication habits. By applying different generation algorithms depending on the patient's lifestyle habits, a more personalized care plan can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI.

[0046] When generating a care plan, the generation unit can improve the accuracy of the generation by referring to the patient's past care plans. The generation unit, for example, corrects the current care plan based on the patient's past care plans. The generation unit can also extract specific patterns from the patient's past care plans and reflect them in the generation. The generation unit can also adjust the generation algorithm by referring to the patient's past care plans. In this way, the accuracy of the generation can be improved by referring to the patient's past care plans. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0047] When generating a care plan, the generation unit can determine the priority of generation based on the time of data collection. The generation unit, for example, generates a care plan preferentially based on recently collected data. The generation unit can also generate a care plan with a focus on data collected during a specific period, for example. The generation unit can also generate an optimal care plan by comparing past data with current data, for example. In this way, by determining the priority of generation based on the time of data collection, it is possible to provide a care plan based on the latest data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The generation unit can adjust the order of generation based on the relevance of data when generating a care plan. For example, the generation unit generates a care plan preferentially based on directly related data. The generation unit can also generate a care plan, for example, by deferring to indirectly related data. The generation unit can also evaluate the relevance of data and generate a care plan in an optimal order. This enables efficient generation of a care plan by adjusting the order of generation based on the relevance of data. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0049] When generating a care plan, the generation unit can adjust the use of technical terminology in the generation according to the patient's level of expertise. For example, if the patient has specialized knowledge, the generation unit generates the care plan using detailed technical terminology. Furthermore, for example, if the patient has only general knowledge, the generation unit can also generate the care plan using simple language. Furthermore, the generation unit can also generate the care plan by selecting appropriate technical terminology according to the patient's level of knowledge. This allows for the provision of a more understandable care plan by adjusting the technical terminology in the generation according to the patient's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0050] When providing a care plan, the providing unit can select the optimal providing method by referring to the patient's past feedback. The providing unit can improve the providing method, for example, based on feedback provided by the patient in the past. The providing unit can also adjust the type of information to be provided, for example, based on the patient's past feedback. The providing unit can also adjust the frequency of provision, for example, by referring to the patient's past feedback. In this way, the providing method can be improved by referring to the patient's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0051] When providing a care plan, the providing unit can customize the content to be provided based on the patient's current living situation. For example, if the patient is at home, the providing unit provides a care plan that can be practiced at home. Furthermore, for example, if the patient is out, the providing unit can also provide a care plan that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the providing unit can also provide a care plan that can be practiced at the facility. In this way, by customizing the content to be provided based on the patient's living situation, a more practical care plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The providing unit can improve the provision method by reflecting patient feedback when providing a care plan. For example, if the patient provides feedback on the provided care plan, the providing unit improves the provision method based on that feedback. The providing unit can also adjust the type of information to be provided, for example, based on the patient's feedback. The providing unit can also adjust the frequency of provision, for example, by referring to the patient's feedback. In this way, the provision method can be improved by reflecting the patient's feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0053] When providing a care plan, the providing unit can select the optimal providing method by taking into account the patient's geographical location information. For example, if the patient is at home, the providing unit can provide a care plan that can be practiced at home. Furthermore, for example, if the patient is out, the providing unit can also provide a care plan that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the providing unit can also provide a care plan that can be practiced at that facility. In this way, by taking into account the patient's geographical location information, a more appropriate care plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When providing a care plan, the providing unit can analyze the patient's social media activity and suggest content to be provided. The providing unit can provide a relevant care plan based on, for example, information shared by the patient on social media. The providing unit can also analyze, for example, the patient's social media activity patterns and provide a care plan that suits their lifestyle. The providing unit can also provide a social care plan based on, for example, the patient's friendships on social media. In this way, by analyzing the patient's social media activity, a more relevant care plan can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0055] The providing unit can customize the provision method by reflecting the patient's past feedback when providing a care plan. The providing unit can improve the provision method, for example, based on feedback provided by the patient in the past. The providing unit can also adjust the type of information to be provided, for example, based on the patient's past feedback. The providing unit can also adjust the frequency of provision, for example, by referring to the patient's feedback. In this way, the provision method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When providing instruction, the instruction unit can select the optimal instruction method by referring to the patient's past instruction history. The instruction unit can, for example, improve the current instruction method based on the patient's past instruction history. The instruction unit can also, for example, extract specific patterns from the patient's past instruction history and reflect them in the instruction. The instruction unit can also, for example, adjust the instruction algorithm by referring to the patient's past instruction history. In this way, the instruction method can be improved by referring to the patient's past instruction history. Some or all of the above-mentioned processing in the instruction unit can be performed, for example, using AI, or can be performed without using AI.

[0057] The instruction unit can customize the instruction content based on the patient's current living situation when providing instruction. For example, if the patient is at home, the instruction unit can provide instruction content that can be practiced at home. Furthermore, for example, if the patient is out, the instruction unit can provide instruction content that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the instruction unit can provide instruction content that can be practiced at the facility. In this way, by customizing the instruction content based on the patient's living situation, more practical instruction can be provided. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The instruction unit can improve the instruction method by reflecting patient feedback during instruction. For example, if the patient provides feedback on the instruction provided, the instruction unit improves the instruction method based on that feedback. The instruction unit can also adjust the type of information to be instructed, for example, based on patient feedback. The instruction unit can also adjust the frequency of instruction, for example, based on patient feedback. In this way, the instruction method can be improved by reflecting patient feedback. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0059] When providing instruction, the instruction unit can select the optimal instruction method by taking into account the patient's geographical location information. For example, if the patient is at home, the instruction unit can provide instruction content that can be practiced at home. Furthermore, for example, if the patient is out, the instruction unit can also provide instruction content that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the instruction unit can also provide instruction content that can be practiced at that facility. In this way, more appropriate instruction can be provided by taking into account the patient's geographical location information. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0060] When providing guidance, the guidance unit can analyze the patient's social media activity and suggest guidance content. The guidance unit can provide relevant guidance content based on, for example, information shared by the patient on social media. The guidance unit can also analyze the patient's social media activity patterns and provide guidance content tailored to their lifestyle habits. The guidance unit can also provide social guidance content based on, for example, the patient's social media friendships. In this way, by analyzing the patient's social media activity, more relevant guidance content can be provided. Some or all of the above-described processing in the guidance unit can be performed, for example, using AI, or can be performed without using AI.

