System

Abstract

An object of a system according to an embodiment is to analyze questions and symptoms related to the health of a user and provide highly reliable medical information.SOLUTION: A system includes a question analysis unit and an information providing unit. The question analysis unit analyzes questions and symptoms related to the health of the user. The information providing part provides highly reliable medical information on the basis of the result analyzed by the question analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the problem that medical information available on the Internet lacks reliability, making it difficult for anxious users to find appropriate solutions.

[0005] The system according to the embodiment aims to analyze health-related questions and symptoms of a user and provide highly reliable medical information. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit and an information providing unit. The question analysis unit analyzes a user's health-related questions and symptoms. The information providing unit provides reliable medical information based on the results of the analysis by the question analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's health-related questions and symptoms and provide highly reliable medical information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 medical information search platform according to an embodiment of the present invention is a system that enables people with health concerns to find appropriate solutions when searching for information online without being misled by anxiety-inducing information. This system uses a generative AI to analyze users' health-related questions and symptoms and provide reliable medical information. This allows the medical information search platform to alleviate users' health concerns and provide appropriate medical information.

[0029] A medical information search platform according to an embodiment includes a question analysis unit and an information provision unit. The question analysis unit analyzes a user's health-related questions and symptoms. For example, the generation AI analyzes a question entered by a user and searches for related medical information. The question analysis unit can also perform a more personalized analysis by referring to the user's past search history and health data. For example, the generation AI identifies frequently searched keywords and topics based on the user's past search history and performs a personalized analysis. The question analysis unit can also perform an analysis taking into account the user's lifestyle and environmental factors. For example, the generation AI collects the user's lifestyle data and incorporates it into the analysis. The information provision unit provides reliable medical information based on the results of the analysis by the question analysis unit. For example, the generation AI provides appropriate advice to the user based on medically recognized sources and expert opinions. The information provision unit can also include the latest research results and clinical trial data. For example, the generation AI provides summaries of recently published medical papers. This allows the medical information search platform according to an embodiment to reduce health-related concerns and obtain appropriate medical information. For example, if a user is suffering from a headache, the generative AI can provide reliable information about the causes of headaches and how to treat them, easing anxiety.

[0030] The question analysis unit can perform more personalized analysis by referencing the user's past search history and health data. For example, the generation AI analyzes the user's past search history to identify frequently searched keywords and topics. For example, if the user has previously searched for keywords such as "headache," "stress," and "lack of sleep," the question analysis unit can perform personalized analysis based on this information. The question analysis unit can also refer to the user's health data and incorporate it into the analysis. For example, the generation AI can perform personalized analysis based on data from the user's fitness device and medical records. This allows for more personalized analysis by referencing the user's past search history and health data.

[0031] The question analysis unit can also take into account the user's lifestyle and environmental factors when conducting analysis. For example, the generation AI collects the user's lifestyle data and reflects this in the analysis. For example, if the user is a smoker, smoking may be considered the cause of headaches. The question analysis unit can also take into account the user's environmental factors when conducting analysis. For example, the generation AI can identify factors that affect health based on the user's living environment and work environment. This allows for more accurate analysis by taking into account the user's lifestyle and environmental factors.

[0032] The question analysis unit can add a function to share the analysis results of questions and symptoms with the user's family and friends, thereby strengthening the support network. For example, the question analysis unit provides a function that allows the generation AI to share the analysis results with the user's family and friends. For example, if the user agrees, the analysis results are sent to the family by email. In addition, by sharing the analysis results, the question analysis unit can receive support from family and friends. For example, if the user has health concerns, family and friends can provide appropriate advice. In this way, sharing the analysis results with family and friends can strengthen the support network.

[0033] The information provision unit can include the latest research results and clinical trial data in the medical information. For example, the information provision unit can include the latest research results in the medical information provided by the generative AI. For example, it can provide summaries of recently published medical papers. The information provision unit can also provide reliable medical information based on clinical trial data. For example, the generative AI can analyze the results of clinical trials and provide appropriate advice to the user. This makes it possible to provide reliable medical information by including the latest research results and clinical trial data.

[0034] The information providing unit can provide medical information taking into account the user's individual health condition and risk factors. For example, the generating AI in the information providing unit takes into account the user's individual health condition and provides appropriate medical information. For example, if the user has high blood pressure, it provides information according to that risk. The information providing unit can also provide medical information based on the user's risk factors. For example, the generating AI takes into account the user's genetic factors and lifestyle habits and provides appropriate advice. This makes it possible to provide more appropriate medical information by taking into account the user's individual health condition and risk factors.

