system

Abstract

The system of the embodiment aims to provide quick and personalized answers and treatment suggestions to patients' questions and inquiries. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a suggestion unit. The reception unit inputs patient questions and inquiries. The analysis unit analyzes the information input by the reception unit. The provision unit provides answers and advice based on the information analyzed by the analysis unit. The suggestion unit makes treatment suggestions to individual patients based on the information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide prompt, personalized answers and treatment suggestions to patients' questions and inquiries.

[0005] The system of the embodiment aims to provide quick and personalized answers and treatment suggestions to patients' questions and inquiries. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a suggestion unit. The reception unit inputs questions and inquiries from patients. The analysis unit analyzes the information input by the reception unit. The provision unit provides answers and advice based on the information analyzed by the analysis unit. The suggestion unit makes treatment suggestions to individual patients based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and personalized answers and treatment suggestions to patients' questions and concerns. [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) The ART Navigator system, an embodiment of the present invention, utilizes generative AI to respond to patients' questions and inquiries and propose optimal treatments for each individual patient. In this ART Navigator system, patients input their questions and inquiries, and the generative AI analyzes the input, provides appropriate answers and advice, and then proposes optimal treatments based on the patient's individual circumstances. For example, a patient inputs specific questions such as, "What should I do in the early stages of infertility treatment?" or "What are the side effects of a particular treatment?" This information is input into the generative AI, which then analyzes the input information and provides appropriate answers and advice. The generative AI generates optimal answers to the patient's questions based on past data and specialized knowledge. For example, it provides specific advice such as, "In the early stages of infertility treatment, it is important to first see a doctor" or "A possible side effect of a particular treatment is hormonal imbalance." Furthermore, the generative AI proposes optimal treatments based on the patient's individual circumstances. For example, it proposes optimal treatments based on information such as the patient's age, health condition, and past treatment history. This allows patients to select the treatment that is best suited to them. This tool helps patients resolve their questions and concerns and receive more effective treatments. For example, if a patient is unsure of what to do in the early stages of infertility treatment, they can use this tool to receive specific advice. Also, if they are concerned about the side effects of a particular treatment, they can use this tool to get expert answers. This allows the ART Navigator system to provide appropriate answers and advice to patients' questions and inquiries, and to recommend the optimal treatment for each individual patient.

[0029] The ART Navigator system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a suggestion unit. The reception unit inputs patient questions and inquiries. The patient questions and inquiries include, but are not limited to, specific questions such as, "What should I do in the early stages of infertility treatment?" and "What are the side effects of a specific treatment?" The reception unit can receive patient questions and inquiries using, for example, text input or voice input. The reception unit also has a function to send the patient's input to the generation AI. The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis unit generates optimal answers to the patient's questions based on, for example, past data and specialized knowledge. The generation AI can generate answers to the patient's questions and inquiries using a text generation AI (e.g., LLM). The analysis unit can also use the generation AI to suggest optimal treatments based on the patient's individual circumstances. The provision unit provides appropriate answers and advice based on the information analyzed by the analysis unit. The provision unit also has a function to provide the patient with answers generated by the generation AI. The providing unit can provide answers and advice in text or audio format. The suggesting unit makes optimal treatment suggestions for individual patients based on the information provided by the providing unit. The suggesting unit suggests optimal treatments based on information such as the patient's age, health condition, and past treatment history. The suggesting unit can use generative AI to suggest optimal treatments for patients. As a result, the ART Navigator system according to the embodiment can provide appropriate answers and advice to patients' questions and inquiries, and make optimal treatment suggestions for individual patients.

[0030] The ART Navigator system includes a collection unit that collects information on a patient's age, health condition, and past medical history. The collection unit collects detailed patient information. This detailed information includes, but is not limited to, the patient's age, health condition, past medical history, lifestyle habits, and allergy information. The collection unit can collect information using, for example, the patient's electronic medical record or a medical questionnaire. The collection unit also has a function for transmitting the patient's input information to the analysis unit and proposal unit. This allows the collection unit to collect detailed patient information, enabling more personalized treatment proposals. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the patient's electronic medical record into AI, which then analyzes and collects the information.

[0031] The ART Navigator system includes a management unit that manages the database and expertise used by the generation AI. The management unit appropriately manages the database and expertise used by the generation AI. Examples of appropriate management include, but are not limited to, periodic database updates, data accuracy checks, and expertise reviews. The management unit, for example, has an automatic database update function, which can periodically keep the database up to date. The management unit can also have a third-party evaluation verify the accuracy of the expertise. By appropriately managing the database and expertise, the management unit can improve the analysis accuracy of the generation AI. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can have AI update the database, and the AI ​​can verify the accuracy of the data.

[0032] The ART Navigator system includes a verification unit for ensuring the reliability of the advice and treatment suggestions provided by the generation AI. The verification unit ensures the reliability of the advice and treatment suggestions provided by the generation AI. Examples of ways to ensure reliability include, but are not limited to, evaluation by a third-party organization, evaluation based on past performance, and collecting and incorporating feedback. For example, the verification unit may have a third-party organization evaluate the advice and treatment suggestions provided by the generation AI and confirm their reliability based on the evaluation results. The verification unit may also collect feedback from patients and improve the reliability of the advice and treatment suggestions based on that feedback. In this way, the verification unit can gain the trust of patients by ensuring the reliability of the advice and treatment suggestions. Some or all of the above-described processing in the verification unit may be performed using AI, or may be performed without AI. For example, the verification unit may input feedback from patients into AI, which may analyze the feedback and evaluate reliability.

[0033] The collection unit can cooperate with the proposal unit to collect patient information. The collection unit cooperates with the proposal unit to collect patient information. For example, the collection unit collects information such as the patient's age, health condition, and past medical history based on a request from the proposal unit. The collection unit can also provide information required by the proposal unit in real time. For example, the collection unit immediately collects information required by the proposal unit when making a treatment proposal and provides it to the proposal unit. This enables the collection unit to collect more accurate information by working with the proposal unit. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a request from the proposal unit to AI, which can collect information and provide it to the proposal unit.

[0034] The management unit can manage the database and specialized knowledge in cooperation with the analysis unit. The management unit manages the database and specialized knowledge in cooperation with the analysis unit. For example, the management unit updates the database and checks specialized knowledge based on a request from the analysis unit. The management unit can also provide data required by the analysis unit in real time. For example, the management unit immediately provides data required by the analysis unit when performing analysis. In this way, the management unit cooperates with the analysis unit to improve the efficiency of management of the database and specialized knowledge. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input a request from the analysis unit into AI, which then updates the database and checks specialized knowledge.

[0035] The verification unit can cooperate with the provision unit to ensure the reliability of the advice and treatment suggestions. The verification unit cooperates with the provision unit to ensure the reliability of the advice and treatment suggestions. For example, the verification unit evaluates the reliability of the advice and treatment suggestions based on a request from the provision unit. The verification unit can also check the reliability of the advice and treatment suggestions provided by the provision unit in real time. For example, the verification unit immediately evaluates the reliability of the advice and treatment suggestions when the provision unit makes them. As a result, the verification unit cooperates with the provision unit to improve the reliability of the advice and treatment suggestions. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input a request from the provision unit into AI, which can evaluate the reliability.

[0036] In the ART Navigator system, the reception unit can analyze a patient's past consultation history and select the optimal reception method. The reception unit analyzes a patient's past consultation history and selects the optimal reception method. For example, the reception unit automatically displays the patient's past consultation topics as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the patient has used in the past. Furthermore, the reception unit can predict and suggest the consultation topics to be used during a specific time period based on the patient's past consultation history. This allows the reception unit to provide the optimal reception method based on the past consultation history, thereby improving convenience for patients. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the patient's past consultation history into AI, which then selects the optimal reception method.