[0061] The instruction unit can customize the instruction method by reflecting the patient's past feedback when providing instruction. The instruction unit can improve the instruction method, for example, based on feedback provided by the patient in the past. The instruction unit can also adjust the type of information to provide instruction, for example, based on the patient's past feedback. The instruction unit can also adjust the frequency of instruction, for example, by referring to the patient's feedback. In this way, the instruction method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0062] When collecting feedback, the feedback unit can select the optimal collection method by referring to the patient's past feedback history. The feedback unit can, for example, improve the current feedback collection method based on the patient's past feedback history. The feedback unit can also, for example, extract specific patterns from the patient's past feedback history and reflect them in the collection. The feedback unit can also adjust the collection algorithm by referring to the patient's past feedback history. In this way, the collection method can be improved by referring to the patient's past feedback history. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without using AI.

[0063] When collecting feedback, the feedback unit can customize the collected content based on the patient's current living situation. For example, if the patient is at home, the feedback unit can provide feedback that can be practiced at home. Furthermore, for example, if the patient is out, the feedback unit can provide feedback that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the feedback unit can provide feedback that can be practiced at the facility. In this way, by customizing the collected content based on the patient's living situation, more relevant feedback can be collected. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0064] The feedback unit can improve the collection method by reflecting patient feedback when collecting feedback. The feedback unit improves the collection method, for example, based on feedback provided by the patient. The feedback unit can also adjust the type of information to be collected, for example, based on patient feedback. The feedback unit can also adjust the frequency of collection, for example, based on patient feedback. In this way, the collection method can be improved by reflecting patient feedback. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0065] When collecting feedback, the feedback unit can select the optimal collection method by taking into account the patient's geographical location information. For example, if the patient is at home, the feedback unit can provide feedback that can be practiced at home. Furthermore, for example, if the patient is out, the feedback unit can also provide feedback that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the feedback unit can also provide feedback that can be practiced at the facility. In this way, more appropriate feedback can be collected by taking into account the patient's geographical location information. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0066] When collecting feedback, the feedback unit can analyze the patient's social media activity and suggest what to collect. The feedback unit can provide relevant feedback based on, for example, information shared by the patient on social media. The feedback unit can also analyze the patient's social media activity patterns and provide feedback that matches their lifestyle. The feedback unit can also provide social feedback based on, for example, the patient's social media friendships. This allows for more relevant feedback to be collected by analyzing the patient's social media activity. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI.

[0067] The feedback unit can customize the collection method by reflecting the patient's past feedback when collecting feedback. The feedback unit can improve the collection method, for example, based on feedback provided by the patient in the past. The feedback unit can also adjust the type of information to be collected, for example, based on the patient's past feedback. The feedback unit can also adjust the frequency of collection, for example, by referring to the patient's feedback. In this way, the collection method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The care plan proposal system may further include a social network analysis unit that analyzes the patient's social network. The social network analysis unit analyzes the patient's relationships with family, friends, and caregivers to strengthen social support. For example, if the patient feels isolated, the social network analysis unit may suggest local support groups or volunteer activities. Also, if the patient's family is tired of caring for the patient, the social network analysis unit may suggest caregiver refreshment programs or support services. Furthermore, if the patient wants to make new friends, the social network analysis unit may suggest social events with people who share common hobbies. This may strengthen the patient's social network and reduce feelings of isolation.

[0070] The care plan proposal system may further include a recreation suggestion unit that takes into account the patient's hobbies and interests. The recreation suggestion unit suggests appropriate recreational activities based on the patient's hobbies and interests. For example, if the patient enjoys music, the recreation suggestion unit may suggest music therapy or attending a concert. If the patient is interested in painting, the recreation suggestion unit may suggest art classes or attending an art exhibition. Furthermore, if the patient loves nature, the recreation suggestion unit may suggest gardening or nature walks. This can improve the patient's quality of life and support their mental health.

[0071] The care plan proposal system can further include a nutritional analysis unit that analyzes the patient's dietary data and optimizes nutritional balance. The nutritional analysis unit analyzes the patient's dietary data in detail and proposes a nutritionally balanced meal plan. For example, if the patient is deficient in a specific nutrient, the nutritional analysis unit can propose a meal plan to supplement that nutrient. Also, if the patient has specific dietary restrictions, the nutritional analysis unit can propose a meal plan that addresses those restrictions. Furthermore, if the patient has specific health goals, the nutritional analysis unit can propose a meal plan aimed at those goals. This optimizes the patient's nutritional balance and improves their health.

[0072] The care plan proposal system can further include an exercise analysis unit that analyzes the patient's exercise data and proposes an appropriate exercise program. The exercise analysis unit analyzes the patient's exercise data in detail and proposes an individualized exercise program. For example, if the patient is not getting enough exercise, the exercise analysis unit can propose a program that starts with light exercise. Also, if the patient has a specific exercise goal, the exercise analysis unit can propose an exercise program aimed at that goal. Furthermore, if the patient has a specific health condition, the exercise analysis unit can propose an exercise program that corresponds to that condition. This can improve the patient's exercise habits and improve their health.