[0035] The information provision unit can adapt the medical information to different languages ​​and cultures, making it available to international users. For example, the information provision unit can make the medical information provided by the generation AI multilingual, making it available to users of different languages. For example, it can translate into English, Spanish, Chinese, etc. The information provision unit can also provide appropriate information taking cultural background into consideration. For example, the generation AI can provide appropriate advice based on the user's cultural background. This makes it possible to provide medical information that is available to international users by adapting to different languages ​​and cultures.

[0036] The information provision unit can perform customization based on the user's preferences and beliefs. For example, the generation AI of the information provision unit takes into account the user's preferences and beliefs and provides customized medical information. For example, if the user prefers natural remedies, that information will be provided preferentially. The information provision unit can also provide information based on the user's beliefs. For example, the generation AI takes into account the user's religious beliefs and provides appropriate advice. This makes it possible to customize information based on the user's preferences and beliefs.

[0037] The question analysis unit can identify the cause of the user's anxiety and propose specific measures to address it. For example, the generation AI in the question analysis unit analyzes the cause of the user's anxiety and proposes specific measures. For example, if the user is worried about headaches, it will provide the cause of the headache and measures to address it. The question analysis unit can also identify the cause of the user's anxiety and provide appropriate advice. For example, the generation AI will consider the user's lifestyle habits and environmental factors and propose specific measures. In this way, the anxiety can be reduced by identifying the cause of the user's anxiety and proposing specific measures.

[0038] The question analysis unit can provide music or a meditation guide to reduce the user's anxiety. For example, the generation AI can provide relaxing music to reduce the user's anxiety. For example, classical music or natural sounds can be played. The question analysis unit can also provide a meditation guide to the user. For example, the generation AI can teach the user how to meditate or relaxation techniques. In this way, the user's anxiety can be reduced by providing music or a meditation guide.

[0039] The information providing unit can refer to the user's past medical history and introduce the most suitable medical institution. For example, the generation AI can refer to the user's past medical history and introduce the most suitable medical institution. For example, it can introduce an appropriate specialist based on data from medical institutions the user has visited in the past. The information providing unit can also select an appropriate medical institution based on the user's medical history. For example, the generation AI can analyze the user's medical records and prescription history and suggest the most suitable medical institution. This makes it possible to introduce the most suitable medical institution by referring to the user's past medical history.

[0040] The information provision unit can introduce medical institutions taking into consideration the user's specific needs and conditions. For example, the generation AI can introduce medical institutions taking into consideration the user's specific needs and conditions. For example, if the user requests a specialist, the generation AI can introduce medical institutions that meet those conditions. The information provision unit can also select medical institutions taking into consideration the user's consultation hours and ease of access. For example, the generation AI can suggest medical institutions that have the user's desired consultation hours. This makes it possible to introduce more appropriate medical institutions by taking into consideration the user's specific needs and conditions.

[0041] The information providing unit can introduce not only the nearest medical institution but also highly rated medical institutions based on the user's location information. For example, the generation AI of the information providing unit introduces the nearest medical institution based on the user's location information. For example, it displays the hospital or clinic closest to the user's current location. The information providing unit can also introduce appropriate medical institutions based on highly rated medical institutions. For example, the generation AI suggests highly rated medical institutions based on user reviews and expert evaluations. This allows the user to select a more appropriate medical institution by introducing not only the nearest medical institution but also highly rated medical institutions based on the user's location information.

[0042] The information provision unit can introduce medical institutions taking into consideration the user's insurance coverage and costs. For example, the generation AI of the information provision unit introduces appropriate medical institutions taking into consideration the user's insurance coverage. For example, it preferentially displays medical institutions that the user's insurance applies to. The information provision unit can also select medical institutions based on the user's costs. For example, the generation AI considers the user's medical expenses and drug costs and suggests appropriate medical institutions. This makes it possible to introduce more appropriate medical institutions by taking into consideration the user's insurance coverage and costs.

[0043] The question analysis unit can analyze a user's health data over the long term and predict changes in their health condition. For example, the question analysis unit constructs a system in which a generation AI analyzes a user's health data over the long term and predicts changes in their health condition. For example, it predicts future health risks based on the user's blood pressure and weight data. The question analysis unit can also monitor the user's health condition and provide appropriate advice. For example, the generation AI analyzes the user's health data and provides advice according to changes in their health condition. This enables more appropriate health management by analyzing the user's health data over the long term and predicting changes in their health condition.