[0037] In the ART Navigator system, the reception unit can filter consultations based on the patient's current living situation and areas of interest at the time of reception. The reception unit filters consultations based on the patient's current living situation and areas of interest at the time of reception. For example, the reception unit prioritizes displaying relevant consultation content based on the patient's current living situation (work, family, etc.). The reception unit can also filter relevant consultation content based on the patient's areas of interest (specific treatments, health management, etc.). Furthermore, the reception unit can suggest the optimal consultation time based on the patient's lifestyle (night owl, morning person, etc.). In this way, the reception unit can provide more appropriate consultation content by filtering according to the patient's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the patient's living situation and areas of interest into AI, which then performs filtering.

[0038] The ART Navigator system allows the reception unit to prioritize relevant questions and inquiries by taking into account the patient's geographic location information. For example, if a patient lives in a specific area, the reception unit can prioritize inquiries related to that area. Also, if a patient is traveling, the reception unit can prioritize inquiries related to the patient's travel destination. Furthermore, if a patient is planning to move, the reception unit can prioritize inquiries related to the patient's new residence. This allows the reception unit to prioritize based on the patient's geographic location information, enabling more appropriate responses. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the patient's geographic location information into AI, which can then determine the priorities.

[0039] In the ART Navigator system, the reception unit analyzes the patient's social media activity and accepts related questions and inquiries. The reception unit analyzes the patient's social media activity and accepts related questions and inquiries. For example, the reception unit prioritizes inquiries related to topics frequently mentioned by the patient on social media. The reception unit can also accept related inquiries based on the opinions of experts the patient follows on social media. Furthermore, the reception unit can analyze the patient's current interests from the patient's social media activity and accept related inquiries. This allows the reception unit to accept inquiries based on the patient's social media activity, enabling more appropriate responses. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the patient's social media activity into AI, which then accepts related questions and inquiries.

[0040] The ART Navigator system allows the analysis unit to adjust the level of detail of the analysis based on the importance of the question or inquiry during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the question or inquiry during analysis. For example, the analysis unit provides detailed analysis results for questions or inquiries of high importance. The analysis unit can also provide concise analysis results for questions or inquiries of low importance. Furthermore, the analysis unit can provide analysis results with appropriate detail for questions or inquiries of medium importance. This allows the analysis unit to provide more appropriate analysis results by performing an analysis according to the importance of the question or inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the question or inquiry into the generation AI, which can adjust the level of detail.

[0041] In the ART Navigator system, the analysis unit can apply different analysis algorithms depending on the category of the question or inquiry during analysis. The analysis unit applies different analysis algorithms depending on the category of the question or inquiry during analysis. For example, the analysis unit can apply an analysis algorithm based on specialized medical data to questions or inquiries about medical care. The analysis unit can also apply an analysis algorithm based on lifestyle data to questions or inquiries about lifestyle habits. Furthermore, the analysis unit can apply an analysis algorithm based on psychological data to questions or inquiries about mental health. This allows the analysis unit to provide more accurate analysis results by applying an analysis algorithm according to the category. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of the question or inquiry into the generation AI, which then applies the analysis algorithm.

[0042] In the ART Navigator system, the analysis unit can determine the analysis priority based on the time of submission of the question or inquiry during analysis. The analysis unit determines the analysis priority based on the time of submission of the question or inquiry during analysis. For example, the analysis unit prioritizes analysis of recently submitted questions or inquiries. The analysis unit can also postpone analysis of questions or inquiries that were submitted recently. Furthermore, the analysis unit can analyze questions or inquiries that were submitted a medium time ago with a moderate priority. In this way, the analysis unit can set priorities based on the time of submission, enabling faster and more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the time of submission of the question or inquiry into the generation AI, and the generation AI can determine the priority.

[0043] The ART Navigator system allows the analysis unit to adjust the order of analysis based on the relevance of questions and inquiries during analysis. The analysis unit adjusts the order of analysis based on the relevance of questions and inquiries during analysis. For example, the analysis unit prioritizes analysis of highly relevant questions and inquiries. The analysis unit can also postpone analysis of less relevant questions and inquiries. Furthermore, the analysis unit can analyze questions and inquiries with moderate relevance in an appropriate order. This allows the analysis unit to perform a more appropriate analysis by setting an order based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of questions and inquiries into the generation AI, which can then adjust the order of analysis.

[0044] In the ART Navigator system, the providing unit can adjust the level of detail of the provided answer or advice based on the importance of the answer or advice when providing the answer or advice. The providing unit adjusts the level of detail of the provided answer or advice based on the importance of the answer or advice when providing the answer or advice. For example, the providing unit provides detailed information for answers or advice with high importance. The providing unit can also provide concise information for answers or advice with low importance. Furthermore, the providing unit can provide information with an appropriate level of detail for answers or advice with medium importance. In this way, the providing unit can provide more appropriate information by providing detail according to the importance. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the importance of the answer or advice to the generation AI, and the generation AI can adjust the level of detail.

[0045] In the ART Navigator system, the providing unit can apply different provision algorithms depending on the category of the answer or advice when providing the answer or advice. The providing unit applies different provision algorithms depending on the category of the answer or advice when providing the answer or advice. For example, the providing unit applies a provision algorithm based on specialized medical data to answers or advice related to medical care. The providing unit can also apply a provision algorithm based on lifestyle data to answers or advice related to lifestyle habits. Furthermore, the providing unit can apply a provision algorithm based on psychological data to answers or advice related to mental health. In this way, the providing unit can provide more accurate information by applying a provision algorithm according to the category. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the category of the answer or advice into the generation AI, and the generation AI can apply the provision algorithm.

[0046] In the ART Navigator system, the providing unit can determine the priority of providing answers and advice based on the time of submission of the answers and advice. The providing unit determines the priority of providing answers and advice based on the time of submission of the answers and advice. For example, the providing unit can prioritize providing answers and advice to recently submitted questions and advice. The providing unit can also postpone providing answers and advice to questions and advice submitted recently. Furthermore, the providing unit can provide answers and advice to questions and advice submitted recently with a moderate priority. This allows the providing unit to set priorities based on the time of submission, enabling faster and more appropriate responses. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the time of submission of answers and advice into the generation AI, and the generation AI can determine the priority.

[0047] In the ART Navigator system, the providing unit can adjust the order of providing answers and advice based on the relevance of the answers and advice when providing them. The providing unit adjusts the order of providing answers and advice based on the relevance of the answers and advice when providing them. For example, the providing unit prioritizes providing answers and advice to questions and inquiries with high relevance. The providing unit can also postpone providing answers and advice to questions and inquiries with low relevance. Furthermore, the providing unit can provide answers and advice to questions and inquiries with medium relevance in an appropriate order. In this way, the providing unit can set an order based on relevance, enabling more appropriate responses. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the relevance of answers and advice into the generation AI, and the generation AI can adjust the order of providing.

[0048] In the ART Navigator system, when the proposal unit makes a proposal, it can analyze the patient's past treatment history and select the optimal treatment proposal method. When making a proposal, the proposal unit analyzes the patient's past treatment history and selects the optimal treatment proposal method. For example, the proposal unit proposes the optimal treatment based on the patient's past treatment history. The proposal unit can also select an effective treatment from the patient's past treatment history. Furthermore, the proposal unit can analyze the patient's past treatment history and propose a treatment with fewer side effects. As a result, the proposal unit provides an optimal treatment proposal based on the past treatment history, thereby improving the treatment effect. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the patient's past treatment history into the generation AI, which can select the optimal treatment proposal method.

[0049] The ART Navigator system allows the suggestion unit to customize the treatment suggestion means based on the patient's current health condition when making a suggestion. The suggestion unit customizes the treatment suggestion means based on the patient's current health condition when making a suggestion. For example, the suggestion unit suggests an optimal treatment based on the patient's current health condition. The suggestion unit can also select an effective treatment means based on the patient's current health condition. Furthermore, the suggestion unit can analyze the patient's current health condition and suggest a treatment means with fewer side effects. As a result, the suggestion unit provides a treatment suggestion based on the patient's current health condition, thereby improving the treatment effect. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the patient's current health condition into the generation AI, which can then customize the treatment suggestion means.

