System

Abstract

An object of a system according to an embodiment is to provide an optimal treatment approach to a patient.SOLUTION: A system includes a collection part, an analysis part, an opinion collection part, and a proposal part. The collection unit collects data of a disease name, a symptom, a past treatment history, and a lifestyle of a patient. The analysis unit analyzes the data collected by the collection unit and specifies a treatment approach required by the patient. The opinion collection unit collects opinions of a plurality of medical experts based on the treatment approach specified by the analysis unit. The proposal unit proposes a treatment plan based on the opinions collected by the opinion collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, there is a risk that treatment approaches will be biased depending on the field or level of the doctor, and there is room for improvement in providing patients with the most appropriate treatment approach.

[0005] The system according to the embodiment aims to provide an optimal treatment approach to the patient. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an opinion collection unit, and a proposal unit. The collection unit collects data on the patient's disease name, symptoms, past treatment history, and lifestyle. The analysis unit analyzes the data collected by the collection unit and identifies the treatment approach desired by the patient. The opinion collection unit collects opinions from multiple medical professionals based on the treatment approach identified by the analysis unit. The proposal unit proposes a treatment plan based on the opinions collected by the opinion collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal treatment approach to the patient. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A treatment approach optimization system according to an embodiment of the present invention collects and analyzes data such as a patient's disease name, symptoms, past treatment history, and lifestyle, and provides an optimal treatment approach. The treatment approach optimization system collects detailed data such as a patient's disease name, symptoms, past treatment history, and lifestyle, and uses AI to analyze the data to identify the treatment approach desired by the patient. Furthermore, based on the identified treatment approach, it collects opinions from multiple medical experts and proposes an optimal treatment plan. For example, the treatment approach optimization system collects data such as a patient's disease name, symptoms, past treatment history, and lifestyle. For example, it collects data such as the effectiveness and side effects of past treatments and the patient's current lifestyle. Next, the treatment approach optimization system uses AI to analyze the collected data and identify the treatment approach desired by the patient. For example, the AI ​​considers the effectiveness and side effects of past treatments and the patient's current lifestyle to identify the optimal treatment approach. Next, the treatment approach optimization system collects opinions from multiple medical experts based on the identified treatment approach. For example, it collects opinions from medical experts from different fields, such as internists, surgeons, and psychiatrists, and proposes an optimal treatment plan. Finally, the treatment approach optimization system proposes the optimal treatment plan to the patient. For example, the optimal treatment plan for a patient is proposed based on the opinions of multiple medical professionals. This allows the treatment approach optimization system to collect and analyze data such as the patient's disease name, symptoms, past treatment history, and lifestyle, and provide the optimal treatment approach. This allows the treatment approach optimization system to collect and analyze data such as the patient's disease name, symptoms, past treatment history, and lifestyle, and provide the optimal treatment approach. For example, a treatment approach that takes into account the effects and side effects of previous treatments and is tailored to the patient's current lifestyle is provided. In addition, because a comprehensive treatment plan is provided based on the opinions of multiple medical professionals, the patient can receive the optimal treatment rather than a biased approach.

[0029] A therapeutic approach optimization system according to an embodiment includes a collection unit, an analysis unit, an opinion collection unit, and a proposal unit. The collection unit collects data on the patient's disease name, symptoms, past treatment history, and lifestyle. The patient's disease name includes, but is not limited to, chronic diseases and acute diseases. The symptoms include, but are not limited to, pain, fever, fatigue, and the like. The past treatment history includes, but is not limited to, the type, duration, and results of treatment. The lifestyle includes, but is not limited to, eating habits, exercise habits, and sleep patterns. The collection unit collects data on the patient's past treatment history and lifestyle. For example, the collection unit collects data on the effects and side effects of treatments the patient has received in the past and their current lifestyle. The collection unit can also collect data on the patient's lifestyle. For example, the collection unit can collect data on the patient's eating habits, exercise habits, sleep patterns, and the like. The analysis unit analyzes the data collected by the collection unit and identifies a therapeutic approach desired by the patient. The analysis unit, for example, identifies a treatment approach desired by the patient based on the collected data. For example, the analysis unit identifies an optimal treatment approach by taking into consideration the effects and side effects of treatments the patient has received in the past, their current lifestyle, and the like. The analysis unit can also analyze the collected data using AI. For example, the analysis unit analyzes data using a machine learning algorithm to identify a treatment approach desired by the patient. The opinion collection unit collects opinions from multiple medical experts based on the treatment approach identified by the analysis unit. The opinion collection unit collects opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists. For example, the opinion collection unit collects opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists, and proposes an optimal treatment plan. The opinion collection unit can also collect opinions from medical experts using AI. For example, the opinion collection unit collects opinions from medical experts through questionnaire surveys or interviews. The proposal unit proposes a treatment plan based on the opinions collected by the opinion collection unit. The proposal unit proposes an optimal treatment plan based on the collected opinions. For example, the proposal unit proposes an optimal treatment plan for the patient based on the opinions of multiple medical experts.The suggestion unit can also propose a treatment plan using AI. For example, the suggestion unit uses AI to generate an optimal treatment plan based on collected opinions and proposes it to the patient. As a result, the treatment approach optimization system according to the embodiment can collect and analyze data such as the patient's disease name, symptoms, past treatment history, and lifestyle, and provide the optimal treatment approach.

[0030] The collection unit can collect data on the patient's past treatment history and lifestyle habits. The collection unit collects, for example, data on the patient's past treatment history and lifestyle habits. For example, the collection unit collects data on the effects and side effects of treatments the patient has received in the past, and data on the patient's current lifestyle habits. The collection unit can also collect data on the patient's lifestyle habits. For example, the collection unit can collect data on the patient's eating habits, exercise habits, sleep patterns, and the like. By collecting data on the patient's past treatment history and lifestyle habits, more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the patient's past treatment history and lifestyle habits into AI, which can analyze the data.

[0031] The analysis unit can identify the treatment approach desired by the patient based on the collected data. The analysis unit, for example, identifies the treatment approach desired by the patient based on the collected data. For example, the analysis unit considers the effects and side effects of treatments the patient has received in the past, their current lifestyle, and the like to identify the optimal treatment approach. The analysis unit can also analyze the collected data using AI. For example, the analysis unit analyzes the data using a machine learning algorithm to identify the treatment approach desired by the patient. This makes it possible to provide a more appropriate treatment approach by identifying the treatment approach desired by the patient based on the collected data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which analyzes the data and identifies the treatment approach.

[0032] The opinion collection unit can collect opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists. For example, the opinion collection unit collects opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists, and proposes an optimal treatment plan. The opinion collection unit can also collect opinions from medical experts using AI. For example, the opinion collection unit collects opinions from medical experts through questionnaire surveys and interviews. By collecting opinions from medical experts in different fields, a comprehensive treatment plan can be provided rather than a biased approach. Some or all of the above-mentioned processing in the opinion collection unit may be performed using AI, for example, or may be performed without using AI. For example, the opinion collection unit can input opinions from medical experts into AI, which analyzes the opinions and proposes an optimal treatment plan.