[0044] The question analysis unit can provide health management support by taking into account the user's lifestyle habits and dietary content. For example, the generation AI in the question analysis unit collects the user's lifestyle data and reflects it in health management support. For example, it provides appropriate advice based on the user's exercise habits and sleep patterns. The question analysis unit can also provide health management support by taking into account the user's dietary content. For example, the generation AI analyzes the user's food records and suggests nutritionally balanced meals. This makes it possible to provide more appropriate health management support by taking into account the user's lifestyle habits and dietary content.

[0045] The question analysis unit can share the user's health data with family and medical professionals to provide comprehensive support. For example, the question analysis unit builds a system in which the generation AI shares the user's health data with family and medical professionals. For example, if the user agrees, the health data is sent to family and doctors. The question analysis unit can also provide comprehensive support based on the user's health data. For example, the generation AI analyzes the user's health condition and works with family and medical professionals to provide appropriate advice. This makes it possible to share the user's health data with family and medical professionals to provide comprehensive support.

[0046] The question analysis unit can work in conjunction with the user's fitness device or smartwatch to support health management. For example, the question analysis unit builds a system in which the generation AI works in conjunction with the user's fitness device or smartwatch to collect health data. For example, it analyzes the user's exercise volume and heart rate in real time. The question analysis unit can also provide appropriate advice based on the user's health data. For example, the generation AI analyzes the user's exercise habits and heart rate fluctuations to support health management. This makes it possible to support more appropriate health management by working in conjunction with the user's fitness device or smartwatch.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The question analysis unit can not only refer to the user's past search history and health data, but also perform analysis based on the user's hobbies and interests. For example, the generative AI can analyze keywords related to the user's past searches for hobbies and interests and provide personalized medical information based on this. The question analysis unit can also make health advice more appealing by taking the user's hobbies and interests into consideration. For example, if the user is interested in sports, it can suggest ways to manage their health through sports. Furthermore, the question analysis unit can monitor the user's hobbies and interests over the long term and provide advice to maintain motivation for health. For example, it can set health goals based on the user's interests and support them in achieving them.

[0049] The question analysis unit can not only consider the user's lifestyle and environmental factors, but also reflect the user's social connections and support network in its analysis. For example, the generative AI can analyze the user's family structure and friendships and provide appropriate medical information based on this. The question analysis unit can also make health advice more effective by taking the user's social connections into account. For example, if the user manages their health with their family, the question analysis unit can suggest a health plan that the entire family can work on. Furthermore, the question analysis unit can monitor the user's social connections over the long term and analyze the impact of social support on health. For example, it can analyze the strength of the user's social support network and predict health risks based on this.

[0050] The question analysis unit not only analyzes the user's health-related questions and symptoms, but also uses data obtained from the user's fitness device or smartwatch for analysis. For example, the generative AI can analyze the user's heart rate and exercise volume and provide appropriate medical information based on this. The question analysis unit can also analyze data obtained from the user's fitness device or smartwatch to provide more personalized health advice. For example, it can analyze the user's exercise habits and sleep patterns and suggest health management methods based on this. Furthermore, the question analysis unit can monitor data obtained from the user's fitness device or smartwatch over the long term and predict changes in health status. For example, it can analyze fluctuations in the user's heart rate and exercise volume to predict future health risks.

[0051] The information provision unit can not only include the latest research results and clinical trial data in medical information, but also provide customized information that takes into account the user's individual health condition and risk factors. For example, the generative AI can analyze the user's health data and provide the latest research results and clinical trial data based on this. The information provision unit can also provide more appropriate medical information by taking into account the user's individual health condition and risk factors. For example, if the user is at high risk for a particular disease, it can provide the latest research results and clinical trial data that correspond to that risk. Furthermore, the information provision unit can monitor the user's health condition and risk factors over the long term and provide advice based on the latest research results and clinical trial data. For example, it can analyze the user's health data and suggest preventive measures for future risks.

[0052] The information provision unit not only provides medical information taking into account the user's individual health condition and risk factors, but also provides customized information based on the user's lifestyle and values. For example, the generative AI analyzes the user's lifestyle and values ​​and provides appropriate medical information based on this. The information provision unit can also provide more appropriate medical information by taking the user's lifestyle and values ​​into consideration. For example, if the user prefers natural therapies, it can provide medical information that matches those values. Furthermore, the information provision unit can monitor the user's lifestyle and values ​​over the long term and provide customized medical information based on this. For example, it can analyze changes in the user's lifestyle and provide medical information accordingly.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The question analysis unit analyzes the user's health-related questions and symptoms. For example, the generation AI analyzes the question entered by the user and searches for relevant medical information. The question analysis unit can also refer to the user's past search history and health data to perform a more personalized analysis. Furthermore, the question analysis unit can also take into account the user's lifestyle and environmental factors in its analysis. Step 2: The information provider provides reliable medical information based on the results of the analysis by the question analyzer. For example, the generative AI provides appropriate advice to the user based on medically recognized sources and expert opinions. The information provider can also include the latest research results and clinical trial data.