[0073] The care plan proposal system may further include a sleep analysis unit that analyzes the patient's sleep data and improves sleep quality. The sleep analysis unit analyzes the patient's sleep data in detail and makes suggestions to improve sleep quality. For example, if the patient is sleep deprived, the sleep analysis unit can make suggestions to increase the amount of sleep. Also, if the patient's sleep quality is poor, the sleep analysis unit can suggest relaxation methods or improvements to the sleep environment. Furthermore, if the patient has a specific sleep disorder, the sleep analysis unit can make suggestions that address that disorder. This can improve the patient's sleep quality and overall health.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection department collects the patient's personal information, medical history, and lifestyle data. Personal information includes name, age, gender, address, etc. Medical history includes past diagnoses, treatment history, and medical history. Lifestyle includes diet, exercise, sleep, smoking, and alcohol consumption. The collection department collects this detailed information. Step 2: The analysis unit analyzes the data collected by the collection unit and performs a detailed analysis of the patient's condition. The analysis is performed based on the algorithm used, the depth of analysis, and the analysis items. Step 3: The generation unit generates a care plan based on the analysis results obtained by the analysis unit. The generated care plan includes a treatment plan, lifestyle guidance, rehabilitation plan, etc. Step 4: The provider provides the care plan generated by the generator to patients, their families, and healthcare professionals in the form of face-to-face, online, or written form.

[0076] (Example 2) A care plan proposal system according to an embodiment of the present invention utilizes a generative AI to propose personalized care plans for dementia patients. The system collects data, such as the patient's personal information, medical history, and lifestyle habits, and the generative AI analyzes this data to generate a care plan optimized for the patient's condition. This care plan includes support for daily living, rehabilitation, medication management, and other information. The generated care plan is then provided to the patient, their family, and healthcare professionals for use in actual care. For example, the care plan proposal system collects detailed information about the patient, such as their age, gender, medical history, current symptoms, and daily life patterns. For example, the system collects information about the patient's diet, exercise routine, and medications. The system then uses a generative AI to analyze the collected data. Based on the collected data, the generative AI analyzes the patient's condition in detail and generates an optimal care plan. For example, the system analyzes the patient's eating patterns and exercise habits to propose a nutritionally balanced meal plan and an appropriate exercise program. The system also considers the patient's medical history and current symptoms to propose necessary rehabilitation and medication management methods. The care plan proposal system then provides the generated care plan to the patient, their family, and medical professionals. For example, the meal plan and exercise program proposed by the generation AI are provided so that the patient and their family can put them into practice in their daily lives. In addition, the rehabilitation and medication management methods are used to help medical professionals provide appropriate care to the patient. This allows the care plan proposal system to provide care plans tailored to each individual dementia patient, improving their quality of life. This allows the care plan proposal system to provide care plans tailored to each individual dementia patient, improving their quality of life. For example, implementing a nutritionally balanced meal plan and an appropriate exercise program may improve the patient's health. In addition, appropriate rehabilitation and medication management methods are expected to slow the progression of symptoms.

[0077] A care plan proposal system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on a patient's personal information, medical history, and lifestyle habits. The patient's personal information includes, but is not limited to, for example, name, age, gender, and address. The medical history includes, but is not limited to, for example, past diagnostic results, treatment history, and medical history. The lifestyle habits include, but are not limited to, for example, diet, exercise, sleep, smoking, and alcohol consumption. The collection unit collects detailed information, such as the patient's age, gender, medical history, current symptoms, and daily living patterns. The analysis unit analyzes the data collected by the collection unit and analyzes the patient's condition in detail. The analysis is performed based on, for example, but is not limited to, the algorithm used, the depth of analysis, and the analysis items. For example, the analysis unit analyzes the patient's condition in detail based on the collected data. The generation unit generates a care plan based on the analysis results obtained by the analysis unit. The generated care plan includes, but is not limited to, for example, a treatment plan, lifestyle guidance, and rehabilitation plan. For example, the generation unit generates a nutritionally balanced meal plan and an appropriate exercise program based on the analysis results. The provision unit provides the care plan generated by the generation unit to the patient, his / her family, and medical professionals. The provision may be in a form such as face-to-face, online, or in writing, but is not limited to these examples. For example, the provision unit provides the generated care plan to the patient, his / her family, and medical professionals. As a result, the care plan proposal system according to the embodiment can propose a care plan tailored to each individual dementia patient.

[0078] The care plan proposal system includes an instruction unit that provides instruction on how to put the care plan into practice. The instruction unit provides instruction on how to put the care plan into practice. The instruction may be provided, for example, in person, online, in writing, or in any other form, but is not limited to these examples. For example, the instruction unit provides instruction to patients, their families, and medical professionals on how to put the generated care plan into practice. The instruction unit provides instruction on, for example, how to put into practice a diet plan. The instruction unit can also provide instruction on, for example, how to put into practice an exercise program. The instruction unit can also provide instruction on, for example, how to put into practice rehabilitation. Thus, by providing instruction on how to put the care plan into practice, patients, their families, and medical professionals can properly put the care plan into practice.