[0033] The proposal unit can propose a treatment plan based on the collected opinions. The proposal unit, for example, proposes an optimal treatment plan based on the collected opinions. For example, the proposal unit proposes an optimal treatment plan for a patient based on the opinions of multiple medical experts. The proposal unit can also propose a treatment plan using AI. For example, the proposal unit generates an optimal treatment plan using AI based on the collected opinions and proposes it to the patient. In this way, by proposing an optimal treatment plan based on the collected opinions, it is possible to provide an optimal treatment approach for the patient. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the collected opinions into AI, which then generates an optimal treatment plan and proposes it to the patient.

[0034] The proposal unit can provide a treatment plan to the patient. The proposal unit, for example, provides an optimal treatment plan for the patient. For example, the proposal unit proposes an optimal treatment plan for the patient based on the opinions of multiple medical professionals. The proposal unit can also provide a treatment plan using AI. For example, the proposal unit generates an optimal treatment plan using AI based on collected opinions and proposes it to the patient. By providing an optimal treatment plan for the patient, it is possible to provide a treatment approach tailored to the patient's needs. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input collected opinions into AI, which then generates an optimal treatment plan and proposes it to the patient.

[0035] The collection unit can analyze the patient's past treatment history and select the optimal data collection method. The collection unit, for example, analyzes the patient's past treatment history and selects the optimal data collection method. For example, the collection unit selects an effective data collection method based on the effects of treatments the patient has received in the past. The collection unit can also adjust the frequency of data collection based on the patient's past treatment history. The collection unit can also analyze the patient's past treatment history and preferentially collect data related to specific treatments. In this way, the optimal data collection method can be selected by analyzing the patient's past treatment history. Some or all of the above-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 analyzes the data and selects the optimal data collection method.

[0036] The collection unit can perform filtering based on the patient's current living situation and areas of interest when collecting data. For example, the collection unit can perform filtering based on the patient's current living situation and areas of interest when collecting data. For example, the collection unit can take the patient's current living situation into consideration and collect only relevant data. The collection unit can also prioritize collecting necessary data based on the patient's areas of interest. The collection unit can also filter unnecessary data based on the patient's living situation and areas of interest. In this way, filtering based on the patient's current living situation and areas of interest can collect only relevant data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the patient's living situation and areas of interest into AI, which can analyze the data and perform filtering.

[0037] The collection unit can select the optimal collection means depending on the patient's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the patient's input method (voice, text, image, etc.) when collecting data. For example, if the patient prefers voice input, the collection unit collects data by voice. Also, if the patient prefers text input, the collection unit can collect data by text. Also, if the patient prefers image input, the collection unit can collect data by image. In this way, by selecting the optimal collection means depending on the patient's input method, data can be collected efficiently. 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 input data into AI, which then selects the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information when collecting data. For example, the collection unit collects region-specific health data based on the patient's current location. The collection unit can also collect data on related medical facilities based on the patient's geographical location information. The collection unit can also prioritize collecting data related to regional health risks by taking into account the patient's geographical location information. In this way, region-specific health data can be collected by preferentially collecting highly relevant data by taking into account the patient's geographical location information. Some or all of the above-described processing by 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 geographical location information into AI, which can analyze the data and prioritize collecting highly relevant data.

[0039] The collection unit can analyze the patient's social media activity and collect related data when collecting data. For example, the collection unit can analyze the patient's social media activity and collect related data when collecting data. For example, the collection unit can analyze the patient's social media posts and collect related health data. The collection unit can also collect data related to health risks based on the patient's social media activity. The collection unit can also collect related health data by referring to the activities of the patient's friends on social media. In this way, related health data can be collected by analyzing the patient's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the patient's social media activity into AI, which can analyze the data and collect related data.

[0040] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the patient's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on the patient's past feedback. The collection unit can also change the type of data to be collected by reflecting the patient's past feedback. The collection unit can also adjust the frequency of data collection by taking the patient's past feedback into consideration. In this way, the collection method can be customized by reflecting the patient's past feedback, enabling more appropriate data collection. 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 data of the patient's past feedback into AI, which can analyze the data and customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, and the AI ​​can adjust the level of detail of the analysis based on the importance of the data.

[0042] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a specific analysis algorithm to data related to a disease name. The analysis unit can also apply a different analysis algorithm to data related to symptoms. The analysis unit can also apply a different analysis algorithm to data related to lifestyle. In this way, by applying different analysis algorithms depending on the category of data, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI, which can then apply different analysis algorithms depending on the category of data.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the current analysis based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also increase the reliability of the analysis by using the patient's past analysis results. In this way, the accuracy of the current analysis can be improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the patient's past analysis results into AI, and the AI ​​can analyze the data to improve the accuracy of the current analysis.

[0044] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. The analysis unit can also adjust the order of analysis depending on the time of data collection. In this way, by determining the analysis priority based on the time of data collection, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection into AI, which analyzes the data and determines the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which analyzes the data and adjusts the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise during analysis. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the patient does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the patient's level of expertise. By adjusting the use of technical terms in the analysis according to the patient's level of expertise, analysis results that are easy for the patient to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the patient's level of expertise into AI, which then analyzes the data and adjusts the use of technical terms.

[0047] The opinion collection unit can improve the accuracy of opinion collection by taking into account the interrelationships between medical experts when collecting opinions. For example, the opinion collection unit can improve the accuracy of opinion collection by taking into account the interrelationships between medical experts when collecting opinions. For example, the opinion collection unit weights opinions by taking into account the interrelationships between medical experts. The opinion collection unit can also evaluate the reliability of opinions based on the interrelationships between medical experts. The opinion collection unit can also determine the priority of opinions to be collected by referring to the interrelationships between medical experts. In this way, the accuracy of collection can be improved by taking into account the interrelationships between medical experts. Some or all of the above-described processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input data on the interrelationships between medical experts into AI, which can analyze the data to improve the accuracy of collection.

[0048] The opinion collection unit can collect opinions while taking into account attribute information of medical professionals. For example, the opinion collection unit collects opinions while taking into account attribute information of medical professionals. For example, the opinion collection unit collects opinions based on the medical professionals' fields of expertise. The opinion collection unit can also collect opinions while taking into account the medical professionals' years of experience. The opinion collection unit can also collect opinions while referring to the medical professionals' past opinion provision history. In this way, by taking into account the attribute information of medical professionals, more reliable opinions can be collected. Some or all of the above-mentioned processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input attribute information of medical professionals into AI, and the AI ​​can analyze the data and collect opinions.