[0055] (Example 2) A medical information search platform according to an embodiment of the present invention is a system that enables people with health concerns to find appropriate solutions when searching for information online without being misled by anxiety-inducing information. This system uses a generative AI to analyze users' health-related questions and symptoms and provide reliable medical information. This allows the medical information search platform to alleviate users' health concerns and provide appropriate medical information.

[0056] A medical information search platform according to an embodiment includes a question analysis unit and an information provision unit. The question analysis unit analyzes a user's health-related questions and symptoms. For example, the generation AI analyzes a question entered by a user and searches for related medical information. The question analysis unit can also perform a more personalized analysis by referring to the user's past search history and health data. For example, the generation AI identifies frequently searched keywords and topics based on the user's past search history and performs a personalized analysis. The question analysis unit can also perform an analysis taking into account the user's lifestyle and environmental factors. For example, the generation AI collects the user's lifestyle data and incorporates it into the analysis. The information provision unit provides reliable medical information based on the results of the analysis by the question analysis unit. For example, the generation AI provides appropriate advice to the user based on medically recognized sources and expert opinions. The information provision unit can also include the latest research results and clinical trial data. For example, the generation AI provides summaries of recently published medical papers. This allows the medical information search platform according to an embodiment to reduce health-related concerns and obtain appropriate medical information. For example, if a user is suffering from a headache, the generative AI can provide reliable information about the causes of headaches and how to treat them, easing anxiety.

[0057] The question analysis unit can perform more personalized analysis by referencing the user's past search history and health data. For example, the generation AI analyzes the user's past search history to identify frequently searched keywords and topics. For example, if the user has previously searched for keywords such as "headache," "stress," and "lack of sleep," the question analysis unit can perform personalized analysis based on this information. The question analysis unit can also refer to the user's health data and incorporate it into the analysis. For example, the generation AI can perform personalized analysis based on data from the user's fitness device and medical records. This allows for more personalized analysis by referencing the user's past search history and health data.

[0058] The question analysis unit can also take into account the user's lifestyle and environmental factors when conducting analysis. For example, the generation AI collects the user's lifestyle data and reflects this in the analysis. For example, if the user is a smoker, smoking may be considered the cause of headaches. The question analysis unit can also take into account the user's environmental factors when conducting analysis. For example, the generation AI can identify factors that affect health based on the user's living environment and work environment. This allows for more accurate analysis by taking into account the user's lifestyle and environmental factors.

[0059] The question analysis unit uses the emotion estimation function to analyze the emotional state of the user at the time of questioning and can provide appropriate information according to the emotion. For example, the generation AI in the question analysis unit analyzes the emotional state of the user at the time of input and provides information according to the emotion. For example, if the user is feeling anxious, it will prioritize providing information that gives a sense of security. The question analysis unit can also monitor the user's emotional state in real time and provide appropriate information. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to analyze the user's emotional state and provide appropriate information according to the emotion.

[0060] The question analysis unit can analyze the user's voice input and perform analysis taking into account the emotion and tone obtained from the voice. For example, the generation AI in the question analysis unit analyzes the user's voice input and estimates the user's emotional state from the tone and speed of the voice. For example, if the user is panicking, it can provide advice on how to deal with the situation calmly. The question analysis unit can also monitor the user's emotional state in real time based on the voice input. For example, the generation AI analyzes the user's voice and calculates an emotion score. This makes it possible to analyze the user's voice input while taking into account emotion and tone.

[0061] The question analysis unit can add a function to share the analysis results of questions and symptoms with the user's family and friends, thereby strengthening the support network. For example, the question analysis unit provides a function that allows the generation AI to share the analysis results with the user's family and friends. For example, if the user agrees, the analysis results are sent to the family by email. In addition, by sharing the analysis results, the question analysis unit can receive support from family and friends. For example, if the user has health concerns, family and friends can provide appropriate advice. In this way, sharing the analysis results with family and friends can strengthen the support network.