[0050] In the ART Navigator system, the suggestion unit can select the optimal treatment proposal method by taking into account the patient's geographic location information when making a proposal. The suggestion unit selects the optimal treatment proposal method by taking into account the patient's geographic location information when making a proposal. For example, if the patient lives in a specific area, the suggestion unit can prioritize suggesting treatments related to that area. Also, if the patient is traveling, the suggestion unit can prioritize suggesting treatments related to the patient's travel destination. Furthermore, if the patient is planning to move, the suggestion unit can prioritize suggesting treatments related to the patient's new residence. This allows the suggestion unit to provide treatment proposals based on geographic location information, enabling more appropriate treatment. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generating AI. For example, the suggestion unit can input the patient's geographic location information into the generating AI, which can select the optimal treatment proposal method.

[0051] In the ART Navigator system, when the suggestion unit makes a suggestion, it can analyze the patient's social media activity to suggest a treatment suggestion method. When making a suggestion, the suggestion unit analyzes the patient's social media activity to suggest a treatment suggestion method. For example, the suggestion unit may prioritize suggesting treatments related to topics frequently mentioned by the patient on social media. The suggestion unit may also suggest related treatments based on the opinions of experts the patient follows on social media. Furthermore, the suggestion unit may analyze the patient's current interests from the patient's social media activity and suggest related treatments. This allows the suggestion unit to provide more appropriate treatment by providing treatment suggestions based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the patient's social media activity into the generation AI, which then suggests a treatment suggestion method.

[0052] In the ART Navigator system, the collection unit can select the optimal information collection method by referring to the patient's past treatment history when collecting information. The collection unit selects the optimal information collection method by referring to the patient's past treatment history when collecting information. For example, the collection unit selects the optimal information collection method based on the patient's past treatment history. The collection unit can also select an effective information collection method from the patient's past treatment history. Furthermore, the collection unit can analyze the patient's past treatment history and select an information collection method with fewer side effects. This allows the collection unit to collect more appropriate information by collecting information based on the past treatment history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's past treatment history into AI, which can select the optimal information collection method.

[0053] In the ART Navigator system, the collection unit can select the optimal information collection method by taking into account the patient's geographic location information when collecting information. The collection unit selects the optimal information collection method by taking into account the patient's geographic location information when collecting information. For example, if the patient lives in a specific area, the collection unit can prioritize collecting information related to that area. Also, if the patient is traveling, the collection unit can prioritize collecting information related to the patient's travel destination. Furthermore, if the patient is planning to move, the collection unit can prioritize collecting information related to the patient's new residence. This allows the collection unit to collect more appropriate information by collecting information based on geographic location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's geographic location information into AI, which can select the optimal information collection method.

[0054] In the ART Navigator system, the management unit can select the optimal management method by referring to past data when managing a database. The management unit selects the optimal management method by referring to past data when managing a database. For example, the management unit selects the optimal database management method based on past data. The management unit can also select an effective database management method from past data. Furthermore, the management unit can analyze past data and select a database management method with fewer side effects. In this way, the management unit provides a management method based on past data, thereby improving the efficiency of database and expert knowledge management. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input past data into AI, which then selects the optimal management method.

[0055] In the ART Navigator system, the management unit can weight data based on the time of data submission when managing the database. The management unit weights data based on the time of data submission when managing the database. For example, the management unit prioritizes weighting of recently submitted data. The management unit can also weight data that was submitted earlier later. Furthermore, the management unit can appropriately weight data that was submitted at a medium time. In this way, the management unit can weight data based on the time of submission, thereby making data management more efficient. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the time of data submission into AI, and the AI ​​can perform weighting.

[0056] In the ART Navigator system, the verification unit can select the optimal verification method by referring to the history of past advice and treatment proposals during verification. The verification unit selects the optimal verification method by referring to the history of past advice and treatment proposals during verification. For example, the verification unit selects the optimal reliability verification method based on the history of past advice and treatment proposals. The verification unit can also select an effective reliability verification method from the history of past advice and treatment proposals. Furthermore, the verification unit can analyze the history of past advice and treatment proposals and select a reliability verification method with fewer side effects. In this way, the verification unit provides a verification method based on the past history, thereby improving the reliability of the advice and treatment proposals. Some or all of the above-mentioned processing in the verification unit may be performed, for example, using AI, or may be performed without using AI. For example, the verification unit can input the history of past advice and treatment proposals into AI, which can select the optimal verification method.

[0057] In the ART Navigator system, the verification unit can weight the verification data based on the time of submission of the advice or treatment proposal during verification. The verification unit weights the verification data based on the time of submission of the advice or treatment proposal during verification. For example, the verification unit prioritizes weighting of recently submitted advice or treatment proposals. The verification unit can also weight advice or treatment proposals that were submitted earlier later. Furthermore, the verification unit can appropriately weight advice or treatment proposals that were submitted at a moderate time. In this way, the verification unit can improve the reliability of the advice or treatment proposal by weighting based on the time of submission. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the time of submission of the advice or treatment proposal into AI, and the AI ​​can perform weighting.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The ART Navigator system collects data on a patient's lifestyle and daily activities, which can be used for analysis by the analysis unit. For example, the collection unit collects data such as the patient's exercise volume, dietary habits, and sleep patterns. The collection unit can also obtain data from devices such as the patient's smartwatch or fitness tracker. Furthermore, the collection unit can collect data based on the patient's diary or self-report. This allows the collection unit to provide detailed data based on the patient's lifestyle, improving the accuracy of the analysis by the analysis unit.

[0060] The ART Navigator system can collect a patient's family history and genetic information and use this information for analysis by the analysis unit. For example, the collection unit collects information about the patient's family history and evaluates genetic risk. The collection unit can also obtain the patient's genetic test results and use them for analysis by the analysis unit. Furthermore, the collection unit can collect information about the patient's family structure and living environment and reflect this information in the analysis by the analysis unit. This allows the collection unit to provide detailed data based on the patient's genetic information, improving the accuracy of the analysis by the analysis unit.

[0061] The ART Navigator system can make treatment suggestions taking into account the patient's living environment and social background. For example, the suggestion unit can suggest feasible treatments based on the patient's living environment and work environment. The suggestion unit can also suggest treatments that will provide support, taking into account the patient's social support network (family, friends, etc.). Furthermore, the suggestion unit can also suggest cost-effective treatments, taking into account the patient's financial situation. As a result, the suggestion unit can make treatment suggestions that are tailored to the patient's living environment and social background, thereby improving the feasibility of treatment.

[0062] The ART Navigator system tracks a patient's treatment history and response, and can use this information for analysis by the analysis unit. For example, the collection unit collects a patient's past treatment history and evaluates the effectiveness of treatment. The collection unit can also track a patient's response to treatment (side effects, improvement, etc.) and reflect this in the analysis by the analysis unit. Furthermore, the collection unit can collect feedback on the patient's treatment and use it for analysis by the analysis unit. This allows the collection unit to provide detailed data based on the patient's treatment history and response, improving the accuracy of the analysis by the analysis unit.

[0063] The ART Navigator system can collect patients' expectations and goals regarding treatment and use them for analysis by the analysis unit. For example, the collection unit collects patients' expectations and goals regarding treatment and evaluates the direction of treatment. The collection unit can also collect patients' hopes and concerns regarding treatment and reflect them in the analysis by the analysis unit. Furthermore, the collection unit can collect feedback on patients' treatment and use it in the analysis by the analysis unit. This allows the collection unit to provide detailed data based on patients' expectations and goals, improving the accuracy of the analysis by the analysis unit.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit inputs the patient's questions and inquiries. Patient questions and inquiries include, but are not limited to, specific questions such as, "What should I do in the early stages of infertility treatment?" and "What are the side effects of a particular treatment?" The reception unit can accept the patient's questions and inquiries using text input or voice input. The reception unit also has a function to send the patient's input to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis unit generates the optimal answer to the patient's question based on past data and specialized knowledge. The generation AI can use text generation AI (e.g., LLM) to generate answers to the patient's questions and inquiries. The analysis unit can also use the generation AI to make optimal treatment suggestions based on the patient's individual situation. Step 3: The provider provides appropriate answers and advice based on the information analyzed by the analyzer. The provider has the function of providing the answers generated by the generation AI to the patient, and can provide answers and advice in text or audio format. Step 4: The proposal unit proposes the optimal treatment for each individual patient based on the information provided by the provision unit. The proposal unit proposes the optimal treatment based on information such as the patient's age, health condition, and past treatment history. The proposal unit can use generative AI to propose the optimal treatment for each patient.