[0049] The opinion collecting unit can weight the collected opinions based on the frequency of submissions by medical experts when collecting opinions. For example, the opinion collecting unit weights the collected opinions based on the frequency of submissions by medical experts when collecting opinions. For example, the opinion collecting unit places more importance on opinions from medical experts who submit more frequently. The opinion collecting unit can also weight opinions from medical experts who submit less frequently. The opinion collecting unit can also weight the opinions based on the frequency of submission. In this way, by weighting the collected opinions based on the frequency of submissions by medical experts, more reliable opinions can be collected. Some or all of the above-mentioned processing in the opinion collecting unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collecting unit can input data on the frequency of submissions by medical experts into AI, and the AI ​​can analyze the data and weight the collected opinions.

[0050] The opinion collection unit can collect opinions taking into consideration the geographical distribution of medical experts. For example, the opinion collection unit collects opinions taking into consideration the geographical distribution of medical experts. For example, the opinion collection unit prioritizes collecting opinions from geographically close medical experts. The opinion collection unit can also collect opinions from geographically distant medical experts in a balanced manner. The opinion collection unit can also collect region-specific medical information taking into consideration the geographical distribution. In this way, region-specific medical information can be collected by taking into consideration the geographical distribution of medical experts. Some or all of the above-described processing in the opinion collection unit may be performed using, or without, AI, for example. For example, the opinion collection unit can input data on the geographical distribution of medical experts into AI, and the AI ​​can analyze the data and collect the information.

[0051] The opinion collection unit can improve the accuracy of the opinion collection by referring to related literature when collecting opinions. For example, the opinion collection unit can improve the accuracy of the opinion collection by referring to related literature when collecting opinions. For example, the opinion collection unit evaluates the reliability of the opinions of medical experts based on the related literature. The opinion collection unit can also improve the accuracy of the opinion collection by referring to the related literature. The opinion collection unit can also determine the priority of the opinions to be collected by referring to the related literature. In this way, the accuracy of the collection can be improved by referring to the related literature. Some or all of the above-mentioned processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input data of related literature into AI, which analyzes the data to improve the accuracy of the collection.

[0052] The opinion collection unit can collect opinions taking into consideration the market value of medical professionals. For example, the opinion collection unit collects opinions taking into consideration the market value of medical professionals. For example, the opinion collection unit prioritizes collecting opinions of medical professionals with high market value. The opinion collection unit can also collect opinions of medical professionals with low market value in a balanced manner. The opinion collection unit can also weight opinions taking into consideration market value. In this way, by taking into consideration the market value of medical professionals, more reliable opinions can be collected. Some or all of the above-mentioned processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input data on the market value of medical professionals into AI, and the AI ​​can analyze the data and collect opinions.

[0053] The proposal unit can adjust the level of detail of the proposal based on the importance of the treatment plan when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the treatment plan when making a proposal. For example, the proposal unit makes a detailed proposal for a treatment plan with a high level of importance. The proposal unit can also make a simplified proposal for a treatment plan with a low level of importance. The proposal unit can also adjust the depth of the proposal depending on the importance of the treatment plan. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the treatment plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the treatment plan to AI, which can analyze the data and adjust the level of detail of the proposal.

[0054] The suggestion unit can apply different suggestion algorithms depending on the category of the treatment plan when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the treatment plan when making a suggestion. For example, the suggestion unit can apply a specific suggestion algorithm to an internal medicine treatment plan. The suggestion unit can also apply a different suggestion algorithm to a surgical treatment plan. The suggestion unit can also apply a different suggestion algorithm to a psychiatric treatment plan. In this way, by applying different suggestion algorithms depending on the category of the treatment plan, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the category of the treatment plan into AI, which can analyze the data and apply different suggestion algorithms.

[0055] The suggestion unit can improve the accuracy of the suggestion by referring to the patient's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the patient's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the current suggestion based on the patient's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the past suggestion results. The suggestion unit can also increase the reliability of the suggestion by using the patient's past suggestion results. In this way, the accuracy of the current suggestion can be improved by referring to the patient's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's past suggestion results into AI, which can analyze the data to improve the accuracy of the current suggestion.

[0056] The proposal unit can determine the priority of proposals based on the time of submission of the treatment plans when making proposals. The proposal unit, for example, determines the priority of proposals based on the time of submission of the treatment plans when making proposals. For example, the proposal unit preferentially proposes the most recent treatment plans. The proposal unit can also propose older treatment plans later. The proposal unit can also adjust the order of proposals depending on the time of submission of the treatment plans. In this way, by determining the priority of proposals based on the time of submission of the treatment plans, the most recent treatment plans can be preferentially proposed. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the time of submission of the treatment plans into AI, which can analyze the data and determine the priority of proposals.

[0057] The proposal unit can adjust the order of proposals based on the relevance of the treatment plans when proposing them. For example, the proposal unit can adjust the order of proposals based on the relevance of the treatment plans when proposing them. For example, the proposal unit prioritizes proposing highly relevant treatment plans. The proposal unit can also postpone proposing less relevant treatment plans. The proposal unit can also adjust the order of proposals according to the relevance of the treatment plans. In this way, by adjusting the order of proposals based on the relevance of the treatment plans, highly relevant treatment plans can be prioritized for proposal. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the relevance of the treatment plans into AI, which can analyze the data and adjust the order of proposals.

[0058] The suggestion unit can adjust the use of technical terminology in the proposal depending on the patient's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal depending on the patient's level of expertise when making a proposal. For example, if the patient has technical expertise, the suggestion unit uses a lot of technical terminology. If the patient does not have technical expertise, the suggestion unit can also provide a proposal in simple language. The suggestion unit can also adjust the way the proposal is expressed depending on the patient's level of expertise. In this way, by adjusting the use of technical terminology in the proposal depending on the patient's level of expertise, it is possible to provide a proposal that is easy for the patient to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's level of expertise into AI, which can analyze the data and adjust the use of technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The treatment approach optimization system can further include a genetic information analysis unit that collects and analyzes the patient's genetic information. For example, the genetic information analysis unit collects the patient's DNA sample and identifies genetic risk factors. The genetic information analysis unit can also predict how effective a specific treatment will be for the patient based on the genetic information. Furthermore, the genetic information analysis unit can also suggest the optimal drug therapy for the patient based on the genetic information. This is expected to realize personalized medicine that takes genetic information into account and provide more effective treatment approaches.

[0061] The treatment approach optimization system can further include an environmental analysis unit that collects and analyzes data on the patient's living environment. For example, the environmental analysis unit collects data on the patient's living environment and work environment and evaluates the impact of environmental factors on health. The environmental analysis unit can also make lifestyle improvement suggestions appropriate for the patient based on the environmental data. Furthermore, the environmental analysis unit can also propose a treatment approach that takes environmental factors into account. This is expected to provide a comprehensive treatment approach that takes the patient's living environment into account.

[0062] The treatment approach optimization system may further include a social network analysis unit that collects and analyzes social network data of the patient. For example, the social network analysis unit may collect data on relationships between the patient and family and friends and evaluate the influence of social support. The social network analysis unit may also propose a support plan appropriate for the patient based on the social network data. Furthermore, the social network analysis unit may also propose a treatment approach that takes social support into consideration. This is expected to provide a comprehensive treatment approach that takes the patient's social environment into consideration.