[0062] The question analysis unit uses the emotion estimation function to analyze the emotions of users when they input questions in real time and make suggestions that elicit positive emotions. For example, the question analysis unit analyzes the emotions of users when the generation AI inputs questions in real time. For example, if the user is feeling anxious, it displays a message that gives a sense of security. The question analysis unit can also analyze the user's emotional state and make suggestions that elicit positive emotions. For example, the generation AI can share words of encouragement or successful experiences with the user. This makes it possible to analyze the user's emotions in real time and make suggestions that elicit positive emotions.

[0063] The information provision unit can include the latest research results and clinical trial data in the medical information. For example, the information provision unit can include the latest research results in the medical information provided by the generative AI. For example, it can provide summaries of recently published medical papers. The information provision unit can also provide reliable medical information based on clinical trial data. For example, the generative AI can analyze the results of clinical trials and provide appropriate advice to the user. This makes it possible to provide reliable medical information by including the latest research results and clinical trial data.

[0064] The information providing unit can provide medical information taking into account the user's individual health condition and risk factors. For example, the generating AI in the information providing unit takes into account the user's individual health condition and provides appropriate medical information. For example, if the user has high blood pressure, it provides information according to that risk. The information providing unit can also provide medical information based on the user's risk factors. For example, the generating AI takes into account the user's genetic factors and lifestyle habits and provides appropriate advice. This makes it possible to provide more appropriate medical information by taking into account the user's individual health condition and risk factors.

[0065] The information provision unit can use the emotion estimation function to evaluate the emotional impact of the information the user receives and provide positive information. For example, the information provision unit can evaluate the emotional impact of medical information provided by the generation AI and provide positive information preferentially. For example, if the user is feeling anxious, it can provide information that gives a sense of security. The information provision unit can also monitor the user's emotional state in real time and provide appropriate information. For example, the generation AI can analyze the user's facial expressions and voice and calculate an emotion score. This allows the generation of summaries that capture emotional nuances, so that emotional elements can also be reflected in the evaluation.

[0066] The information provision unit can adapt the medical information to different languages ​​and cultures, making it available to international users. For example, the information provision unit can make the medical information provided by the generation AI multilingual, making it available to users of different languages. For example, it can translate into English, Spanish, Chinese, etc. The information provision unit can also provide appropriate information taking cultural background into consideration. For example, the generation AI can provide appropriate advice based on the user's cultural background. This makes it possible to provide medical information that is available to international users by adapting to different languages ​​and cultures.

[0067] The information provision unit can perform customization based on the user's preferences and beliefs. For example, the generation AI of the information provision unit takes into account the user's preferences and beliefs and provides customized medical information. For example, if the user prefers natural remedies, that information will be provided preferentially. The information provision unit can also provide information based on the user's beliefs. For example, the generation AI takes into account the user's religious beliefs and provides appropriate advice. This makes it possible to customize information based on the user's preferences and beliefs.

[0068] The information provision unit can use the emotion estimation function to monitor the user's emotional response to the information they receive in real time and adjust how the information is provided. For example, the information provision unit can monitor the user's emotional response to medical information provided by the generation AI in real time and adjust how the information is provided. For example, if the user is feeling anxious, the tone of the information can be softened. The information provision unit can also analyze the user's emotional state and provide appropriate information. For example, the generation AI can analyze the user's facial expressions and voice and calculate an emotion score. This allows the information provision unit to monitor the user's emotional response in real time and adjust how the information is provided, making it possible to provide more appropriate information.

[0069] The question analysis unit can identify the cause of the user's anxiety and propose specific measures to address it. For example, the generation AI in the question analysis unit analyzes the cause of the user's anxiety and proposes specific measures. For example, if the user is worried about headaches, it will provide the cause of the headache and measures to address it. The question analysis unit can also identify the cause of the user's anxiety and provide appropriate advice. For example, the generation AI will consider the user's lifestyle habits and environmental factors and propose specific measures. In this way, the anxiety can be reduced by identifying the cause of the user's anxiety and proposing specific measures.

[0070] The question analysis unit can use the emotion estimation function to monitor the user's anxiety level in real time and provide appropriate support. For example, the question analysis unit uses the emotion estimation function to build a system in which the generation AI monitors the user's anxiety level in real time. For example, it analyzes the user's facial expressions and voice to calculate the anxiety level. The question analysis unit can also analyze the user's anxiety level and provide appropriate support. For example, the generation AI can advise the user on relaxation techniques and stress management. In this way, the user's anxiety level can be monitored in real time and appropriate support can be provided to reduce anxiety.

[0071] The question analysis unit can provide music or a meditation guide to reduce the user's anxiety. For example, the generation AI can provide relaxing music to reduce the user's anxiety. For example, classical music or natural sounds can be played. The question analysis unit can also provide a meditation guide to the user. For example, the generation AI can teach the user how to meditate or relaxation techniques. In this way, the user's anxiety can be reduced by providing music or a meditation guide.