[0066] (Example 2) The ART Navigator system, an embodiment of the present invention, utilizes generative AI to respond to patients' questions and inquiries and propose optimal treatments for each individual patient. In this ART Navigator system, patients input their questions and inquiries, and the generative AI analyzes the input, provides appropriate answers and advice, and then proposes optimal treatments based on the patient's individual circumstances. For example, a patient inputs specific questions such as, "What should I do in the early stages of infertility treatment?" or "What are the side effects of a particular treatment?" This information is input into the generative AI, which then analyzes the input information and provides appropriate answers and advice. The generative AI generates optimal answers to the patient's questions based on past data and specialized knowledge. For example, it provides specific advice such as, "In the early stages of infertility treatment, it is important to first see a doctor" or "A possible side effect of a particular treatment is hormonal imbalance." Furthermore, the generative AI proposes optimal treatments based on the patient's individual circumstances. For example, it proposes optimal treatments based on information such as the patient's age, health condition, and past treatment history. This allows patients to select the treatment that is best suited to them. This tool helps patients resolve their questions and concerns and receive more effective treatments. For example, if a patient is unsure of what to do in the early stages of infertility treatment, they can use this tool to receive specific advice. Also, if they are concerned about the side effects of a particular treatment, they can use this tool to get expert answers. This allows the ART Navigator system to provide appropriate answers and advice to patients' questions and inquiries, and to recommend the optimal treatment for each individual patient.

[0067] The ART Navigator system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a suggestion unit. The reception unit inputs patient questions and inquiries. The patient questions and inquiries include, but are not limited to, specific questions such as, "What should I do in the early stages of infertility treatment?" and "What are the side effects of a specific treatment?" The reception unit can receive patient questions and inquiries using, for example, text input or voice input. The reception unit also has a function to send the patient's input to the generation AI. The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis unit generates optimal answers to the patient's questions based on, for example, past data and specialized knowledge. The generation AI can generate answers to the patient's questions and inquiries using a text generation AI (e.g., LLM). The analysis unit can also use the generation AI to suggest optimal treatments based on the patient's individual circumstances. The provision unit provides appropriate answers and advice based on the information analyzed by the analysis unit. The provision unit also has a function to provide the patient with answers generated by the generation AI. The providing unit can provide answers and advice in text or audio format. The suggesting unit makes optimal treatment suggestions for individual patients based on the information provided by the providing unit. The suggesting unit suggests optimal treatments based on information such as the patient's age, health condition, and past treatment history. The suggesting unit can use generative AI to suggest optimal treatments for patients. As a result, the ART Navigator system according to the embodiment can provide appropriate answers and advice to patients' questions and inquiries, and make optimal treatment suggestions for individual patients.

[0068] The ART Navigator system includes a collection unit that collects information on a patient's age, health condition, and past medical history. The collection unit collects detailed patient information. This detailed information includes, but is not limited to, the patient's age, health condition, past medical history, lifestyle habits, and allergy information. The collection unit can collect information using, for example, the patient's electronic medical record or a medical questionnaire. The collection unit also has a function for transmitting the patient's input information to the analysis unit and proposal unit. This allows the collection unit to collect detailed patient information, enabling more personalized treatment proposals. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the patient's electronic medical record into AI, which then analyzes and collects the information.

[0069] The ART Navigator system includes a management unit that manages the database and expertise used by the generation AI. The management unit appropriately manages the database and expertise used by the generation AI. Examples of appropriate management include, but are not limited to, periodic database updates, data accuracy checks, and expertise reviews. The management unit, for example, has an automatic database update function, which can periodically keep the database up to date. The management unit can also have a third-party evaluation verify the accuracy of the expertise. By appropriately managing the database and expertise, the management unit can improve the analysis accuracy of the generation AI. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can have AI update the database, and the AI ​​can verify the accuracy of the data.

[0070] The ART Navigator system includes a verification unit for ensuring the reliability of the advice and treatment suggestions provided by the generation AI. The verification unit ensures the reliability of the advice and treatment suggestions provided by the generation AI. Examples of ways to ensure reliability include, but are not limited to, evaluation by a third-party organization, evaluation based on past performance, and collecting and incorporating feedback. For example, the verification unit may have a third-party organization evaluate the advice and treatment suggestions provided by the generation AI and confirm their reliability based on the evaluation results. The verification unit may also collect feedback from patients and improve the reliability of the advice and treatment suggestions based on that feedback. In this way, the verification unit can gain the trust of patients by ensuring the reliability of the advice and treatment suggestions. Some or all of the above-described processing in the verification unit may be performed using AI, or may be performed without AI. For example, the verification unit may input feedback from patients into AI, which may analyze the feedback and evaluate reliability.

[0071] The collection unit can cooperate with the proposal unit to collect patient information. The collection unit cooperates with the proposal unit to collect patient information. For example, the collection unit collects information such as the patient's age, health condition, and past medical history based on a request from the proposal unit. The collection unit can also provide information required by the proposal unit in real time. For example, the collection unit immediately collects information required by the proposal unit when making a treatment proposal and provides it to the proposal unit. This enables the collection unit to collect more accurate information by working with the proposal unit. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a request from the proposal unit to AI, which can collect information and provide it to the proposal unit.

[0072] The management unit can manage the database and specialized knowledge in cooperation with the analysis unit. The management unit manages the database and specialized knowledge in cooperation with the analysis unit. For example, the management unit updates the database and checks specialized knowledge based on a request from the analysis unit. The management unit can also provide data required by the analysis unit in real time. For example, the management unit immediately provides data required by the analysis unit when performing analysis. In this way, the management unit cooperates with the analysis unit to improve the efficiency of management of the database and specialized knowledge. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input a request from the analysis unit into AI, which then updates the database and checks specialized knowledge.

[0073] The verification unit can cooperate with the provision unit to ensure the reliability of the advice and treatment suggestions. The verification unit cooperates with the provision unit to ensure the reliability of the advice and treatment suggestions. For example, the verification unit evaluates the reliability of the advice and treatment suggestions based on a request from the provision unit. The verification unit can also check the reliability of the advice and treatment suggestions provided by the provision unit in real time. For example, the verification unit immediately evaluates the reliability of the advice and treatment suggestions when the provision unit makes them. As a result, the verification unit cooperates with the provision unit to improve the reliability of the advice and treatment suggestions. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input a request from the provision unit into AI, which can evaluate the reliability.

[0074] In the ART Navigator system, the reception unit can estimate a patient's emotions and adjust the reception method for questions and inquiries based on the estimated patient emotions. The reception unit estimates a patient's emotions and adjusts the reception method for questions and inquiries based on the estimated patient emotions. For example, if the reception unit is feeling anxious, it provides a gentle-toned interface and simplifies the input procedure. Alternatively, if the patient is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the patient is in a hurry, the reception unit prioritizes voice input, allowing the patient to quickly input their questions and inquiries. This allows the reception unit to provide a reception method that suits the patient's emotions, thereby improving patient satisfaction. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception department can input the patient's input information into AI, which can then infer the patient's emotions and adjust the reception method accordingly.

[0075] In the ART Navigator system, the reception unit can analyze a patient's past consultation history and select the optimal reception method. The reception unit analyzes a patient's past consultation history and selects the optimal reception method. For example, the reception unit automatically displays the patient's past consultation topics as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the patient has used in the past. Furthermore, the reception unit can predict and suggest the consultation topics to be used during a specific time period based on the patient's past consultation history. This allows the reception unit to provide the optimal reception method based on the past consultation history, thereby improving convenience for patients. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the patient's past consultation history into AI, which then selects the optimal reception method.