[0063] The treatment approach optimization system can further include a motion analysis unit that collects and analyzes the patient's exercise data. For example, the motion analysis unit collects data on the patient's exercise habits and exercise volume, and evaluates the impact of exercise on health. The motion analysis unit can also propose an exercise plan suitable for the patient based on the exercise data. Furthermore, the motion analysis unit can propose a treatment approach that takes into account the patient's exercise habits. This is expected to provide a comprehensive treatment approach that takes into account the patient's exercise habits.

[0064] The treatment approach optimization system may further include a diet analysis unit that collects and analyzes the patient's dietary data. For example, the diet analysis unit may collect data on the patient's eating habits and nutritional intake, and evaluate the impact of diet on health. The diet analysis unit may also propose a meal plan appropriate for the patient based on the dietary data. Furthermore, the diet analysis unit may also propose a treatment approach that takes into account the patient's eating habits. This is expected to provide a comprehensive treatment approach that takes into account the patient's eating habits.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data on the patient's illness, symptoms, past treatment history, and lifestyle. For example, illnesses include chronic and acute illnesses, and symptoms include pain, fever, fatigue, etc. Past treatment history includes the type, duration, and results of treatment, and lifestyle includes eating habits, exercise habits, sleep patterns, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the treatment approach desired by the patient. For example, based on the collected data, it considers the effectiveness and side effects of the patient's past treatments, current lifestyle habits, etc., to identify the optimal treatment approach. The analysis unit can also use AI to analyze the data using machine learning algorithms. Step 3: The opinion gathering unit collects the opinions of multiple medical professionals based on the treatment approach identified by the analysis unit. For example, opinions are collected from medical professionals in different fields, such as internists, surgeons, and psychiatrists, to gather their opinions in order to propose the optimal treatment plan. The opinion gathering unit can also use AI to collect opinions from medical professionals through questionnaire surveys and interviews. Step 4: The suggestion unit proposes a treatment plan based on the opinions collected by the opinion collection unit. For example, it proposes the optimal treatment plan for the patient based on the opinions of multiple medical professionals. The suggestion unit can also use AI to generate the optimal treatment plan based on the collected opinions and propose it to the patient.

[0067] (Example 2) A treatment approach optimization system according to an embodiment of the present invention collects and analyzes data such as a patient's disease name, symptoms, past treatment history, and lifestyle, and provides an optimal treatment approach. The treatment approach optimization system collects detailed data such as a patient's disease name, symptoms, past treatment history, and lifestyle, and uses AI to analyze the data to identify the treatment approach desired by the patient. Furthermore, based on the identified treatment approach, it collects opinions from multiple medical experts and proposes an optimal treatment plan. For example, the treatment approach optimization system collects data such as a patient's disease name, symptoms, past treatment history, and lifestyle. For example, it collects data such as the effectiveness and side effects of past treatments and the patient's current lifestyle. Next, the treatment approach optimization system uses AI to analyze the collected data and identify the treatment approach desired by the patient. For example, the AI ​​considers the effectiveness and side effects of past treatments and the patient's current lifestyle to identify the optimal treatment approach. Next, the treatment approach optimization system collects opinions from multiple medical experts based on the identified treatment approach. For example, it collects opinions from medical experts from different fields, such as internists, surgeons, and psychiatrists, and proposes an optimal treatment plan. Finally, the treatment approach optimization system proposes the optimal treatment plan to the patient. For example, the optimal treatment plan for a patient is proposed based on the opinions of multiple medical professionals. This allows the treatment approach optimization system to collect and analyze data such as the patient's disease name, symptoms, past treatment history, and lifestyle, and provide the optimal treatment approach. This allows the treatment approach optimization system to collect and analyze data such as the patient's disease name, symptoms, past treatment history, and lifestyle, and provide the optimal treatment approach. For example, a treatment approach that takes into account the effects and side effects of previous treatments and is tailored to the patient's current lifestyle is provided. In addition, because a comprehensive treatment plan is provided based on the opinions of multiple medical professionals, the patient can receive the optimal treatment rather than a biased approach.

[0068] A therapeutic approach optimization system according to an embodiment includes a collection unit, an analysis unit, an opinion collection unit, and a proposal unit. The collection unit collects data on the patient's disease name, symptoms, past treatment history, and lifestyle. The patient's disease name includes, but is not limited to, chronic diseases and acute diseases. The symptoms include, but are not limited to, pain, fever, fatigue, and the like. The past treatment history includes, but is not limited to, the type, duration, and results of treatment. The lifestyle includes, but is not limited to, eating habits, exercise habits, and sleep patterns. The collection unit collects data on the patient's past treatment history and lifestyle. For example, the collection unit collects data on the effects and side effects of treatments the patient has received in the past and their current lifestyle. The collection unit can also collect data on the patient's lifestyle. For example, the collection unit can collect data on the patient's eating habits, exercise habits, sleep patterns, and the like. The analysis unit analyzes the data collected by the collection unit and identifies a therapeutic approach desired by the patient. The analysis unit, for example, identifies a treatment approach desired by the patient based on the collected data. For example, the analysis unit identifies an optimal treatment approach by taking into consideration the effects and side effects of treatments the patient has received in the past, their current lifestyle, and the like. The analysis unit can also analyze the collected data using AI. For example, the analysis unit analyzes data using a machine learning algorithm to identify a treatment approach desired by the patient. The opinion collection unit collects opinions from multiple medical experts based on the treatment approach identified by the analysis unit. The opinion collection unit collects opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists. For example, the opinion collection unit collects opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists, and proposes an optimal treatment plan. The opinion collection unit can also collect opinions from medical experts using AI. For example, the opinion collection unit collects opinions from medical experts through questionnaire surveys or interviews. The proposal unit proposes a treatment plan based on the opinions collected by the opinion collection unit. The proposal unit proposes an optimal treatment plan based on the collected opinions. For example, the proposal unit proposes an optimal treatment plan for the patient based on the opinions of multiple medical experts.The suggestion unit can also propose a treatment plan using AI. For example, the suggestion unit uses AI to generate an optimal treatment plan based on collected opinions and proposes it to the patient. As a result, the treatment approach optimization system according to the embodiment can collect and analyze data such as the patient's disease name, symptoms, past treatment history, and lifestyle, and provide the optimal treatment approach.

[0069] The collection unit can collect data on the patient's past treatment history and lifestyle habits. The collection unit collects, for example, data on the patient's past treatment history and lifestyle habits. For example, the collection unit collects data on the effects and side effects of treatments the patient has received in the past, and data on the patient's current lifestyle habits. The collection unit can also collect data on the patient's lifestyle habits. For example, the collection unit can collect data on the patient's eating habits, exercise habits, sleep patterns, and the like. By collecting data on the patient's past treatment history and lifestyle habits, more detailed information can be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the patient's past treatment history and lifestyle habits into AI, which can analyze the data.