[0072] The question analysis unit can use the emotion estimation function to provide counseling or mental health support to reduce the user's anxiety. For example, the question analysis unit uses the emotion estimation function to build a system in which the generation AI provides counseling to reduce the user's anxiety. For example, the question analysis unit supports the user in booking online counseling sessions. The question analysis unit can also provide the user with mental health support. For example, the generation AI can introduce the user to a mental health app or expert support. This can reduce the user's anxiety by providing counseling or mental health support.

[0073] The information providing unit can refer to the user's past medical history and introduce the most suitable medical institution. For example, the generation AI can refer to the user's past medical history and introduce the most suitable medical institution. For example, it can introduce an appropriate specialist based on data from medical institutions the user has visited in the past. The information providing unit can also select an appropriate medical institution based on the user's medical history. For example, the generation AI can analyze the user's medical records and prescription history and suggest the most suitable medical institution. This makes it possible to introduce the most suitable medical institution by referring to the user's past medical history.

[0074] The information provision unit can introduce medical institutions taking into consideration the user's specific needs and conditions. For example, the generation AI can introduce medical institutions taking into consideration the user's specific needs and conditions. For example, if the user requests a specialist, the generation AI can introduce medical institutions that meet those conditions. The information provision unit can also select medical institutions taking into consideration the user's consultation hours and ease of access. For example, the generation AI can suggest medical institutions that have the user's desired consultation hours. This makes it possible to introduce more appropriate medical institutions by taking into consideration the user's specific needs and conditions.

[0075] The information provision unit uses the emotion estimation function to analyze the emotional state of the user when selecting a medical institution and can provide information that gives a sense of security. For example, the generation AI in the information provision unit uses the emotion estimation function to analyze the emotional state of the user when selecting a medical institution. For example, if the user is feeling anxious, it provides information that gives a sense of security. The information provision unit can also monitor the user's emotional state in real time and provide appropriate information. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This allows the user to analyze the emotional state and provide information that gives a sense of security, allowing them to select a more appropriate medical institution.

[0076] The information providing unit can introduce not only the nearest medical institution but also highly rated medical institutions based on the user's location information. For example, the generation AI of the information providing unit introduces the nearest medical institution based on the user's location information. For example, it displays the hospital or clinic closest to the user's current location. The information providing unit can also introduce appropriate medical institutions based on highly rated medical institutions. For example, the generation AI suggests highly rated medical institutions based on user reviews and expert evaluations. This allows the user to select a more appropriate medical institution by introducing not only the nearest medical institution but also highly rated medical institutions based on the user's location information.

[0077] The information provision unit can introduce medical institutions taking into consideration the user's insurance coverage and costs. For example, the generation AI of the information provision unit introduces appropriate medical institutions taking into consideration the user's insurance coverage. For example, it preferentially displays medical institutions that the user's insurance applies to. The information provision unit can also select medical institutions based on the user's costs. For example, the generation AI considers the user's medical expenses and drug costs and suggests appropriate medical institutions. This makes it possible to introduce more appropriate medical institutions by taking into consideration the user's insurance coverage and costs.

[0078] The information provision unit uses the emotion estimation function to monitor the user's emotional reactions in real time when selecting a medical institution, and can support the optimal selection. For example, the information provision unit uses the emotion estimation function to build a system in which the generation AI uses the emotion estimation function to monitor the user's emotional reactions in real time when selecting a medical institution. For example, if the user is feeling anxious, it provides information that gives a sense of security. The information provision unit can also analyze the user's emotional state and provide appropriate information. For example, the generation AI analyzes the user's facial expressions and voice and calculates an emotion score. This allows the user's emotional reactions to be monitored in real time and supports the optimal selection, allowing the user to select a more appropriate medical institution.

[0079] The question analysis unit can analyze a user's health data over the long term and predict changes in their health condition. For example, the question analysis unit constructs a system in which a generation AI analyzes a user's health data over the long term and predicts changes in their health condition. For example, it predicts future health risks based on the user's blood pressure and weight data. The question analysis unit can also monitor the user's health condition and provide appropriate advice. For example, the generation AI analyzes the user's health data and provides advice according to changes in their health condition. This enables more appropriate health management by analyzing the user's health data over the long term and predicting changes in their health condition.