[0076] In the ART Navigator system, the reception unit can filter consultations based on the patient's current living situation and areas of interest at the time of reception. The reception unit filters consultations based on the patient's current living situation and areas of interest at the time of reception. For example, the reception unit prioritizes displaying relevant consultation content based on the patient's current living situation (work, family, etc.). The reception unit can also filter relevant consultation content based on the patient's areas of interest (specific treatments, health management, etc.). Furthermore, the reception unit can suggest the optimal consultation time based on the patient's lifestyle (night owl, morning person, etc.). In this way, the reception unit can provide more appropriate consultation content by filtering according to the patient's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the patient's living situation and areas of interest into AI, which then performs filtering.

[0077] In the ART Navigator system, the reception unit estimates a patient's emotions and prioritizes the questions and inquiries to be received based on the estimated patient emotions. The reception unit estimates a patient's emotions and prioritizes the questions and inquiries to be received based on the estimated patient emotions. For example, if a patient is feeling very anxious, the reception unit may prioritize the patient's inquiries. Alternatively, if a patient is relaxed, the reception unit may prioritize inquiries requiring a quick response. Furthermore, if a patient is in a hurry, the reception unit may prioritize inquiries requiring a quick response. This allows the reception unit to prioritize the patient's emotions, enabling faster and more appropriate responses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may 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 reception unit may be performed using, for example, an AI, or without an AI. For example, the reception unit may input the patient's emotions into an AI, which may then determine the priority.

[0078] The ART Navigator system allows the reception unit to prioritize relevant questions and inquiries by taking into account the patient's geographic location information. For example, if a patient lives in a specific area, the reception unit can prioritize inquiries related to that area. Also, if a patient is traveling, the reception unit can prioritize inquiries related to the patient's travel destination. Furthermore, if a patient is planning to move, the reception unit can prioritize inquiries related to the patient's new residence. This allows the reception unit to prioritize based on the patient's geographic location information, enabling more appropriate responses. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the patient's geographic location information into AI, which can then determine the priorities.

[0079] In the ART Navigator system, the reception unit analyzes the patient's social media activity and accepts related questions and inquiries. The reception unit analyzes the patient's social media activity and accepts related questions and inquiries. For example, the reception unit prioritizes inquiries related to topics frequently mentioned by the patient on social media. The reception unit can also accept related inquiries based on the opinions of experts the patient follows on social media. Furthermore, the reception unit can analyze the patient's current interests from the patient's social media activity and accept related inquiries. This allows the reception unit to accept inquiries based on the patient's social media activity, enabling more appropriate responses. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the patient's social media activity into AI, which then accepts related questions and inquiries.

[0080] In the ART Navigator system, the analysis unit estimates the patient's emotions and adjusts the presentation of the analysis based on the estimated patient emotions. The analysis unit estimates the patient's emotions and adjusts the presentation of the analysis based on the estimated patient emotions. For example, if the patient is feeling anxious, the analysis unit provides analysis results in a gentle tone. Furthermore, if the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows the analysis unit to provide analysis results tailored to the patient's emotions, thereby deepening understanding of the patient. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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. For example, the analysis unit can input the patient's emotions into AI, which then adjusts the presentation.

[0081] The ART Navigator system allows the analysis unit to adjust the level of detail of the analysis based on the importance of the question or inquiry during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the question or inquiry during analysis. For example, the analysis unit provides detailed analysis results for questions or inquiries of high importance. The analysis unit can also provide concise analysis results for questions or inquiries of low importance. Furthermore, the analysis unit can provide analysis results with appropriate detail for questions or inquiries of medium importance. This allows the analysis unit to provide more appropriate analysis results by performing an analysis according to the importance of the question or inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the question or inquiry into the generation AI, which can adjust the level of detail.

[0082] In the ART Navigator system, the analysis unit can apply different analysis algorithms depending on the category of the question or inquiry during analysis. The analysis unit applies different analysis algorithms depending on the category of the question or inquiry during analysis. For example, the analysis unit can apply an analysis algorithm based on specialized medical data to questions or inquiries about medical care. The analysis unit can also apply an analysis algorithm based on lifestyle data to questions or inquiries about lifestyle habits. Furthermore, the analysis unit can apply an analysis algorithm based on psychological data to questions or inquiries about mental health. This allows the analysis unit to provide more accurate analysis results by applying an analysis algorithm according to the category. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of the question or inquiry into the generation AI, which then applies the analysis algorithm.

[0083] In the ART Navigator system, the analysis unit estimates the patient's emotions and adjusts the length of the analysis based on the estimated patient emotions. The analysis unit estimates the patient's emotions and adjusts the length of the analysis based on the estimated patient emotions. For example, if the patient is feeling anxious, the analysis unit provides a short and concise analysis result. Also, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is in a hurry, the analysis unit can provide a concise analysis result. This allows the analysis unit to provide a length of analysis result that corresponds to the patient's emotions, thereby deepening understanding. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 analysis unit may be performed using, for example, the generation AI, or may be performed without the generation AI. For example, the analysis unit inputs the patient's emotions into the generation AI, which then adjusts the length of the analysis.

[0084] In the ART Navigator system, the analysis unit can determine the analysis priority based on the time of submission of the question or inquiry during analysis. The analysis unit determines the analysis priority based on the time of submission of the question or inquiry during analysis. For example, the analysis unit prioritizes analysis of recently submitted questions or inquiries. The analysis unit can also postpone analysis of questions or inquiries that were submitted recently. Furthermore, the analysis unit can analyze questions or inquiries that were submitted a medium time ago with a moderate priority. In this way, the analysis unit can set priorities based on the time of submission, enabling faster and more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the time of submission of the question or inquiry into the generation AI, and the generation AI can determine the priority.

[0085] The ART Navigator system allows the analysis unit to adjust the order of analysis based on the relevance of questions and inquiries during analysis. The analysis unit adjusts the order of analysis based on the relevance of questions and inquiries during analysis. For example, the analysis unit prioritizes analysis of highly relevant questions and inquiries. The analysis unit can also postpone analysis of less relevant questions and inquiries. Furthermore, the analysis unit can analyze questions and inquiries with moderate relevance in an appropriate order. This allows the analysis unit to perform a more appropriate analysis by setting an order based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of questions and inquiries into the generation AI, which can then adjust the order of analysis.

[0086] In the ART Navigator system, the providing unit estimates the patient's emotions and adjusts the expression of the answers and advice provided based on the estimated patient emotions. The providing unit estimates the patient's emotions and adjusts the expression of the answers and advice provided based on the estimated patient emotions. For example, if the patient is feeling anxious, the providing unit may provide answers and advice in a gentle tone. Furthermore, if the patient is relaxed, the providing unit may provide detailed answers and advice. Furthermore, if the patient is in a hurry, the providing unit may provide concise answers and advice that focus on the main points. This allows the providing unit to provide an expression method that matches the patient's emotions, thereby deepening understanding of the patient. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input the patient's emotions into the generation AI, which may adjust the expression method.

[0087] In the ART Navigator system, the providing unit can adjust the level of detail of the provided answer or advice based on the importance of the answer or advice when providing the answer or advice. The providing unit adjusts the level of detail of the provided answer or advice based on the importance of the answer or advice when providing the answer or advice. For example, the providing unit provides detailed information for answers or advice with high importance. The providing unit can also provide concise information for answers or advice with low importance. Furthermore, the providing unit can provide information with an appropriate level of detail for answers or advice with medium importance. In this way, the providing unit can provide more appropriate information by providing detail according to the importance. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the importance of the answer or advice to the generation AI, and the generation AI can adjust the level of detail.