[0079] The care plan proposal system includes a feedback unit that accepts feedback. The feedback unit accepts feedback. The feedback may be in the form of, for example, a questionnaire, an interview, an online form, or the like, but is not limited to these examples. For example, the feedback unit accepts feedback from patients, their families, and medical professionals regarding the generated care plan. For example, the feedback unit accepts feedback regarding the results of implementing the care plan. The feedback unit can also accept feedback regarding areas for improvement in the care plan. The feedback unit can also accept feedback regarding the effectiveness of the care plan. As a result, accepting feedback makes it possible to improve or adjust the care plan.

[0080] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions. For example, the collection unit can collect data during times when the patient is relaxed to avoid stress. Furthermore, the collection unit can temporarily suspend data collection and resume it later if the patient feels anxious. Furthermore, the collection unit can also collect data during times when the patient is active to obtain more accurate lifestyle habit data. This allows for more accurate data collection by adjusting the timing of data collection according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI.

[0081] The collection unit can analyze the patient's past medical history and select the optimal data collection method. The collection unit, for example, prioritizes the collection of important data items based on the patient's past medical records. The collection unit can also, for example, refer to the patient's past treatment history to focus on collecting data related to a specific symptom. The collection unit can also, for example, collect additional necessary data based on the patient's past test results. In this way, important data can be collected preferentially by selecting the optimal data collection method based on the patient's past medical history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The collection unit can filter data based on the patient's current living situation and areas of interest when collecting data. For example, the collection unit prioritizes collecting data related to the patient's current hobbies and activities. The collection unit can also select an appropriate data collection method depending on the patient's living environment (home, facility, etc.). The collection unit can also collect related data based on the patient's areas of interest (music, sports, etc.). This allows for filtering data based on the patient's living situation and areas of interest, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0083] When collecting data, the collection unit can select an appropriate collection means depending on the patient's input method. For example, if the patient prefers voice input, the collection unit collects data using voice recognition technology. Alternatively, if the patient prefers text input, the collection unit can collect data by providing a simple input form. Alternatively, if the patient prefers image input, the collection unit can collect data using photos or videos. This improves the efficiency of data collection by selecting the optimal collection means depending on the patient's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, for example, if the patient is relaxed, the collection unit can prioritize collecting daily life data. Furthermore, for example, if the patient is feeling anxious, the collection unit can prioritize collecting data related to the cause of the anxiety. Thus, by determining the priority of data based on the patient's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI.

[0085] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the patient. For example, when the patient is at home, the collection unit prioritizes collecting lifestyle habit data at home. Furthermore, for example, when the patient is out, the collection unit can prioritize collecting activity data while away from home. Furthermore, for example, when the patient is in a specific facility, the collection unit can prioritize collecting lifestyle data at the facility. In this way, by collecting data by taking into account the geographical location information of the patient, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The collection unit can analyze the patient's social media activity and collect relevant data when collecting data. The collection unit can collect relevant data, for example, based on information shared by the patient on social media. The collection unit can also analyze the patient's social media activity patterns and collect lifestyle habit data. The collection unit can also collect social data, for example, by referring to the patient's social media friendships. This allows for the collection of more relevant data by analyzing the patient's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without using AI.

[0087] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting data. The collection unit can improve the data collection method, for example, based on feedback provided by the patient in the past. The collection unit can also adjust the type of data to be collected, for example, based on the patient's past feedback. The collection unit can also adjust the frequency of data collection, for example, by referring to the patient's feedback. In this way, the data collection method can be improved by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, if the patient is relaxed, the analysis unit can provide detailed analysis results. For example, if the patient is feeling anxious, the analysis unit can provide concise and easy-to-understand analysis results. For example, if the patient is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation method of the analysis according to the patient's emotions, more understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, performs a detailed analysis of important data items. The analysis unit can also perform a concise analysis of general data items. The analysis unit can also perform a particularly detailed analysis of data directly related to the patient's condition, for example. By adjusting the level of detail of the analysis based on the importance of the data, detailed analysis can be performed on important data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data. The analysis unit can also apply a fitness analysis algorithm to exercise data. The analysis unit can also apply a drug management analysis algorithm to medication data. By applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. The analysis unit, for example, corrects the current analysis result based on the patient's past analysis results. The analysis unit can also extract specific patterns from the patient's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, if the patient is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the patient is relaxed, the analysis unit can provide a detailed analysis result. For example, if the patient is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis based on the patient's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also focus on analysis of data collected during a specific period, for example. The analysis unit can also compare past data with current data for analysis, for example. This allows the latest data to be analyzed preferentially by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of directly related data. The analysis unit can also postpone analysis of indirectly related data, for example. The analysis unit can also evaluate the relevance of the data and perform analysis in an optimal order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has specialized knowledge, the analysis unit can use detailed technical terms. Furthermore, for example, if the patient has only general knowledge, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can provide the analysis results by selecting appropriate technical terms according to the patient's level of knowledge. In this way, by adjusting the technical terms in the analysis according to the patient's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0096] The generation unit can estimate the patient's emotions and adjust the care plan generation method based on the estimated patient's emotions. For example, if the patient is relaxed, the generation unit can generate a detailed care plan. For example, if the patient is feeling anxious, the generation unit can generate a concise and easy-to-understand care plan. For example, if the patient is feeling excited, the generation unit can generate a visually appealing care plan. This allows for adjusting the care plan generation method based on the patient's emotions to provide a more appropriate care plan. 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 such examples. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or without AI.