[0080] The question analysis unit can provide health management support by taking into account the user's lifestyle habits and dietary content. For example, the generation AI in the question analysis unit collects the user's lifestyle data and reflects it in health management support. For example, it provides appropriate advice based on the user's exercise habits and sleep patterns. The question analysis unit can also provide health management support by taking into account the user's dietary content. For example, the generation AI analyzes the user's food records and suggests nutritionally balanced meals. This makes it possible to provide more appropriate health management support by taking into account the user's lifestyle habits and dietary content.

[0081] The question analysis unit uses the emotion estimation function to monitor the user's emotional response to health management in real time and provide optimal support. For example, the question analysis unit uses the emotion estimation function to build a system in which the generation AI monitors the user's emotional response to health management in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The question analysis unit can also analyze the user's emotional state and provide appropriate support. For example, the generation AI provides the user with advice on relaxation techniques and stress management. This allows the system to monitor the user's emotional response to health management in real time and provide optimal support, enabling more appropriate health management.

[0082] The question analysis unit can share the user's health data with family and medical professionals to provide comprehensive support. For example, the question analysis unit builds a system in which the generation AI shares the user's health data with family and medical professionals. For example, if the user agrees, the health data is sent to family and doctors. The question analysis unit can also provide comprehensive support based on the user's health data. For example, the generation AI analyzes the user's health condition and works with family and medical professionals to provide appropriate advice. This makes it possible to share the user's health data with family and medical professionals to provide comprehensive support.

[0083] The question analysis unit can work in conjunction with the user's fitness device or smartwatch to support health management. For example, the question analysis unit builds a system in which the generation AI works in conjunction with the user's fitness device or smartwatch to collect health data. For example, it analyzes the user's exercise volume and heart rate in real time. The question analysis unit can also provide appropriate advice based on the user's health data. For example, the generation AI analyzes the user's exercise habits and heart rate fluctuations to support health management. This makes it possible to support more appropriate health management by working in conjunction with the user's fitness device or smartwatch.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The question analysis unit not only analyzes the user's health-related questions and symptoms, but also takes into account the user's psychological state. For example, the generative AI can estimate the user's stress level and psychological burden from the user's input and provide appropriate medical information based on this. The question analysis unit can also provide advice on stress management and mental health by analyzing the user's psychological state. For example, if the user is feeling stressed, it can suggest relaxation techniques and exercises to reduce stress. Furthermore, the question analysis unit can monitor the user's psychological state over the long term and predict changes in their psychological health. For example, it can analyze fluctuations in the user's stress level and predict future mental health risks.

[0086] The question analysis unit can not only refer to the user's past search history and health data, but also perform analysis based on the user's hobbies and interests. For example, the generative AI can analyze keywords related to the user's past searches for hobbies and interests and provide personalized medical information based on this. The question analysis unit can also make health advice more appealing by taking the user's hobbies and interests into consideration. For example, if the user is interested in sports, it can suggest ways to manage their health through sports. Furthermore, the question analysis unit can monitor the user's hobbies and interests over the long term and provide advice to maintain motivation for health. For example, it can set health goals based on the user's interests and support them in achieving them.

[0087] The question analysis unit can not only consider the user's lifestyle and environmental factors, but also reflect the user's social connections and support network in its analysis. For example, the generative AI can analyze the user's family structure and friendships and provide appropriate medical information based on this. The question analysis unit can also make health advice more effective by taking the user's social connections into account. For example, if the user manages their health with their family, the question analysis unit can suggest a health plan that the entire family can work on. Furthermore, the question analysis unit can monitor the user's social connections over the long term and analyze the impact of social support on health. For example, it can analyze the strength of the user's social support network and predict health risks based on this.

[0088] The question analysis unit uses its emotion estimation function to not only analyze the user's emotional state at the time of questioning, but also track the user's emotional changes in real time and provide information at the appropriate time. For example, if the user's emotional state changes suddenly, the generative AI can immediately provide information that provides reassurance. The question analysis unit can also provide advice based on the user's emotions by analyzing the user's emotional changes. For example, if the user suddenly feels anxious, it can suggest relaxation techniques or stress management methods. Furthermore, the question analysis unit can monitor the user's emotional changes over the long term and analyze emotional patterns. For example, it can analyze the user's emotional fluctuations and predict future mental health risks based on the emotional patterns.

[0089] The question analysis unit not only analyzes the user's voice input, but also estimates the user's level of fatigue and concentration from the tone and speed of the voice and can provide appropriate information based on this. For example, if the generation AI senses that the user sounds tired, it can advise them to take a rest. The question analysis unit can also provide advice to improve concentration by analyzing the user's tone and speed of the voice. For example, if the user is lacking concentration, it can suggest a short break or a way to refresh themselves. Furthermore, the question analysis unit can monitor the user's tone and speed of the voice over the long term and analyze changes in fatigue and concentration. For example, it can analyze the user's voice data to identify patterns of fatigue and concentration and provide health management advice based on this.