[0088] In the ART Navigator system, the providing unit can apply different provision algorithms depending on the category of the answer or advice when providing the answer or advice. The providing unit applies different provision algorithms depending on the category of the answer or advice when providing the answer or advice. For example, the providing unit applies a provision algorithm based on specialized medical data to answers or advice related to medical care. The providing unit can also apply a provision algorithm based on lifestyle data to answers or advice related to lifestyle habits. Furthermore, the providing unit can apply a provision algorithm based on psychological data to answers or advice related to mental health. In this way, the providing unit can provide more accurate information by applying a provision algorithm according to the category. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the category of the answer or advice into the generation AI, and the generation AI can apply the provision algorithm.

[0089] In the ART Navigator system, the providing unit estimates the patient's emotions and adjusts the length of the answers and advice provided based on the estimated patient emotions. The providing unit estimates the patient's emotions and adjusts the length of the answers and advice provided based on the estimated patient emotions. For example, if the patient is feeling anxious, the providing unit provides short, concise answers and advice. If the patient is relaxed, the providing unit can also provide detailed answers and advice. If the patient is in a hurry, the providing unit can also provide concise answers and advice. This allows the providing unit to provide answers and advice of a length appropriate to the patient's emotions, thereby deepening understanding of the patient. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the patient's emotions into the generation AI, which then adjusts the length of the answers and advice.

[0090] In the ART Navigator system, the providing unit can determine the priority of providing answers and advice based on the time of submission of the answers and advice. The providing unit determines the priority of providing answers and advice based on the time of submission of the answers and advice. For example, the providing unit can prioritize providing answers and advice to recently submitted questions and advice. The providing unit can also postpone providing answers and advice to questions and advice submitted recently. Furthermore, the providing unit can provide answers and advice to questions and advice submitted recently with a moderate priority. This allows the providing unit to set priorities based on the time of submission, enabling faster and more appropriate responses. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the time of submission of answers and advice into the generation AI, and the generation AI can determine the priority.

[0091] In the ART Navigator system, the providing unit can adjust the order of providing answers and advice based on the relevance of the answers and advice when providing them. The providing unit adjusts the order of providing answers and advice based on the relevance of the answers and advice when providing them. For example, the providing unit prioritizes providing answers and advice to questions and inquiries with high relevance. The providing unit can also postpone providing answers and advice to questions and inquiries with low relevance. Furthermore, the providing unit can provide answers and advice to questions and inquiries with medium relevance in an appropriate order. In this way, the providing unit can set an order based on relevance, enabling more appropriate responses. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the relevance of answers and advice into the generation AI, and the generation AI can adjust the order of providing.

[0092] In the ART Navigator system, the suggestion unit can estimate a patient's emotions and adjust the treatment suggestion method based on the estimated patient's emotions. The suggestion unit can estimate a patient's emotions and adjust the treatment suggestion method based on the estimated patient's emotions. For example, if the patient is feeling anxious, the suggestion unit can provide a treatment suggestion in a gentle tone. Furthermore, if the patient is relaxed, the suggestion unit can provide a detailed treatment suggestion that focuses on the main points. Furthermore, if the patient is in a hurry, the suggestion unit can provide a concise treatment suggestion that focuses on the main points. This improves patient satisfaction by providing treatment suggestions that correspond 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 suggestion unit can be performed using, for example, the generative AI, or without the generative AI. For example, the suggestion unit can input the patient's emotions into the generative AI, which can then adjust the treatment suggestion method.

[0093] In the ART Navigator system, when the proposal unit makes a proposal, it can analyze the patient's past treatment history and select the optimal treatment proposal method. When making a proposal, the proposal unit analyzes the patient's past treatment history and selects the optimal treatment proposal method. For example, the proposal unit proposes the optimal treatment based on the patient's past treatment history. The proposal unit can also select an effective treatment from the patient's past treatment history. Furthermore, the proposal unit can analyze the patient's past treatment history and propose a treatment with fewer side effects. As a result, the proposal unit provides an optimal treatment proposal based on the past treatment history, thereby improving the treatment effect. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the patient's past treatment history into the generation AI, which can select the optimal treatment proposal method.

[0094] The ART Navigator system allows the suggestion unit to customize the treatment suggestion means based on the patient's current health condition when making a suggestion. The suggestion unit customizes the treatment suggestion means based on the patient's current health condition when making a suggestion. For example, the suggestion unit suggests an optimal treatment based on the patient's current health condition. The suggestion unit can also select an effective treatment means based on the patient's current health condition. Furthermore, the suggestion unit can analyze the patient's current health condition and suggest a treatment means with fewer side effects. As a result, the suggestion unit provides a treatment suggestion based on the patient's current health condition, thereby improving the treatment effect. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the patient's current health condition into the generation AI, which can then customize the treatment suggestion means.

[0095] In the ART Navigator system, the suggestion unit estimates the patient's emotions and prioritizes treatment suggestions based on the estimated patient emotions. The suggestion unit estimates the patient's emotions and prioritizes treatment suggestions based on the estimated patient emotions. For example, if the patient is feeling strong anxiety, the suggestion unit may prioritize treatment suggestions based on that anxiety. Furthermore, if the patient is relaxed, the suggestion unit may prioritize treatment suggestions that require immediate attention if the patient is in a hurry. This enables the suggestion unit to prioritize treatments based on the patient's emotions, thereby enabling faster and more appropriate treatment suggestions. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may 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 suggestion unit may be performed using, for example, the generative AI. For example, the suggestion unit may input the patient's emotions into the generative AI, which may then prioritize the treatment suggestions.

[0096] In the ART Navigator system, the suggestion unit can select the optimal treatment proposal method by taking into account the patient's geographic location information when making a proposal. The suggestion unit selects the optimal treatment proposal method by taking into account the patient's geographic location information when making a proposal. For example, if the patient lives in a specific area, the suggestion unit can prioritize suggesting treatments related to that area. Also, if the patient is traveling, the suggestion unit can prioritize suggesting treatments related to the patient's travel destination. Furthermore, if the patient is planning to move, the suggestion unit can prioritize suggesting treatments related to the patient's new residence. This allows the suggestion unit to provide treatment proposals based on geographic location information, enabling more appropriate treatment. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generating AI. For example, the suggestion unit can input the patient's geographic location information into the generating AI, which can select the optimal treatment proposal method.

[0097] In the ART Navigator system, when the suggestion unit makes a suggestion, it can analyze the patient's social media activity to suggest a treatment suggestion method. When making a suggestion, the suggestion unit analyzes the patient's social media activity to suggest a treatment suggestion method. For example, the suggestion unit may prioritize suggesting treatments related to topics frequently mentioned by the patient on social media. The suggestion unit may also suggest related treatments based on the opinions of experts the patient follows on social media. Furthermore, the suggestion unit may analyze the patient's current interests from the patient's social media activity and suggest related treatments. This allows the suggestion unit to provide more appropriate treatment by providing treatment suggestions based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the patient's social media activity into the generation AI, which then suggests a treatment suggestion method.

[0098] In the ART Navigator system, the collection unit can estimate a patient's emotions and adjust the information collection method based on the estimated patient's emotions. The collection unit estimates a patient's emotions and adjusts the information collection method based on the estimated patient's emotions. For example, if the patient is feeling anxious, the collection unit can collect information in a gentle tone. Furthermore, if the patient is relaxed, the collection unit can collect detailed information. Furthermore, if the patient is in a hurry, the collection unit can collect concise information that focuses on the main points. This allows the collection unit to collect more appropriate information by collecting information according to 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 collection unit may be performed using AI, or without AI. For example, the collection unit can input the patient's emotions into AI, which can then adjust the information collection method.

[0099] In the ART Navigator system, the collection unit can select the optimal information collection method by referring to the patient's past treatment history when collecting information. The collection unit selects the optimal information collection method by referring to the patient's past treatment history when collecting information. For example, the collection unit selects the optimal information collection method based on the patient's past treatment history. The collection unit can also select an effective information collection method from the patient's past treatment history. Furthermore, the collection unit can analyze the patient's past treatment history and select an information collection method with fewer side effects. This allows the collection unit to collect more appropriate information by collecting information based on the past treatment history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's past treatment history into AI, which can select the optimal information collection method.

