System

Abstract

To provide a system for providing proper proposals and support with respect to physiological symptoms.SOLUTION: A system includes a collection part, a proposal part, and a provision part. The collection part collects information related to physiological symptoms. The proposal part makes proposals based on the information collected by the collection part. The provision part provides a user with contents proposed by the proposal 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques are insufficient in providing appropriate suggestions and support for physiological symptoms, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate suggestions and support for physiological symptoms. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, and a provision unit. The collection unit collects information on physiological symptoms. The suggestion unit makes suggestions based on the information collected by the collection unit. The provision unit provides the content suggested by the suggestion unit to a user. Effect of the Invention

[0007] The system according to the embodiment can provide appropriate suggestions and support for physiological symptoms. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The menstrual symptom support system according to the embodiment of the present invention is a system for supporting women who suffer from menstrual symptoms every month. This system has a function of collecting information about menstrual symptoms, making suggestions, and providing the information to the user. Specifically, first, as a support for menstrual symptoms, the system collects the voices of people with similar symptoms and provides them to the user. This allows the user to feel that they are not alone and to feel relieved. In addition, a chat room is proposed, providing a place where the user can interact with other people. Next, as suggestions for improving menstrual symptoms, suggestions are made for diet, exercise, supplements, music, aroma, and hospitals. These suggestions are made based on past utilization results, effect measurement reports, and evaluations by research papers. This allows the user to obtain highly reliable information. For example, if a specific diet has the effect of reducing menstrual pain, the system suggests that diet. Also, if a specific exercise has the effect of improving menstrual symptoms, the system suggests that exercise. This system aims to provide a specific method for the user to deal with their menstrual symptoms and improve the quality of the user's life. As a result, the menstrual symptom support system can provide specific measures for the user's menstrual symptoms and improve the quality of the user's life.

[0029] The physiological symptom support system according to the embodiment includes a collection unit, a suggestion unit, and a provision unit. The collection unit collects information on physiological symptoms. The information on physiological symptoms includes, for example, headache, abdominal pain, fatigue, etc., but is not limited to such examples. The collection unit can collect information by, for example, a questionnaire, a sensor, user input, etc. The suggestion unit makes a suggestion based on the information collected by the collection unit. The suggestion includes, for example, improving lifestyle habits, visiting a medical institution, etc., but is not limited to such examples. The suggestion unit uses a generation AI to analyze the collected information and generate an optimal suggestion. For example, the generation AI inputs physiological symptom data of a user and generates a suggestion based on past data and research papers. The provision unit provides the content proposed by the suggestion unit to the user. The provision method includes, for example, notification, email, in-app message, etc., but is not limited to such examples. The provision unit uses the generation AI to provide information to the user in an optimal manner. For example, the generation AI selects an optimal provision method based on the user's usage history and current situation. As a result, the physiological symptom support system according to the embodiment can efficiently collect, suggest, and provide information on physiological symptoms.

[0030] The collection unit collects information on physiological symptoms. Information on physiological symptoms includes, but is not limited to, for example, headache, stomachache, and fatigue. The collection unit can collect information by, for example, a questionnaire, a sensor, user input, and other methods. Specifically, the questionnaire is provided in the form of an online form or a question within an app, and the user can enter details of their symptoms, their frequency, intensity, and the circumstances when they occurred. The sensor is a sensor built into a wearable device or a smartphone, and automatically collects physiological data such as heart rate, body temperature, activity level, and sleep pattern. This makes it possible to detect even minute changes that the user is not aware of. The user input is a method in which the user manually records symptoms through an app or web portal, and is particularly suitable for recording acute symptoms and reactions to specific triggers in detail. The collection unit can combine these methods to collect data from multiple angles and comprehensively grasp the user's physiological symptoms. Furthermore, the collected data is securely stored in a cloud server and updated in real time, so that analysis based on the latest information is always possible. This enables the collection unit to efficiently and accurately collect information regarding the user's physiological symptoms, and smoothly provide the data to the suggestion unit, which is the next step.