[0070] The analysis unit can identify the treatment approach desired by the patient based on the collected data. The analysis unit, for example, identifies the treatment approach desired by the patient based on the collected data. For example, the analysis unit considers the effects and side effects of treatments the patient has received in the past, their current lifestyle, and the like to identify the optimal treatment approach. The analysis unit can also analyze the collected data using AI. For example, the analysis unit analyzes the data using a machine learning algorithm to identify the treatment approach desired by the patient. This makes it possible to provide a more appropriate treatment approach by identifying the treatment approach desired by the patient based on the collected data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which analyzes the data and identifies the treatment approach.

[0071] The opinion collection unit can collect opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists. For example, the opinion collection unit collects opinions from medical experts in different fields, such as internists, surgeons, and psychiatrists, and proposes an optimal treatment plan. The opinion collection unit can also collect opinions from medical experts using AI. For example, the opinion collection unit collects opinions from medical experts through questionnaire surveys and interviews. By collecting opinions from medical experts in different fields, a comprehensive treatment plan can be provided rather than a biased approach. Some or all of the above-mentioned processing in the opinion collection unit may be performed using AI, for example, or may be performed without using AI. For example, the opinion collection unit can input opinions from medical experts into AI, which analyzes the opinions and proposes an optimal treatment plan.

[0072] The proposal unit can propose a treatment plan based on the collected opinions. The proposal unit, for example, proposes an optimal treatment plan based on the collected opinions. For example, the proposal unit proposes an optimal treatment plan for a patient based on the opinions of multiple medical experts. The proposal unit can also propose a treatment plan using AI. For example, the proposal unit generates an optimal treatment plan using AI based on the collected opinions and proposes it to the patient. In this way, by proposing an optimal treatment plan based on the collected opinions, it is possible to provide an optimal treatment approach for the patient. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the collected opinions into AI, which then generates an optimal treatment plan and proposes it to the patient.

[0073] The proposal unit can provide a treatment plan to the patient. The proposal unit, for example, provides an optimal treatment plan for the patient. For example, the proposal unit proposes an optimal treatment plan for the patient based on the opinions of multiple medical professionals. The proposal unit can also provide a treatment plan using AI. For example, the proposal unit generates an optimal treatment plan using AI based on collected opinions and proposes it to the patient. By providing an optimal treatment plan for the patient, it is possible to provide a treatment approach tailored to the patient's needs. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input collected opinions into AI, which then generates an optimal treatment plan and proposes it to the patient.

[0074] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient's emotions. For example, the collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can delay data collection until the patient is relaxed. Alternatively, if the patient is relaxed, the collection unit can immediately start data collection. Alternatively, if the patient is feeling anxious, the collection unit can gradually collect data to reduce the patient's burden. This reduces the patient's burden by adjusting the timing of data collection based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the patient's emotion data into an AI, which can analyze the emotion and adjust the timing of data collection.

[0075] The collection unit can analyze the patient's past treatment history and select the optimal data collection method. The collection unit, for example, analyzes the patient's past treatment history and selects the optimal data collection method. For example, the collection unit selects an effective data collection method based on the effects of treatments the patient has received in the past. The collection unit can also adjust the frequency of data collection based on the patient's past treatment history. The collection unit can also analyze the patient's past treatment history and preferentially collect data related to specific treatments. In this way, the optimal data collection method can be selected by analyzing the patient's past treatment history. Some or all of the above-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 analyzes the data and selects the optimal data collection method.

[0076] The collection unit can perform filtering based on the patient's current living situation and areas of interest when collecting data. For example, the collection unit can perform filtering based on the patient's current living situation and areas of interest when collecting data. For example, the collection unit can take the patient's current living situation into consideration and collect only relevant data. The collection unit can also prioritize collecting necessary data based on the patient's areas of interest. The collection unit can also filter unnecessary data based on the patient's living situation and areas of interest. In this way, filtering based on the patient's current living situation and areas of interest can collect only relevant data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the patient's living situation and areas of interest into AI, which can analyze the data and perform filtering.

[0077] The collection unit can select the optimal collection means depending on the patient's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the patient's input method (voice, text, image, etc.) when collecting data. For example, if the patient prefers voice input, the collection unit collects data by voice. Also, if the patient prefers text input, the collection unit can collect data by text. Also, if the patient prefers image input, the collection unit can collect data by image. In this way, by selecting the optimal collection means depending on the patient's input method, data can be collected efficiently. 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 input data into AI, which then selects the optimal collection means.

[0078] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, the collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can prioritize collecting stress-related data. Also, if the patient is relaxed, the collection unit can prioritize collecting general health data. Also, if the patient is feeling anxious, the collection unit can prioritize collecting anxiety-related data. In this way, by determining the priority of data to be collected based on the patient's emotions, more important data can be collected preferentially. 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-mentioned processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the patient's emotion data into an AI, which can analyze the emotions and determine the priority of the data.

[0079] The collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the patient's geographical location information when collecting data. For example, the collection unit collects region-specific health data based on the patient's current location. The collection unit can also collect data on related medical facilities based on the patient's geographical location information. The collection unit can also prioritize collecting data related to regional health risks by taking into account the patient's geographical location information. In this way, region-specific health data can be collected by preferentially collecting highly relevant data by taking into account the patient's geographical location information. Some or all of the above-described processing by 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 geographical location information into AI, which can analyze the data and prioritize collecting highly relevant data.

[0080] The collection unit can analyze the patient's social media activity and collect related data when collecting data. For example, the collection unit can analyze the patient's social media activity and collect related data when collecting data. For example, the collection unit can analyze the patient's social media posts and collect related health data. The collection unit can also collect data related to health risks based on the patient's social media activity. The collection unit can also collect related health data by referring to the activities of the patient's friends on social media. In this way, related health data can be collected by analyzing the patient's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the patient's social media activity into AI, which can analyze the data and collect related data.

[0081] The collection unit can customize the collection method by reflecting the patient's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the patient's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on the patient's past feedback. The collection unit can also change the type of data to be collected by reflecting the patient's past feedback. The collection unit can also adjust the frequency of data collection by taking the patient's past feedback into consideration. In this way, the collection method can be customized by reflecting the patient's past feedback, enabling more appropriate data collection. 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 data of the patient's past feedback into AI, which can analyze the data and customize the collection method.

[0082] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, the analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, if the patient is feeling stressed, the analysis unit can provide simple, highly visible analysis results. If the patient is relaxed, the analysis unit can also provide detailed analysis results. If the patient is feeling anxious, the analysis unit can also provide analysis results that give a sense of security. By adjusting the presentation method of the analysis based on the patient's emotions, it is possible to provide analysis results that are easy for the patient to understand. 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 such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input patient emotion data into an AI, which analyzes the emotion and adjusts the presentation method of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, and the AI ​​can adjust the level of detail of the analysis based on the importance of the data.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a specific analysis algorithm to data related to a disease name. The analysis unit can also apply a different analysis algorithm to data related to symptoms. The analysis unit can also apply a different analysis algorithm to data related to lifestyle. In this way, by applying different analysis algorithms depending on the category of data, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI, which can then apply different analysis algorithms depending on the category of data.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the current analysis based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also increase the reliability of the analysis by using the patient's past analysis results. In this way, the accuracy of the current analysis can be improved by referring to the patient's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the patient's past analysis results into AI, and the AI ​​can analyze the data to improve the accuracy of the current analysis.