[0100] In the ART Navigator system, the collection unit estimates the patient's emotions and determines the priority of information collection based on the estimated patient emotions. The collection unit estimates the patient's emotions and determines the priority of information collection based on the estimated patient emotions. For example, if the patient is feeling strong anxiety, the collection unit prioritizes information collection on that emotion. Furthermore, if the patient is relaxed, the collection unit can also prioritize information collection based on normal priority. Furthermore, if the patient is in a hurry, the collection unit can prioritize information collection that requires a rapid response. This enables the collection unit to set priorities according to the patient's emotions, enabling faster and more appropriate information collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-mentioned processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the patient's emotions into an AI, which then determines the priority of information collection.

[0101] In the ART Navigator system, the collection unit can select the optimal information collection method by taking into account the patient's geographic location information when collecting information. The collection unit selects the optimal information collection method by taking into account the patient's geographic location information when collecting information. For example, if the patient lives in a specific area, the collection unit can prioritize collecting information related to that area. Also, if the patient is traveling, the collection unit can prioritize collecting information related to the patient's travel destination. Furthermore, if the patient is planning to move, the collection unit can prioritize collecting information related to the patient's new residence. This allows the collection unit to collect more appropriate information by collecting information based on geographic location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's geographic location information into AI, which can select the optimal information collection method.

[0102] In the ART Navigator system, the management unit can estimate a patient's emotions and adjust the database and expertise management methods based on the estimated patient emotions. The management unit estimates a patient's emotions and adjusts the database and expertise management methods based on the estimated patient emotions. For example, if the patient is feeling anxious, the management unit can perform database management in a gentle tone. Furthermore, if the patient is relaxed, the management unit can perform detailed database management. Furthermore, if the patient is in a hurry, the management unit can perform concise database management that focuses on the key points. This allows the management unit to provide a management method that corresponds to the patient's emotions, thereby improving the efficiency of database and expertise management. 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 management unit may be performed using, for example, an AI, or without an AI. For example, the management unit can input the patient's emotions into an AI, which can then adjust the management method.

[0103] In the ART Navigator system, the management unit can select the optimal management method by referring to past data when managing a database. The management unit selects the optimal management method by referring to past data when managing a database. For example, the management unit selects the optimal database management method based on past data. The management unit can also select an effective database management method from past data. Furthermore, the management unit can analyze past data and select a database management method with fewer side effects. In this way, the management unit provides a management method based on past data, thereby improving the efficiency of database and expert knowledge management. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input past data into AI, which then selects the optimal management method.

[0104] In the ART Navigator system, the management unit estimates the patient's emotions and adjusts the database update frequency based on the estimated patient emotions. The management unit estimates the patient's emotions and adjusts the database update frequency based on the estimated patient emotions. For example, if the patient is feeling anxious, the management unit may update the database frequently. Alternatively, if the patient is relaxed, the management unit may update the database at a normal update frequency. Furthermore, if the patient is in a hurry, the management unit may update the database quickly. This allows the management unit to provide an update frequency according to the patient's emotions, thereby improving database management efficiency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may 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 management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit may input the patient's emotions into an AI, which may adjust the update frequency.

[0105] In the ART Navigator system, the management unit can weight data based on the time of data submission when managing the database. The management unit weights data based on the time of data submission when managing the database. For example, the management unit prioritizes weighting of recently submitted data. The management unit can also weight data that was submitted earlier later. Furthermore, the management unit can appropriately weight data that was submitted at a medium time. In this way, the management unit can weight data based on the time of submission, thereby making data management more efficient. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the time of data submission into AI, and the AI ​​can perform weighting.

[0106] The ART Navigator system allows the verification unit to estimate a patient's emotions and adjust the method for verifying the reliability of advice and treatment suggestions based on the estimated patient emotions. The verification unit estimates a patient's emotions and adjusts the method for verifying the reliability of advice and treatment suggestions based on the estimated patient emotions. For example, if the patient is feeling anxious, the verification unit may perform reliability verification in a gentle tone. Furthermore, if the patient is relaxed, the verification unit may perform detailed reliability verification. Furthermore, if the patient is in a hurry, the verification unit may perform concise reliability verification that focuses on the main points. This allows the verification unit to perform reliability verification according to the patient's emotions, thereby improving the reliability of advice and treatment suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may 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 verification unit may be performed using, for example, an AI, or without an AI. For example, the verification unit may input the patient's emotions into an AI, which may then adjust the reliability verification method.

[0107] In the ART Navigator system, the verification unit can select the optimal verification method by referring to the history of past advice and treatment proposals during verification. The verification unit selects the optimal verification method by referring to the history of past advice and treatment proposals during verification. For example, the verification unit selects the optimal reliability verification method based on the history of past advice and treatment proposals. The verification unit can also select an effective reliability verification method from the history of past advice and treatment proposals. Furthermore, the verification unit can analyze the history of past advice and treatment proposals and select a reliability verification method with fewer side effects. In this way, the verification unit provides a verification method based on the past history, thereby improving the reliability of the advice and treatment proposals. Some or all of the above-mentioned processing in the verification unit may be performed, for example, using AI, or may be performed without using AI. For example, the verification unit can input the history of past advice and treatment proposals into AI, which can select the optimal verification method.

[0108] In the ART Navigator system, the verification unit estimates the patient's emotions and adjusts the frequency of verification based on the estimated patient emotions. The verification unit estimates the patient's emotions and adjusts the frequency of verification based on the estimated patient emotions. For example, if the patient feels anxious, the verification unit may perform reliability verification frequently. If the patient feels relaxed, the verification unit may also perform reliability verification at a normal frequency. Furthermore, if the patient is in a hurry, the verification unit may also perform reliability verification quickly. This allows the verification unit to provide a verification frequency according to the patient's emotions, thereby improving the reliability of advice and treatment suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may 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 verification unit may be performed using, for example, an AI, or without an AI. For example, the verification unit may input the patient's emotions into an AI, which may then adjust the frequency of verification.

[0109] In the ART Navigator system, the verification unit can weight the verification data based on the time of submission of the advice or treatment proposal during verification. The verification unit weights the verification data based on the time of submission of the advice or treatment proposal during verification. For example, the verification unit prioritizes weighting of recently submitted advice or treatment proposals. The verification unit can also weight advice or treatment proposals that were submitted earlier later. Furthermore, the verification unit can appropriately weight advice or treatment proposals that were submitted at a moderate time. In this way, the verification unit can improve the reliability of the advice or treatment proposal by weighting based on the time of submission. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the time of submission of the advice or treatment proposal into AI, and the AI ​​can perform weighting. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, suggestion unit, collection unit, management unit, and verification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts patient inquiries and consultations using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes patient input information using the generation AI. The provision unit provides the analysis results to the patient using the output device 40 of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal treatment suggestions based on the patient's individual condition. The collection unit collects detailed patient information using the camera 42 and microphone 38B of the smart device 14. The management unit manages the database and expertise used by the generation AI using the database 24 of the data processing device 12. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the advice and treatment suggestions provided by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, suggestion unit, collection unit, management unit, and verification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts questions and inquiries from patients using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes input information from patients using a generation AI. The provision unit provides the analysis results to the patient using the speaker 240 of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal treatment suggestions based on the patient's individual condition. The collection unit collects detailed information about the patient using the camera 42 of the smart glasses 214. The management unit manages the database and expertise used by the generation AI using the database 24 of the data processing device 12. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the advice and treatment suggestions provided by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, suggestion unit, collection unit, management unit, and verification unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit accepts questions and inquiries from patients using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information input by the patient using the generation AI. The provision unit provides the analysis results to the patient using the speaker 240 of the headset-type terminal 314. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal treatment suggestions based on the patient's individual condition. The collection unit collects detailed information about the patient using the camera 42 of the headset-type terminal 314. The management unit manages the database and expertise used by the generation AI using the database 24 of the data processing device 12. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the advice and treatment suggestions provided by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, suggestion unit, collection unit, management unit, and verification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts questions and inquiries from patients using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information input by the patient using the generation AI. The provision unit provides the analysis results to the patient using the speaker 240 of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes optimal treatment suggestions based on the patient's individual condition. The collection unit collects detailed information about the patient using the camera 42 of the robot 414. The management unit manages the database and expertise used by the generation AI using the database 24 of the data processing device 12. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the reliability of the advice and treatment suggestions provided by the generation AI.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The ART Navigator system collects data on a patient's lifestyle and daily activities, which can be used for analysis by the analysis unit. For example, the collection unit collects data such as the patient's exercise volume, dietary habits, and sleep patterns. The collection unit can also obtain data from devices such as the patient's smartwatch or fitness tracker. Furthermore, the collection unit can collect data based on the patient's diary or self-report. This allows the collection unit to provide detailed data based on the patient's lifestyle, improving the accuracy of the analysis by the analysis unit.