[0031] The suggestion unit makes suggestions based on the information collected by the collection unit. Suggestions include, but are not limited to, for example, improving lifestyle habits and visiting a medical institution. The suggestion unit uses the generation AI to analyze the collected information and generate optimal suggestions. Specifically, the generation AI inputs the user's physiological symptom data and generates suggestions based on past data and research papers. The generation AI first analyzes the user's symptom data and identifies symptom patterns and trends. Next, it refers to past databases and the latest research papers to compare case studies and treatments of other users with similar symptoms. For example, for a user who frequently suffers from headaches, it can suggest lifestyle improvements such as stress management, improving diet, and ensuring adequate sleep. In addition, if a particular symptom is severe or no improvement is seen, it is recommended that the user see a specialist. The generation AI customizes these suggestions to suit the individual situation of the user and provides the most effective approach. Furthermore, the generation AI can continuously improve the content of the suggestions by collecting user feedback and evaluating the effectiveness of the suggestions. As a result, the suggestion unit can provide optimal measures for the user's physiological symptoms and support the user's health management.

[0032] The providing unit provides the content proposed by the suggesting unit to the user. The providing method includes, for example, notification, email, in-app message, etc., but is not limited to such examples. The providing unit uses the generating AI to provide information to the user in an optimal manner. Specifically, the generating AI selects the optimal providing method based on the user's usage history and current situation. For example, for a user who uses a smartphone on a daily basis, the suggestion is provided through an in-app message or a push notification, and for a user who frequently checks email, detailed suggested content is sent by email. In addition, for suggestions with high urgency or important notifications, SMS or voice calls are used in combination to reliably transmit information. Furthermore, the providing unit can continuously improve by collecting user feedback and evaluating the effectiveness of the providing method. For example, whether the user accepted the suggestion, the reaction to the suggestion, and the execution status are monitored, and reflected in the next suggestion. As a result, the providing unit can provide information to the user at the optimal timing and method, and promote the execution of the suggestion. As a result, the physiological symptom support system according to the embodiment can efficiently collect, propose, and provide information on physiological symptoms, and can comprehensively support the user's health management.

[0033] The suggestion unit can suggest a chat room. For example, the suggestion unit suggests a chat room as a place where the user can interact with other people. The chat room includes, for example, text chat, voice chat, video chat, and the like, but is not limited to such examples. The suggestion unit can use the generation AI to suggest an optimal chat room according to the user's situation and needs. For example, the generation AI suggests a chat room where people with similar symptoms gather based on the user's input data. The suggestion unit can also suggest how to use the chat room and rules. This allows the user to interact with other people and share information.

[0034] The evaluation unit can perform evaluation using effectiveness measurement reports and research papers. The evaluation unit performs evaluation using, for example, past utilization results, effectiveness measurement reports, and research papers. The effectiveness measurement reports include, for example, but are not limited to, questionnaire results and experimental data. The research papers include, for example, but are not limited to, peer-reviewed papers and academic conference presentations. The evaluation unit uses the generation AI to analyze the collected data and perform a highly reliable evaluation. For example, the generation AI inputs questionnaire results and experimental data and performs evaluation using statistical analysis and machine learning algorithms. The evaluation unit can also evaluate the effectiveness of the proposal based on research papers. This allows the user to obtain highly reliable information.

[0035] The suggestion unit can make suggestions regarding food, exercise, supplements, music, aromas, and hospitals. The suggestion unit makes suggestions regarding food, exercise, supplements, music, aromas, and hospitals, for example, according to the physiological symptoms of the user. The suggestions regarding food include, for example, nutritional balance, calorie restriction, and the like, but are not limited to such examples. The suggestions regarding exercise include, for example, aerobic exercise, muscle training, and the like, but are not limited to such examples. The suggestions regarding supplements include, for example, vitamins, minerals, and the like, but are not limited to such examples. The suggestions regarding music include, for example, relaxing music, music for improving concentration, and the like, but are not limited to such examples. The suggestions regarding aromas include, for example, lavender, peppermint, and the like, but are not limited to such examples. The suggestions regarding hospitals include, for example, introductions to specialist departments, reservation methods, and the like, but are not limited to such examples. The suggestion unit uses the generation AI to analyze the physiological symptom data of the user and generate optimal suggestions. For example, the generation AI makes suggestions based on the user's input data, referring to past data and research papers. This allows the user to receive a variety of improvement suggestions.

[0036] The collection unit can collect opinions of people with similar symptoms. The collection unit collects opinions of people with similar symptoms, for example, by using an online forum or a questionnaire survey. Methods for collecting opinions include, but are not limited to, text input, voice input, video input, and the like. The collection unit uses a generation AI to analyze the collected opinions and provide them to the user. For example, the generation AI classifies the collected opinions and extracts the voices of people with similar symptoms. In addition, the collection unit can provide information that gives the user a sense of security based on the collected opinions. This allows the user to feel that he or she is not alone and to feel a sense of security.