[0097] When generating a care plan, the generation unit can adjust the level of detail of the generation based on the importance of the analysis results. The generation unit generates a detailed care plan based on, for example, important analysis results. The generation unit can also generate a concise care plan based on, for example, general analysis results. The generation unit can also generate a particularly detailed care plan based on, for example, analysis results directly related to the patient's condition. In this way, by adjusting the level of detail of the generation based on the importance of the analysis results, it is possible to provide a detailed care plan based on important analysis results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0098] When generating a care plan, the generation unit can apply different generation algorithms depending on the patient's lifestyle habits. The generation unit can generate a nutritionally balanced meal plan based on, for example, eating habits. The generation unit can also generate an appropriate exercise program based on, for example, exercise habits. The generation unit can also generate a medication management plan based on, for example, medication habits. By applying different generation algorithms depending on the patient's lifestyle habits, a more personalized care plan can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI.

[0099] When generating a care plan, the generation unit can improve the accuracy of the generation by referring to the patient's past care plans. The generation unit, for example, corrects the current care plan based on the patient's past care plans. The generation unit can also extract specific patterns from the patient's past care plans and reflect them in the generation. The generation unit can also adjust the generation algorithm by referring to the patient's past care plans. In this way, the accuracy of the generation can be improved by referring to the patient's past care plans. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The generation unit can estimate the patient's emotions and adjust the length of the care plan based on the estimated patient's emotions. For example, if the patient is in a hurry, the generation unit can generate a short and to-the-point care plan. For example, if the patient is relaxed, the generation unit can generate a detailed care plan. For example, if the patient is excited, the generation unit can generate a visually appealing care plan. This allows for adjusting the length of the care plan based on the patient's emotions, thereby providing a more appropriate care plan. 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 such examples. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI.

[0101] When generating a care plan, the generation unit can determine the priority of generation based on the time of data collection. The generation unit, for example, generates a care plan preferentially based on recently collected data. The generation unit can also generate a care plan with a focus on data collected during a specific period, for example. The generation unit can also generate an optimal care plan by comparing past data with current data, for example. In this way, by determining the priority of generation based on the time of data collection, it is possible to provide a care plan based on the latest data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The generation unit can adjust the order of generation based on the relevance of data when generating a care plan. For example, the generation unit generates a care plan preferentially based on directly related data. The generation unit can also generate a care plan, for example, by deferring to indirectly related data. The generation unit can also evaluate the relevance of data and generate a care plan in an optimal order. This enables efficient generation of a care plan by adjusting the order of generation based on the relevance of data. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0103] When generating a care plan, the generation unit can adjust the use of technical terminology in the generation according to the patient's level of expertise. For example, if the patient has specialized knowledge, the generation unit generates the care plan using detailed technical terminology. Furthermore, for example, if the patient has only general knowledge, the generation unit can also generate the care plan using simple language. Furthermore, the generation unit can also generate the care plan by selecting appropriate technical terminology according to the patient's level of knowledge. This allows for the provision of a more understandable care plan by adjusting the technical terminology in the generation according to the patient's level of expertise. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0104] The providing unit can estimate the patient's emotions and adjust the method of providing the care plan based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can provide a care plan that includes detailed explanations. For example, if the patient is feeling anxious, the providing unit can provide a concise and easy-to-understand care plan. For example, if the patient is excited, the providing unit can provide a visually appealing care plan. This allows for adjusting the method of providing the care plan based on the patient's emotions, thereby providing a more appropriate care plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0105] When providing a care plan, the providing unit can select the optimal providing method by referring to the patient's past feedback. The providing unit can improve the providing method, for example, based on feedback provided by the patient in the past. The providing unit can also adjust the type of information to be provided, for example, based on the patient's past feedback. The providing unit can also adjust the frequency of provision, for example, by referring to the patient's past feedback. In this way, the providing method can be improved by referring to the patient's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0106] When providing a care plan, the providing unit can customize the content to be provided based on the patient's current living situation. For example, if the patient is at home, the providing unit provides a care plan that can be practiced at home. Furthermore, for example, if the patient is out, the providing unit can also provide a care plan that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the providing unit can also provide a care plan that can be practiced at the facility. In this way, by customizing the content to be provided based on the patient's living situation, a more practical care plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0107] The providing unit can improve the provision method by reflecting patient feedback when providing a care plan. For example, if the patient provides feedback on the provided care plan, the providing unit improves the provision method based on that feedback. The providing unit can also adjust the type of information to be provided, for example, based on the patient's feedback. The providing unit can also adjust the frequency of provision, for example, by referring to the patient's feedback. In this way, the provision method can be improved by reflecting the patient's feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The providing unit can estimate the patient's emotions and adjust the order in which care plans are provided based on the estimated patient's emotions. For example, if the patient is relaxed, the providing unit can prioritize providing a care plan that includes detailed explanations. Furthermore, for example, if the patient is feeling anxious, the providing unit can prioritize providing a concise and easy-to-understand care plan. Furthermore, for example, if the patient is excited, the providing unit can prioritize providing a visually appealing care plan. By adjusting the order in which care plans are provided based on the patient's emotions, a more appropriate care plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0109] When providing a care plan, the providing unit can select the optimal providing method by taking into account the patient's geographical location information. For example, if the patient is at home, the providing unit can provide a care plan that can be practiced at home. Furthermore, for example, if the patient is out, the providing unit can also provide a care plan that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the providing unit can also provide a care plan that can be practiced at that facility. In this way, by taking into account the patient's geographical location information, a more appropriate care plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0110] When providing a care plan, the providing unit can analyze the patient's social media activity and suggest content to be provided. The providing unit can provide a relevant care plan based on, for example, information shared by the patient on social media. The providing unit can also analyze, for example, the patient's social media activity patterns and provide a care plan that suits their lifestyle. The providing unit can also provide a social care plan based on, for example, the patient's friendships on social media. In this way, by analyzing the patient's social media activity, a more relevant care plan can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI.