[0112] The ART Navigator system can estimate a patient's emotions and adjust the timing of treatment suggestions based on the estimated emotions. For example, if the patient is feeling stressed, the suggestion unit can suggest a treatment at a time when the patient is able to relax. Also, if the patient is relaxed, the suggestion unit can make a detailed treatment suggestion. Furthermore, if the patient is in a hurry, the suggestion unit can quickly make a concise treatment suggestion. In this way, the suggestion unit can suggest a treatment at a time that suits the patient's emotions, thereby improving the patient's acceptance.

[0113] The ART Navigator system can collect a patient's family history and genetic information and use this information for analysis by the analysis unit. For example, the collection unit collects information about the patient's family history and evaluates genetic risk. The collection unit can also obtain the patient's genetic test results and use them for analysis by the analysis unit. Furthermore, the collection unit can collect information about the patient's family structure and living environment and reflect this information in the analysis by the analysis unit. This allows the collection unit to provide detailed data based on the patient's genetic information, improving the accuracy of the analysis by the analysis unit.

[0114] The ART Navigator system can estimate a patient's emotions and customize the content of advice based on the estimated emotions. For example, if the patient is feeling anxious, the providing unit can provide reassuring advice. If the patient is relaxed, the providing unit can also provide advice with detailed information. Furthermore, if the patient is in a hurry, the providing unit can provide concise and to-the-point advice. In this way, the providing unit can provide advice that is appropriate for the patient's emotions, thereby improving patient understanding and satisfaction.

[0115] The ART Navigator system can make treatment suggestions taking into account the patient's living environment and social background. For example, the suggestion unit can suggest feasible treatments based on the patient's living environment and work environment. The suggestion unit can also suggest treatments that will provide support, taking into account the patient's social support network (family, friends, etc.). Furthermore, the suggestion unit can also suggest cost-effective treatments, taking into account the patient's financial situation. As a result, the suggestion unit can make treatment suggestions that are tailored to the patient's living environment and social background, thereby improving the feasibility of treatment.

[0116] The ART Navigator system can estimate a patient's emotions and adjust the content of treatment suggestions based on the estimated emotions. For example, if the patient feels anxious, the suggestion unit can suggest a low-risk treatment. If the patient feels relaxed, the suggestion unit can also suggest a detailed treatment. Furthermore, if the patient is in a hurry, the suggestion unit can also suggest a treatment that can be implemented quickly. In this way, the suggestion unit can make treatment suggestions based on the patient's emotions, thereby improving the patient's acceptance.

[0117] The ART Navigator system tracks a patient's treatment history and response, and can use this information for analysis by the analysis unit. For example, the collection unit collects a patient's past treatment history and evaluates the effectiveness of treatment. The collection unit can also track a patient's response to treatment (side effects, improvement, etc.) and reflect this in the analysis by the analysis unit. Furthermore, the collection unit can collect feedback on the patient's treatment and use it for analysis by the analysis unit. This allows the collection unit to provide detailed data based on the patient's treatment history and response, improving the accuracy of the analysis by the analysis unit.

[0118] The ART Navigator system can estimate a patient's emotions and adjust the priority of treatment suggestions based on the estimated emotions. For example, if the patient is feeling strong anxiety, the suggestion unit will prioritize that treatment suggestion. If the patient is relaxed, the suggestion unit can also suggest treatments with normal priority. Furthermore, if the patient is in a hurry, the suggestion unit can also prioritize treatment suggestions that require a quick response. This allows the suggestion unit to set priorities according to the patient's emotions, enabling faster and more appropriate treatment suggestions.

[0119] The ART Navigator system can collect patients' expectations and goals regarding treatment and use them for analysis by the analysis unit. For example, the collection unit collects patients' expectations and goals regarding treatment and evaluates the direction of treatment. The collection unit can also collect patients' hopes and concerns regarding treatment and reflect them in the analysis by the analysis unit. Furthermore, the collection unit can collect feedback on patients' treatment and use it in the analysis by the analysis unit. This allows the collection unit to provide detailed data based on patients' expectations and goals, improving the accuracy of the analysis by the analysis unit.

[0120] The ART Navigator system can estimate a patient's emotions and adjust the treatment suggestion method based on the estimated emotions. For example, if the patient feels anxious, the suggestion unit can provide a treatment suggestion in a gentle tone. If the patient feels relaxed, the suggestion unit can also provide a detailed treatment suggestion. Furthermore, if the patient is in a hurry, the suggestion unit can provide a concise treatment suggestion that focuses on the main points. In this way, the suggestion unit can provide treatment suggestions that correspond to the patient's emotions, thereby improving patient satisfaction.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit inputs the patient's questions and inquiries. Patient questions and inquiries include, but are not limited to, specific questions such as, "What should I do in the early stages of infertility treatment?" and "What are the side effects of a particular treatment?" The reception unit can accept the patient's questions and inquiries using text input or voice input. The reception unit also has a function to send the patient's input to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis unit generates the optimal answer to the patient's question based on past data and specialized knowledge. The generation AI can use text generation AI (e.g., LLM) to generate answers to the patient's questions and inquiries. The analysis unit can also use the generation AI to make optimal treatment suggestions based on the patient's individual situation. Step 3: The provider provides appropriate answers and advice based on the information analyzed by the analyzer. The provider has the function of providing the answers generated by the generation AI to the patient, and can provide answers and advice in text or audio format. Step 4: The proposal unit proposes the optimal treatment for each individual patient based on the information provided by the provision unit. The proposal unit proposes the optimal treatment based on information such as the patient's age, health condition, and past treatment history. The proposal unit can use generative AI to propose the optimal treatment for each patient.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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, in order to avoid confusion and to 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where patients can input their questions and inquiries; an analysis unit that analyzes the information input by the reception unit; a providing unit that provides answers and advice based on the information analyzed by the analyzing unit; a suggestion unit that makes a treatment suggestion to an individual patient based on the information provided by the provision unit; Equipped with A system characterized by:

2. Equipped with a collection department to collect information on the patient's age, health condition, and past medical history 2. The system of claim 1.

3. Equipped with a management department that manages the database and expertise used by the generation AI 2. The system of claim 1.

4. Equipped with a verification unit to ensure the reliability of advice and treatment suggestions provided by the generation AI 2. The system of claim 1.

5. The collecting unit Collect patient information in cooperation with the suggestion unit 3. The system of claim 2.

6. The management unit Manage databases and specialized knowledge in cooperation with the analysis department 4. The system of claim 3.

7. The verification unit Work with the provider to ensure the reliability of advice and treatment proposals 5. The system of claim 4.

8. The reception unit Estimate the patient's emotions and adjust how you respond to questions and inquiries based on the estimated emotions.

2. The system of claim 1.

9. The reception unit Analyze the patient's past consultation history and select the optimal reception method 2. The system of claim 1.

10. The reception unit At the time of check-in, filtering is performed based on the patient's current life situation and areas of interest.

2. The system of claim 1.

11. The reception unit Estimate the patient's emotions and prioritize the questions and inquiries to be received based on the estimated patient emotions.

2. The system of claim 1.

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