[0037] The collection unit can analyze the user's past physiological symptom data and select an information collection method. The collection unit selects the most effective information collection method based on, for example, physiological symptom data recorded by the user in the past. Past physiological symptom data includes, for example, diaries, medical records, etc., but is not limited to such examples. The collection unit uses the generation AI to analyze the past data and select the optimal information collection method. For example, the generation AI inputs the user's past data, predicts the timing when a specific symptom will appear, and collects information according to that timing. The collection unit can also suggest the optimal information collection method for a specific symptom based on the past data. This allows the collection unit to select the optimal information collection method based on the past data.

[0038] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting information on physiological symptoms. For example, when the user inputs the current living situation, the collection unit filters information on related physiological symptoms based on the information. The living situation includes, for example, occupation, home environment, lifestyle, etc., but is not limited to such examples. The collection unit uses the generation AI to filter information based on the user's living situation and field of interest. For example, the generation AI preferentially collects related information based on the user's input data. The collection unit can also preferentially collect related information based on the user's field of interest (e.g., diet and exercise). This allows the collection unit to provide information according to the user's living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's input data to the generation AI and have the generation AI perform filtering.

[0039] The collection unit can collect information on physiological symptoms with priority based on the geographical location information of the user when collecting information on the physiological symptoms. For example, when the user is in a specific area, the collection unit collects information on physiological symptoms related to the area with priority. The geographical location information includes, but is not limited to, for example, GPS data, address information, and the like. The collection unit uses the generation AI to analyze the geographical location information of the user and collects information related to the area with priority. For example, the generation AI inputs the user's location data and collects information related to the area. In addition, when the user is traveling, the collection unit can also collect information on physiological symptoms related to the climate and environment of the travel destination with priority. This allows the collection unit to collect optimal information based on the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location data to the generation AI and cause the generation AI to collect information with high relevance.

[0040] The collection unit can analyze the user's social media activity and collect related information when collecting information on physiological symptoms. The collection unit, for example, collects information on related physiological symptoms based on information shared by the user on social media. Social media activities include, for example, but are not limited to, the content of posts, the number of likes, and the number of followers. The collection unit uses the generation AI to analyze the user's social media activity and collect related information. For example, the generation AI takes the user's posting data as input and preferentially collects information related to topics of interest. The collection unit can also collect information on related physiological symptoms based on information on accounts and groups the user follows. This allows the collection unit to collect optimal information based on the user's social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related information.

[0041] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the physiological symptom at the time of suggestion. For example, the suggestion unit makes a detailed suggestion for a physiological symptom with a high level of importance. The importance of a physiological symptom includes, for example, the frequency and the impact of the symptom, but is not limited to such examples. The suggestion unit uses a generation AI to evaluate the importance of the physiological symptom and make an optimal suggestion. For example, the generation AI inputs symptom data of the user and calculates an importance score. In addition, the suggestion unit can also make a concise suggestion for a physiological symptom with a low level of importance. This allows the suggestion unit to make an optimal suggestion according to the importance of the physiological symptom. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit inputs symptom data of the user to the generation AI and causes the generation AI to execute a process of adjusting the level of detail of the suggestion.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of the physiological symptom at the time of suggestion. For example, the suggestion unit applies an algorithm that takes nutritional balance into consideration for suggestions regarding meals. The categories of physiological symptoms include, but are not limited to, for example, the digestive system, the respiratory system, and the nervous system. The suggestion unit uses the generation AI to apply an optimal suggestion algorithm depending on the category of the physiological symptom. For example, the generation AI inputs the symptom data of the user and applies an algorithm depending on the category. In addition, the suggestion unit can also apply an algorithm that takes the physical strength level of the user into consideration for suggestions regarding exercise. This allows the suggestion unit to make an optimal suggestion depending on the category of the physiological symptom. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit inputs the symptom data of the user to the generation AI and causes the generation AI to execute a process of applying the suggestion algorithm.