[0111] The providing unit can customize the provision method by reflecting the patient's past feedback when providing a care plan. The providing unit can improve the provision method, for example, based on feedback provided by the patient in the past. The providing unit can also adjust the type of information to be provided, for example, based on the patient's past feedback. The providing unit can also adjust the frequency of provision, for example, by referring to the patient's feedback. In this way, the provision method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0112] The instruction unit can estimate the patient's emotions and adjust the instruction method based on the estimated patient's emotions. For example, if the patient is relaxed, the instruction unit can provide detailed instruction. For example, if the patient is feeling anxious, the instruction unit can provide concise and easy-to-understand instruction. For example, if the patient is feeling excited, the instruction unit can provide visually appealing instruction. This allows for more appropriate instruction by adjusting the instruction method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI.

[0113] When providing instruction, the instruction unit can select the optimal instruction method by referring to the patient's past instruction history. The instruction unit can, for example, improve the current instruction method based on the patient's past instruction history. The instruction unit can also, for example, extract specific patterns from the patient's past instruction history and reflect them in the instruction. The instruction unit can also, for example, adjust the instruction algorithm by referring to the patient's past instruction history. In this way, the instruction method can be improved by referring to the patient's past instruction history. Some or all of the above-mentioned processing in the instruction unit can be performed, for example, using AI, or can be performed without using AI.

[0114] The instruction unit can customize the instruction content based on the patient's current living situation when providing instruction. For example, if the patient is at home, the instruction unit can provide instruction content that can be practiced at home. Furthermore, for example, if the patient is out, the instruction unit can provide instruction content that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the instruction unit can provide instruction content that can be practiced at the facility. In this way, by customizing the instruction content based on the patient's living situation, more practical instruction can be provided. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0115] The instruction unit can improve the instruction method by reflecting patient feedback during instruction. For example, if the patient provides feedback on the instruction provided, the instruction unit improves the instruction method based on that feedback. The instruction unit can also adjust the type of information to be instructed, for example, based on patient feedback. The instruction unit can also adjust the frequency of instruction, for example, based on patient feedback. In this way, the instruction method can be improved by reflecting patient feedback. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0116] The instruction unit can estimate the patient's emotions and determine the priority of instruction based on the estimated patient's emotions. For example, if the patient is relaxed, the instruction unit can prioritize detailed instruction. Furthermore, for example, if the patient is feeling anxious, the instruction unit can prioritize concise and easy-to-understand instruction. Furthermore, for example, if the patient is excited, the instruction unit can prioritize visually appealing instruction. This allows for more appropriate instruction to be provided by determining the priority of instruction based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI.

[0117] When providing instruction, the instruction unit can select the optimal instruction method by taking into account the patient's geographical location information. For example, if the patient is at home, the instruction unit can provide instruction content that can be practiced at home. Furthermore, for example, if the patient is out, the instruction unit can also provide instruction content that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the instruction unit can also provide instruction content that can be practiced at that facility. In this way, more appropriate instruction can be provided by taking into account the patient's geographical location information. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0118] When providing guidance, the guidance unit can analyze the patient's social media activity and suggest guidance content. The guidance unit can provide relevant guidance content based on, for example, information shared by the patient on social media. The guidance unit can also analyze the patient's social media activity patterns and provide guidance content tailored to their lifestyle habits. The guidance unit can also provide social guidance content based on, for example, the patient's social media friendships. In this way, by analyzing the patient's social media activity, more relevant guidance content can be provided. Some or all of the above-described processing in the guidance unit can be performed, for example, using AI, or can be performed without using AI.

[0119] The instruction unit can customize the instruction method by reflecting the patient's past feedback when providing instruction. The instruction unit can improve the instruction method, for example, based on feedback provided by the patient in the past. The instruction unit can also adjust the type of information to provide instruction, for example, based on the patient's past feedback. The instruction unit can also adjust the frequency of instruction, for example, by referring to the patient's feedback. In this way, the instruction method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI.

[0120] The feedback unit can estimate the patient's emotions and adjust the feedback collection method based on the estimated patient's emotions. For example, if the patient is relaxed, the feedback unit can request detailed feedback. For example, if the patient is feeling anxious, the feedback unit can request concise and easy-to-understand feedback. For example, if the patient is excited, the feedback unit can provide a visually appealing feedback form. This allows for more appropriate feedback to be collected by adjusting the feedback collection method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without AI.

[0121] When collecting feedback, the feedback unit can select the optimal collection method by referring to the patient's past feedback history. The feedback unit can, for example, improve the current feedback collection method based on the patient's past feedback history. The feedback unit can also, for example, extract specific patterns from the patient's past feedback history and reflect them in the collection. The feedback unit can also adjust the collection algorithm by referring to the patient's past feedback history. In this way, the collection method can be improved by referring to the patient's past feedback history. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without using AI.

[0122] When collecting feedback, the feedback unit can customize the collected content based on the patient's current living situation. For example, if the patient is at home, the feedback unit can provide feedback that can be practiced at home. Furthermore, for example, if the patient is out, the feedback unit can provide feedback that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the feedback unit can provide feedback that can be practiced at the facility. In this way, by customizing the collected content based on the patient's living situation, more relevant feedback can be collected. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0123] The feedback unit can improve the collection method by reflecting patient feedback when collecting feedback. The feedback unit improves the collection method, for example, based on feedback provided by the patient. The feedback unit can also adjust the type of information to be collected, for example, based on patient feedback. The feedback unit can also adjust the frequency of collection, for example, based on patient feedback. In this way, the collection method can be improved by reflecting patient feedback. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The feedback unit can estimate the patient's emotions and determine the priority of feedback based on the estimated patient's emotions. For example, if the patient is relaxed, the feedback unit can prioritize collecting detailed feedback. For example, if the patient is feeling anxious, the feedback unit can prioritize collecting concise and easy-to-understand feedback. For example, if the patient is excited, the feedback unit can prioritize collecting visually appealing feedback. This allows for more appropriate feedback to be collected by determining the priority of feedback based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or without using AI.