[0086] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, the analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, if the patient is feeling stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is feeling anxious, the analysis unit can provide an analysis result that provides a sense of security. By adjusting the length of the analysis based on the patient's emotions, it is possible to provide an analysis result of an appropriate length for the patient. 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 such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the patient's emotion data into an AI, which analyzes the emotion and adjusts the length of the analysis.

[0087] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. The analysis unit can also adjust the order of analysis depending on the time of data collection. In this way, by determining the analysis priority based on the time of data collection, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data collection into AI, which analyzes the data and determines the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which analyzes the data and adjusts the order of analysis.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise during analysis. For example, if the patient has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the patient does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the patient's level of expertise. By adjusting the use of technical terms in the analysis according to the patient's level of expertise, analysis results that are easy for the patient to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the patient's level of expertise into AI, which then analyzes the data and adjusts the use of technical terms.

[0090] The opinion collection unit can estimate the patient's emotions and adjust the criteria for opinion collection based on the estimated patient's emotions. For example, the opinion collection unit can estimate the patient's emotions and adjust the criteria for opinion collection based on the estimated patient's emotions. For example, if the patient is feeling stressed, the opinion collection unit can delay opinion collection until the patient is relaxed. Furthermore, if the patient is relaxed, the opinion collection unit can immediately start opinion collection. Furthermore, if the patient is feeling anxious, the opinion collection unit can gradually collect opinions to reduce the burden on the patient. This reduces the burden on the patient by adjusting the criteria for opinion collection based on 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 opinion collection unit can be performed using, for example, an AI, or without an AI. For example, the opinion collection unit can input patient emotion data into an AI, which analyzes the emotions and adjusts the criteria for opinion collection.

[0091] The opinion collection unit can improve the accuracy of opinion collection by taking into account the interrelationships between medical experts when collecting opinions. For example, the opinion collection unit can improve the accuracy of opinion collection by taking into account the interrelationships between medical experts when collecting opinions. For example, the opinion collection unit weights opinions by taking into account the interrelationships between medical experts. The opinion collection unit can also evaluate the reliability of opinions based on the interrelationships between medical experts. The opinion collection unit can also determine the priority of opinions to be collected by referring to the interrelationships between medical experts. In this way, the accuracy of collection can be improved by taking into account the interrelationships between medical experts. Some or all of the above-described processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input data on the interrelationships between medical experts into AI, which can analyze the data to improve the accuracy of collection.

[0092] The opinion collection unit can collect opinions while taking into account attribute information of medical professionals. For example, the opinion collection unit collects opinions while taking into account attribute information of medical professionals. For example, the opinion collection unit collects opinions based on the medical professionals' fields of expertise. The opinion collection unit can also collect opinions while taking into account the medical professionals' years of experience. The opinion collection unit can also collect opinions while referring to the medical professionals' past opinion provision history. In this way, by taking into account the attribute information of medical professionals, more reliable opinions can be collected. Some or all of the above-mentioned processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input attribute information of medical professionals into AI, and the AI ​​can analyze the data and collect opinions.

[0093] The opinion collecting unit can weight the collected opinions based on the frequency of submissions by medical experts when collecting opinions. For example, the opinion collecting unit weights the collected opinions based on the frequency of submissions by medical experts when collecting opinions. For example, the opinion collecting unit places more importance on opinions from medical experts who submit more frequently. The opinion collecting unit can also weight opinions from medical experts who submit less frequently. The opinion collecting unit can also weight the opinions based on the frequency of submission. In this way, by weighting the collected opinions based on the frequency of submissions by medical experts, more reliable opinions can be collected. Some or all of the above-mentioned processing in the opinion collecting unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collecting unit can input data on the frequency of submissions by medical experts into AI, and the AI ​​can analyze the data and weight the collected opinions.

[0094] The opinion collection unit can estimate the patient's emotions and adjust the order in which the opinion collection results are displayed based on the estimated patient's emotions. The opinion collection unit, for example, estimates the patient's emotions and adjusts the order in which the opinion collection results are displayed based on the estimated patient's emotions. For example, if the patient is feeling stressed, the opinion collection unit can prioritize displaying simple, highly visible opinions. Furthermore, if the patient is feeling relaxed, the opinion collection unit can prioritize displaying detailed opinions. Furthermore, if the patient is feeling anxious, the opinion collection unit can prioritize displaying reassuring opinions. By adjusting the order in which the opinion collection results are displayed based on the patient's emotions, the opinions can be displayed in an order that is easy for the patient to understand. The 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-described processing in the opinion collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the opinion collection unit can input patient emotion data into an AI, which analyzes the emotions and adjusts the order in which the opinion collection results are displayed.

[0095] The opinion collection unit can collect opinions taking into consideration the geographical distribution of medical experts. For example, the opinion collection unit collects opinions taking into consideration the geographical distribution of medical experts. For example, the opinion collection unit prioritizes collecting opinions from geographically close medical experts. The opinion collection unit can also collect opinions from geographically distant medical experts in a balanced manner. The opinion collection unit can also collect region-specific medical information taking into consideration the geographical distribution. In this way, region-specific medical information can be collected by taking into consideration the geographical distribution of medical experts. Some or all of the above-described processing in the opinion collection unit may be performed using, or without, AI, for example. For example, the opinion collection unit can input data on the geographical distribution of medical experts into AI, and the AI ​​can analyze the data and collect the information.

[0096] The opinion collection unit can improve the accuracy of the opinion collection by referring to related literature when collecting opinions. For example, the opinion collection unit can improve the accuracy of the opinion collection by referring to related literature when collecting opinions. For example, the opinion collection unit evaluates the reliability of the opinions of medical experts based on the related literature. The opinion collection unit can also improve the accuracy of the opinion collection by referring to the related literature. The opinion collection unit can also determine the priority of the opinions to be collected by referring to the related literature. In this way, the accuracy of the collection can be improved by referring to the related literature. Some or all of the above-mentioned processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input data of related literature into AI, which analyzes the data to improve the accuracy of the collection.

[0097] The opinion collection unit can collect opinions taking into consideration the market value of medical professionals. For example, the opinion collection unit collects opinions taking into consideration the market value of medical professionals. For example, the opinion collection unit prioritizes collecting opinions of medical professionals with high market value. The opinion collection unit can also collect opinions of medical professionals with low market value in a balanced manner. The opinion collection unit can also weight opinions taking into consideration market value. In this way, by taking into consideration the market value of medical professionals, more reliable opinions can be collected. Some or all of the above-mentioned processing in the opinion collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the opinion collection unit can input data on the market value of medical professionals into AI, and the AI ​​can analyze the data and collect opinions.