[0090] The question analysis unit not only analyzes the user's health-related questions and symptoms, but also uses data obtained from the user's fitness device or smartwatch for analysis. For example, the generative AI can analyze the user's heart rate and exercise volume and provide appropriate medical information based on this. The question analysis unit can also analyze data obtained from the user's fitness device or smartwatch to provide more personalized health advice. For example, it can analyze the user's exercise habits and sleep patterns and suggest health management methods based on this. Furthermore, the question analysis unit can monitor data obtained from the user's fitness device or smartwatch over the long term and predict changes in health status. For example, it can analyze fluctuations in the user's heart rate and exercise volume to predict future health risks.

[0091] The question analysis unit uses its emotion estimation function to not only analyze the emotions of users when they enter questions in real time, but also provide feedback that corresponds to the user's emotions. For example, if the generation AI is feeling anxious, it can display a message that provides reassurance. The question analysis unit can also provide feedback that corresponds to the user's emotions by analyzing the user's emotional state. For example, if the user is feeling down, it can display words of encouragement or positive messages. Furthermore, the question analysis unit can monitor the user's emotional state over the long term and provide feedback that corresponds to changes in emotions. For example, it can analyze the user's emotional fluctuations and provide appropriate feedback based on emotional patterns.

[0092] The information provision unit can not only include the latest research results and clinical trial data in medical information, but also provide customized information that takes into account the user's individual health condition and risk factors. For example, the generative AI can analyze the user's health data and provide the latest research results and clinical trial data based on this. The information provision unit can also provide more appropriate medical information by taking into account the user's individual health condition and risk factors. For example, if the user is at high risk for a particular disease, it can provide the latest research results and clinical trial data that correspond to that risk. Furthermore, the information provision unit can monitor the user's health condition and risk factors over the long term and provide advice based on the latest research results and clinical trial data. For example, it can analyze the user's health data and suggest preventive measures for future risks.

[0093] The information provision unit not only provides medical information taking into account the user's individual health condition and risk factors, but also provides customized information based on the user's lifestyle and values. For example, the generative AI analyzes the user's lifestyle and values ​​and provides appropriate medical information based on this. The information provision unit can also provide more appropriate medical information by taking the user's lifestyle and values ​​into consideration. For example, if the user prefers natural therapies, it can provide medical information that matches those values. Furthermore, the information provision unit can monitor the user's lifestyle and values ​​over the long term and provide customized medical information based on this. For example, it can analyze changes in the user's lifestyle and provide medical information accordingly.

[0094] The information provision unit can use the emotion estimation function to not only evaluate the emotional impact of the information the user receives, but also adjust the way information is provided according to the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide information in a tone and expression that corresponds to the emotion. The information provision unit can also monitor the user's emotional state in real time and adjust the way information is provided. For example, if the user is feeling anxious, it can provide information in a softer tone. Furthermore, the information provision unit can monitor the user's emotional state over the long term and adjust the way information is provided according to changes in emotion. For example, it can analyze fluctuations in the user's emotions and optimize the way information is provided based on emotional patterns.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The question analysis unit analyzes the user's health-related questions and symptoms. For example, the generation AI analyzes the question entered by the user and searches for relevant medical information. The question analysis unit can also refer to the user's past search history and health data to perform a more personalized analysis. Furthermore, the question analysis unit can also take into account the user's lifestyle and environmental factors in its analysis. Step 2: The information provider provides reliable medical information based on the results of the analysis by the question analyzer. For example, the generative AI provides appropriate advice to the user based on medically recognized sources and expert opinions. The information provider can also include the latest research results and clinical trial data.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] 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. [Explanation of symbols]

[0164] 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 question analysis unit that analyzes questions and symptoms related to the user's health; an information providing unit that provides highly reliable medical information based on the results of the analysis by the question analyzing unit. A system characterized by:

2. The question analysis unit Analyze the emotional state of the user when asking a question and provide appropriate information according to the emotion.

2. The system of claim 1.

3. The information providing unit Include the latest research findings and clinical trial data in the medical information 2. The system of claim 1.

4. The question analysis unit Identify the cause of the user's anxiety and propose specific measures to address it 2. The system of claim 1.

5. The information providing unit Analyzing the emotional state of the user when selecting a medical institution and providing information that gives a sense of security 2. The system of claim 1.

Images