[0125] When collecting feedback, the feedback unit can select the optimal collection method by taking into account the patient's geographical location information. For example, if the patient is at home, the feedback unit can provide feedback that can be practiced at home. Furthermore, for example, if the patient is out, the feedback unit can also provide feedback that can be practiced while away from home. Furthermore, for example, if the patient is in a specific facility, the feedback unit can also provide feedback that can be practiced at the facility. In this way, more appropriate feedback can be collected by taking into account the patient's geographical location information. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI.

[0126] When collecting feedback, the feedback unit can analyze the patient's social media activity and suggest what to collect. The feedback unit can provide relevant feedback based on, for example, information shared by the patient on social media. The feedback unit can also analyze the patient's social media activity patterns and provide feedback that matches their lifestyle. The feedback unit can also provide social feedback based on, for example, the patient's social media friendships. This allows for more relevant feedback to be collected by analyzing the patient's social media activity. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI.

[0127] The feedback unit can customize the collection method by reflecting the patient's past feedback when collecting feedback. The feedback unit can improve the collection method, for example, based on feedback provided by the patient in the past. The feedback unit can also adjust the type of information to be collected, for example, based on the patient's past feedback. The feedback unit can also adjust the frequency of collection, for example, by referring to the patient's feedback. In this way, the collection method can be customized by reflecting the patient's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, instruction unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data on the patient's personal information, medical history, and lifestyle habits using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to analyze the patient's condition in detail. For example, the generation unit generates a care plan based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the care plan generated using the control unit 46A of the smart device 14 to the patient, their family, and medical professionals. The instruction unit can provide instructions on how to implement the care plan using the control unit 46A of the smart device 14. The feedback unit can receive feedback using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, instruction unit, and feedback 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 can collect data on the patient's personal information, medical history, and lifestyle habits using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to analyze the patient's condition in detail. For example, the generation unit generates a care plan based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the care plan generated using the control unit 46A of the smart glasses 214 to the patient, their family, and medical professionals. The instruction unit can provide instructions on how to implement the care plan using the control unit 46A of the smart glasses 214. The feedback unit can receive feedback using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, instruction unit, and feedback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data on the patient's personal information, medical history, and lifestyle habits using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to analyze the patient's condition in detail. For example, the generation unit generates a care plan based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the care plan generated using the control unit 46A of the headset-type terminal 314 to the patient, their family, and medical professionals. The instruction unit can provide instructions on how to implement the care plan using the control unit 46A of the headset-type terminal 314. The feedback unit can receive feedback using the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, instruction unit, and feedback unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data on the patient's personal information, medical history, and lifestyle habits using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and analyzes the patient's condition in detail. For example, the generation unit generates a care plan based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the care plan generated using the control unit 46A of the robot 414 to the patient, their family, and medical professionals. The instruction unit can provide instructions on how to put the care plan into practice using the control unit 46A of the robot 414, for example. The feedback unit can receive feedback using the control unit 46A of the robot 414, for example.

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] The care plan proposal system may further include a social network analysis unit that analyzes the patient's social network. The social network analysis unit analyzes the patient's relationships with family, friends, and caregivers to strengthen social support. For example, if the patient feels isolated, the social network analysis unit may suggest local support groups or volunteer activities. Also, if the patient's family is tired of caring for the patient, the social network analysis unit may suggest caregiver refreshment programs or support services. Furthermore, if the patient wants to make new friends, the social network analysis unit may suggest social events with people who share common hobbies. This may strengthen the patient's social network and reduce feelings of isolation.

[0130] The care plan proposal system may further include a recreation suggestion unit that takes into account the patient's hobbies and interests. The recreation suggestion unit suggests appropriate recreational activities based on the patient's hobbies and interests. For example, if the patient enjoys music, the recreation suggestion unit may suggest music therapy or attending a concert. If the patient is interested in painting, the recreation suggestion unit may suggest art classes or attending an art exhibition. Furthermore, if the patient loves nature, the recreation suggestion unit may suggest gardening or nature walks. This can improve the patient's quality of life and support their mental health.

[0131] The care plan proposal system may further include a relaxation proposal unit that estimates the patient's emotions and proposes relaxation methods based on the estimated emotions. The relaxation proposal unit proposes appropriate relaxation methods according to the patient's emotional state. For example, if the patient is feeling stressed, the relaxation proposal unit may propose relaxation methods such as deep breathing, meditation, or yoga. If the patient is feeling anxious, the relaxation proposal unit may also propose aromatherapy or massage. Furthermore, if the patient is relaxed, the relaxation proposal unit may also propose music therapy or a walk in nature. This makes it possible to provide relaxation methods according to the patient's emotional state and support their mental health.

[0132] The care plan proposal system may further include a communication adjustment unit that estimates the patient's emotions and adjusts the communication method based on the estimated emotions. The communication adjustment unit suggests an appropriate communication method depending on the patient's emotional state. For example, if the patient is relaxed, it can suggest communication that includes detailed explanations. If the patient is feeling anxious, it can suggest simple and easy-to-understand communication. Furthermore, if the patient is excited, it can suggest visually appealing communication. This makes it possible to provide a communication method that suits the patient's emotional state and achieve more effective information transmission.