[0098] The suggestion unit can estimate the patient's emotions and adjust the way the suggestions are presented based on the estimated patient's emotions. For example, the suggestion unit can estimate the patient's emotions and adjust the way the suggestions are presented based on the estimated patient's emotions. For example, if the patient is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. If the patient is relaxed, the suggestion unit can also provide detailed suggestions. If the patient is feeling anxious, the suggestion unit can also provide suggestions that give a sense of security. By adjusting the way the suggestions are presented based on the patient's emotions, the suggestion unit can provide suggestions that are easy for the patient to understand. The 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 these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the patient's emotion data into an AI, which can analyze the emotion and adjust the way the suggestions are presented.

[0099] The proposal unit can adjust the level of detail of the proposal based on the importance of the treatment plan when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the treatment plan when making a proposal. For example, the proposal unit makes a detailed proposal for a treatment plan with a high level of importance. The proposal unit can also make a simplified proposal for a treatment plan with a low level of importance. The proposal unit can also adjust the depth of the proposal depending on the importance of the treatment plan. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the treatment plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the treatment plan to AI, which can analyze the data and adjust the level of detail of the proposal.

[0100] The suggestion unit can apply different suggestion algorithms depending on the category of the treatment plan when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the treatment plan when making a suggestion. For example, the suggestion unit can apply a specific suggestion algorithm to an internal medicine treatment plan. The suggestion unit can also apply a different suggestion algorithm to a surgical treatment plan. The suggestion unit can also apply a different suggestion algorithm to a psychiatric treatment plan. In this way, by applying different suggestion algorithms depending on the category of the treatment plan, more appropriate suggestions can be made. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the category of the treatment plan into AI, which can analyze the data and apply different suggestion algorithms.

[0101] The suggestion unit can improve the accuracy of the suggestion by referring to the patient's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the patient's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the current suggestion based on the patient's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the past suggestion results. The suggestion unit can also increase the reliability of the suggestion by using the patient's past suggestion results. In this way, the accuracy of the current suggestion can be improved by referring to the patient's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's past suggestion results into AI, which can analyze the data to improve the accuracy of the current suggestion.

[0102] The suggestion unit can estimate the patient's emotions and adjust the length of the suggestions based on the estimated patient's emotions. For example, the suggestion unit can estimate the patient's emotions and adjust the length of the suggestions based on the estimated patient's emotions. For example, if the patient is feeling stressed, the suggestion unit can provide short and to-the-point suggestions. If the patient is feeling relaxed, the suggestion unit can provide detailed suggestions. If the patient is feeling anxious, the suggestion unit can provide reassuring suggestions. By adjusting the length of the suggestions based on the patient's emotions, it is possible to provide suggestions of an appropriate length for the patient. 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 suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input patient emotion data into an AI, which can analyze the emotion and adjust the length of the suggestions.

[0103] The proposal unit can determine the priority of proposals based on the time of submission of the treatment plans when making proposals. The proposal unit, for example, determines the priority of proposals based on the time of submission of the treatment plans when making proposals. For example, the proposal unit preferentially proposes the most recent treatment plans. The proposal unit can also propose older treatment plans later. The proposal unit can also adjust the order of proposals depending on the time of submission of the treatment plans. In this way, by determining the priority of proposals based on the time of submission of the treatment plans, the most recent treatment plans can be preferentially proposed. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the time of submission of the treatment plans into AI, which can analyze the data and determine the priority of proposals.

[0104] The proposal unit can adjust the order of proposals based on the relevance of the treatment plans when proposing them. For example, the proposal unit can adjust the order of proposals based on the relevance of the treatment plans when proposing them. For example, the proposal unit prioritizes proposing highly relevant treatment plans. The proposal unit can also postpone proposing less relevant treatment plans. The proposal unit can also adjust the order of proposals according to the relevance of the treatment plans. In this way, by adjusting the order of proposals based on the relevance of the treatment plans, highly relevant treatment plans can be prioritized for proposal. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the relevance of the treatment plans into AI, which can analyze the data and adjust the order of proposals.

[0105] The suggestion unit can adjust the use of technical terminology in the proposal depending on the patient's level of expertise when making a proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal depending on the patient's level of expertise when making a proposal. For example, if the patient has technical expertise, the suggestion unit uses a lot of technical terminology. If the patient does not have technical expertise, the suggestion unit can also provide a proposal in simple language. The suggestion unit can also adjust the way the proposal is expressed depending on the patient's level of expertise. In this way, by adjusting the use of technical terminology in the proposal depending on the patient's level of expertise, it is possible to provide a proposal that is easy for the patient to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the patient's level of expertise into AI, which can analyze the data and adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, opinion collection unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as the patient's disease name, symptoms, past treatment history, and lifestyle using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies the treatment approach desired by the patient. The opinion collection unit can collect opinions from medical professionals using, for example, the control unit 46A of the smart device 14. The opinion collection unit can also be realized by the specific processing unit 290 of the data processing device 12. The suggestion unit can propose an optimal treatment plan to the patient using, for example, the control unit 46A of the smart device 14. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, opinion collection unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as the patient's illness name, symptoms, past treatment history, and lifestyle using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies the treatment approach desired by the patient. The opinion collection unit can collect opinions from medical professionals using, for example, the control unit 46A of the smart glasses 214. The opinion collection unit can also be realized by the specific processing unit 290 of the data processing device 12. The suggestion unit can propose an optimal treatment plan to the patient using, for example, the control unit 46A of the smart glasses 214. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, opinion collection unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data such as the patient's illness name, symptoms, past treatment history, and lifestyle using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies the treatment approach desired by the patient. The opinion collection unit can collect opinions from medical professionals using, for example, the control unit 46A of the headset-type terminal 314. The opinion collection unit can also be realized by the specific processing unit 290 of the data processing device 12. The suggestion unit can propose an optimal treatment plan to the patient using, for example, the control unit 46A of the headset-type terminal 314. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, opinion collection unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as the patient's illness name, symptoms, past treatment history, and lifestyle using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies the treatment approach desired by the patient. The opinion collection unit can collect opinions from medical professionals using, for example, the control unit 46A of the robot 414. The opinion collection unit can also be realized by the specific processing unit 290 of the data processing device 12. The suggestion unit can propose an optimal treatment plan to the patient using, for example, the control unit 46A of the robot 414. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The treatment approach optimization system can further include a genetic information analysis unit that collects and analyzes the patient's genetic information. For example, the genetic information analysis unit collects the patient's DNA sample and identifies genetic risk factors. The genetic information analysis unit can also predict how effective a specific treatment will be for the patient based on the genetic information. Furthermore, the genetic information analysis unit can also suggest the optimal drug therapy for the patient based on the genetic information. This is expected to realize personalized medicine that takes genetic information into account and provide more effective treatment approaches.

[0108] The treatment approach optimization system can further include an emotion adjustment unit that estimates the patient's emotion and adjusts the treatment approach based on the estimated emotion. For example, if the patient is feeling stressed, the emotion adjustment unit can preferentially suggest a treatment that has a relaxing effect. Also, if the patient is feeling anxious, the emotion adjustment unit can suggest a treatment that provides a sense of security. Furthermore, if the patient is relaxed, the emotion adjustment unit can suggest a proactive treatment approach. This is expected to provide a treatment approach that is appropriate for the patient's emotional state, thereby reducing the patient's psychological burden.

[0109] The treatment approach optimization system can further include an environmental analysis unit that collects and analyzes data on the patient's living environment. For example, the environmental analysis unit collects data on the patient's living environment and work environment and evaluates the impact of environmental factors on health. The environmental analysis unit can also make lifestyle improvement suggestions appropriate for the patient based on the environmental data. Furthermore, the environmental analysis unit can also propose a treatment approach that takes environmental factors into account. This is expected to provide a comprehensive treatment approach that takes the patient's living environment into account.

[0110] The treatment approach optimization system can further estimate the patient's emotions and adjust the timing of collecting opinions from medical professionals based on the estimated emotions. For example, if the patient is feeling stressed, the opinion collection unit can delay collecting opinions until the patient is relaxed. Alternatively, if the patient is relaxed, the opinion collection unit can immediately start collecting opinions. Furthermore, if the patient is feeling anxious, the opinion collection unit can collect opinions in stages to reduce the burden on the patient. This is expected to allow opinions to be collected according to the patient's emotional state, thereby reducing the psychological burden on the patient.

[0111] The treatment approach optimization system may further include a social network analysis unit that collects and analyzes social network data of the patient. For example, the social network analysis unit may collect data on relationships between the patient and family and friends and evaluate the influence of social support. The social network analysis unit may also propose a support plan appropriate for the patient based on the social network data. Furthermore, the social network analysis unit may also propose a treatment approach that takes social support into consideration. This is expected to provide a comprehensive treatment approach that takes the patient's social environment into consideration.

[0112] The treatment approach optimization system can further estimate the patient's emotions and adjust the treatment plan proposal method based on the estimated emotions. For example, if the patient is feeling stressed, the proposal unit can provide simple, highly visible proposals. If the patient is feeling relaxed, the proposal unit can also provide detailed proposals. Furthermore, if the patient is feeling anxious, the proposal unit can also provide reassuring proposals. This is expected to provide a proposal method that corresponds to the patient's emotional state and make proposals that are easy for the patient to understand.

[0113] The treatment approach optimization system can further include a motion analysis unit that collects and analyzes the patient's exercise data. For example, the motion analysis unit collects data on the patient's exercise habits and exercise volume, and evaluates the impact of exercise on health. The motion analysis unit can also propose an exercise plan suitable for the patient based on the exercise data. Furthermore, the motion analysis unit can propose a treatment approach that takes into account the patient's exercise habits. This is expected to provide a comprehensive treatment approach that takes into account the patient's exercise habits.

[0114] The treatment approach optimization system can further estimate the patient's emotions and prioritize treatment plans based on the estimated emotions. For example, if the patient is feeling stressed, the suggestion unit can suggest a treatment plan that prioritizes stress reduction. If the patient is relaxed, the suggestion unit can also suggest an aggressive treatment plan. Furthermore, if the patient is feeling anxious, the suggestion unit can also suggest a treatment plan that prioritizes anxiety reduction. This is expected to provide a treatment plan that corresponds to the patient's emotional state, thereby reducing the patient's psychological burden.

[0115] The treatment approach optimization system may further include a diet analysis unit that collects and analyzes the patient's dietary data. For example, the diet analysis unit may collect data on the patient's eating habits and nutritional intake, and evaluate the impact of diet on health. The diet analysis unit may also propose a meal plan appropriate for the patient based on the dietary data. Furthermore, the diet analysis unit may also propose a treatment approach that takes into account the patient's eating habits. This is expected to provide a comprehensive treatment approach that takes into account the patient's eating habits.

[0116] The treatment approach optimization system can further estimate the patient's emotions and adjust the feedback of the treatment plan based on the estimated emotions. For example, if the patient is feeling stressed, the suggestion unit can preferentially provide positive feedback. If the patient is feeling relaxed, the suggestion unit can also provide detailed feedback. Furthermore, if the patient is feeling anxious, the suggestion unit can also provide reassuring feedback. This is expected to provide feedback that is appropriate for the patient's emotional state and is easy for the patient to understand.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects data on the patient's illness, symptoms, past treatment history, and lifestyle. For example, illnesses include chronic and acute illnesses, and symptoms include pain, fever, fatigue, etc. Past treatment history includes the type, duration, and results of treatment, and lifestyle includes eating habits, exercise habits, sleep patterns, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the treatment approach desired by the patient. For example, based on the collected data, it considers the effectiveness and side effects of the patient's past treatments, current lifestyle habits, etc., to identify the optimal treatment approach. The analysis unit can also use AI to analyze the data using machine learning algorithms. Step 3: The opinion gathering unit collects the opinions of multiple medical professionals based on the treatment approach identified by the analysis unit. For example, opinions are collected from medical professionals in different fields, such as internists, surgeons, and psychiatrists, to gather their opinions in order to propose the optimal treatment plan. The opinion gathering unit can also use AI to collect opinions from medical professionals through questionnaire surveys and interviews. Step 4: The suggestion unit proposes a treatment plan based on the opinions collected by the opinion collection unit. For example, it proposes the optimal treatment plan for the patient based on the opinions of multiple medical professionals. The suggestion unit can also use AI to generate the optimal treatment plan based on the collected opinions and propose it to the patient.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data on patients' illnesses, symptoms, past medical history, and lifestyles; an analysis unit that analyzes the data collected by the collection unit and identifies a treatment approach desired by the patient; an opinion gathering unit that gathers opinions of a plurality of medical experts based on the treatment approach identified by the analysis unit; a proposal unit that proposes a treatment plan based on the opinions collected by the opinion collection unit. A system characterized by:

2. The collecting unit Collect data on patients' past medical history and lifestyle habits The system of claim 1 .

3. The analysis unit Identify the treatment approach the patient desires based on the data collected The system of claim 1 .

4. The opinion collection unit Gathering opinions from medical professionals across different fields, including physicians, surgeons, and psychiatrists The system of claim 1 .

5. The proposal unit Propose a treatment plan based on the collected opinions The system of claim 1 .

6. The proposal unit Providing patients with treatment plans The system of claim 1 .

7. The collecting unit Estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions The system of claim 1 .

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

9. The collecting unit As data is collected, filtering is performed based on the patient's current living situation and areas of interest. The system of claim 1 .

10. The collecting unit When collecting data, choose the most appropriate collection method depending on the patient's input method. The system of claim 1 .

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