[0133] The care plan proposal system may further include a priority determination unit that estimates the patient's emotions and determines the priority of the care plans based on the estimated emotions. The priority determination unit adjusts the implementation order of the care plans according to the patient's emotional state. For example, if the patient is feeling stressed, it may prioritize the implementation of a care plan related to stress reduction. Also, if the patient is relaxed, it may prioritize the implementation of a care plan related to improving daily life. Furthermore, if the patient is feeling anxious, it may prioritize the implementation of a care plan related to anxiety reduction. In this way, it is possible to determine the priority of care plans according to the patient's emotional state and provide more effective care.

[0134] The care plan proposal system can further include a nutritional analysis unit that analyzes the patient's dietary data and optimizes nutritional balance. The nutritional analysis unit analyzes the patient's dietary data in detail and proposes a nutritionally balanced meal plan. For example, if the patient is deficient in a specific nutrient, the nutritional analysis unit can propose a meal plan to supplement that nutrient. Also, if the patient has specific dietary restrictions, the nutritional analysis unit can propose a meal plan that addresses those restrictions. Furthermore, if the patient has specific health goals, the nutritional analysis unit can propose a meal plan aimed at those goals. This optimizes the patient's nutritional balance and improves their health.

[0135] The care plan proposal system can further include an exercise analysis unit that analyzes the patient's exercise data and proposes an appropriate exercise program. The exercise analysis unit analyzes the patient's exercise data in detail and proposes an individualized exercise program. For example, if the patient is not getting enough exercise, the exercise analysis unit can propose a program that starts with light exercise. Also, if the patient has a specific exercise goal, the exercise analysis unit can propose an exercise program aimed at that goal. Furthermore, if the patient has a specific health condition, the exercise analysis unit can propose an exercise program that corresponds to that condition. This can improve the patient's exercise habits and improve their health.

[0136] The care plan proposal system may further include a sleep analysis unit that analyzes the patient's sleep data and improves sleep quality. The sleep analysis unit analyzes the patient's sleep data in detail and makes suggestions to improve sleep quality. For example, if the patient is sleep deprived, the sleep analysis unit can make suggestions to increase the amount of sleep. Also, if the patient's sleep quality is poor, the sleep analysis unit can suggest relaxation methods or improvements to the sleep environment. Furthermore, if the patient has a specific sleep disorder, the sleep analysis unit can make suggestions that address that disorder. This can improve the patient's sleep quality and overall health.

[0137] The care plan proposal system may further include a feedback adjustment unit that estimates the patient's emotions and adjusts the feedback collection method based on the estimated emotions. The feedback adjustment unit suggests an appropriate feedback collection method according to the patient's emotional state. For example, if the patient is relaxed, detailed feedback may be requested. If the patient is anxious, brief and easy-to-understand feedback may be requested. Furthermore, if the patient is excited, a visually appealing feedback form may be provided. This makes it possible to provide a feedback collection method according to the patient's emotional state and collect more appropriate feedback.

[0138] The care plan proposal system may further include a provision method adjustment unit that estimates the patient's emotions and adjusts the method of providing the care plan based on the estimated emotions. The provision method adjustment unit proposes an appropriate method of providing the care plan depending on the patient's emotional state. For example, if the patient is relaxed, a care plan including detailed explanations may be provided. If the patient is anxious, a concise and easy-to-understand care plan may be provided. Furthermore, if the patient is excited, a visually appealing care plan may be provided. This makes it possible to provide a method of providing a care plan that suits the patient's emotional state and achieve more effective care.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The collection department collects the patient's personal information, medical history, and lifestyle data. Personal information includes name, age, gender, address, etc. Medical history includes past diagnoses, treatment history, and medical history. Lifestyle includes diet, exercise, sleep, smoking, and alcohol consumption. The collection department collects this detailed information. Step 2: The analysis unit analyzes the data collected by the collection unit and performs a detailed analysis of the patient's condition. The analysis is performed based on the algorithm used, the depth of analysis, and the analysis items. Step 3: The generation unit generates a care plan based on the analysis results obtained by the analysis unit. The generated care plan includes a treatment plan, lifestyle guidance, rehabilitation plan, etc. Step 4: The provider provides the care plan generated by the generator to patients, their families, and healthcare professionals in the form of face-to-face, online, or written form.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[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 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.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

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

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Explanation of symbols]

[0213] 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 that collects personal information, medical history, and lifestyle data of patients; an analysis unit that analyzes the data collected by the collection unit and analyzes the patient's condition in detail; a generation unit that generates a care plan based on the analysis result obtained by the analysis unit; a providing unit that provides the care plan generated by the generating unit to patients, their families, and medical professionals. A system characterized by:

2. Provide a leadership team to provide guidance on how to implement care plans 2. The system of claim 1.

3. Equipped with a feedback unit that receives feedback 2. The system of claim 1.

4. The collecting unit The patient's emotions are estimated using an algorithm, and the timing of data collection is adjusted based on the estimated patient emotions.

2. The system of claim 1.

5. The collecting unit Analyze the patient's past medical history and select the appropriate data collection method 2. The system of claim 1.

6. The collecting unit Filtering data collection based on the patient's current life situation or area of ​​interest 2. The system of claim 1.

7. The collecting unit When collecting data, select the appropriate collection method depending on the patient's input method.

2. The system of claim 1.

8. The collecting unit Estimate patient emotions and prioritize data collection based on the estimated emotions 2. The system of claim 1.

9. The collecting unit When collecting data, consider the patient's geographic location to prioritize the collection of the most relevant data.

2. The system of claim 1.

10. The collecting unit During data collection, analyze patients' social media activity and collect relevant data 2. The system of claim 1.

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