[0043] The suggestion unit can determine the priority of the suggestion based on the time of occurrence of the physiological symptoms at the time of suggestion. For example, the suggestion unit prioritizes the suggestion during the period when the physiological symptoms frequently occur. The time of occurrence of the physiological symptoms includes, but is not limited to, for example, a season, a time period, a specific event, etc. The suggestion unit uses the generation AI to analyze the time of occurrence of the physiological symptoms and makes an optimal suggestion. For example, the generation AI inputs the symptom data of the user and determines the priority of the suggestion based on the time of occurrence. In addition, the suggestion unit can lower the priority of the suggestion during the period when the physiological symptoms rarely occur. This allows the suggestion unit to make an optimal suggestion according to the time of occurrence of the physiological symptoms. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the symptom data of the user to the generation AI and cause the generation AI to execute a process of determining the priority of the suggestion.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of the physiological symptoms at the time of suggestion. For example, the suggestion unit prioritizes suggestions for highly relevant physiological symptoms. The relevance of physiological symptoms includes, but is not limited to, common causes and common symptoms. The suggestion unit uses a generation AI to evaluate the relevance of physiological symptoms and makes suggestions in an optimal order. For example, the generation AI inputs symptom data of the user and calculates a relevance score. The suggestion unit can also postpone the order of suggestions for less relevant physiological symptoms. This allows the suggestion unit to make suggestions in an optimal order according to the relevance of the physiological symptoms. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit inputs symptom data of the user to the generation AI and causes the generation AI to execute a process of adjusting the order of suggestions.

[0045] The providing unit can select the optimal information providing method by referring to the user's past usage history when providing information. The providing unit selects the optimal method based on, for example, the information providing method used by the user in the past. The past usage history includes, for example, an access log, a frequency of use, and the like, but is not limited to such examples. The providing unit uses the generation AI to analyze the user's past usage history and selects the optimal information providing method. For example, the generation AI inputs the user's usage history data and proposes the most effective information providing method. The providing unit can also propose the optimal information providing method to the user based on the past usage history. This allows the providing unit to provide information in the optimal method based on the past usage history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's usage history data to the generation AI and cause the generation AI to execute a process of selecting an information providing method.

[0046] The providing unit can customize the provided content based on the user's current living situation when providing information. For example, when the user inputs the current living situation, the providing unit customizes the provided content based on the information. The living situation includes, for example, occupation, home environment, and lifestyle, but is not limited to such examples. The providing unit uses the generating AI to analyze the living situation of the user and provide optimal information. For example, the generating AI provides information according to the living situation based on the user's input data. The providing unit can also provide optimal information according to the user's living situation (for example, how busy the user is at work or the home situation). This allows the providing unit to provide optimal information according to the user's living situation. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data to the generating AI and cause the generating AI to execute a process of customizing the provided content.

[0047] The providing unit can select a method of providing information based on the geographical location information of the user when providing information. For example, when the user is in a specific area, the providing unit provides information related to the area with priority. The geographical location information includes, but is not limited to, for example, GPS data, address information, and the like. The providing unit uses the generating AI to analyze the geographical location information of the user and selects an optimal information providing method. For example, the generating AI inputs the user's location data and provides information related to the area. In addition, when the user is traveling, the providing unit can also provide information related to the climate and environment of the travel destination with priority. This allows the providing unit to provide optimal information based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location data to the generating AI and cause the generating AI to execute a process of selecting an information providing method.

[0048] The providing unit can adjust the content of the information provided by analyzing the social media activity of the user when providing the information. The providing unit provides related information based on, for example, information shared by the user on social media. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The providing unit uses the generating AI to analyze the social media activity of the user and provides optimal information. For example, the generating AI inputs the user's post data and provides information related to topics of interest preferentially. The providing unit can also provide related information based on information on accounts and groups the user follows. This allows the providing unit to provide optimal information based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to execute a process of adjusting the content of the provision.

[0049] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data at the time of evaluation. The evaluation unit selects an optimal evaluation algorithm based on, for example, past evaluation data. Past evaluation data includes, for example, questionnaire results, experimental data, etc., but is not limited to such examples. The evaluation unit analyzes past data using a generation AI and optimizes the evaluation algorithm. For example, the generation AI takes past evaluation data as input, extracts a specific pattern, and optimizes the evaluation algorithm. The evaluation unit can also improve the accuracy of the evaluation algorithm based on the past data. This allows the evaluation unit to apply an optimal evaluation algorithm based on the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input past evaluation data to the generation AI and cause the generation AI to execute a process of optimizing the evaluation algorithm.

[0050] The evaluation unit can apply different evaluation methods for each category of physiological symptoms at the time of evaluation. For example, the evaluation unit applies an evaluation method that takes nutritional balance into consideration for evaluation regarding meals. The categories of physiological symptoms include, for example, the digestive system, the respiratory system, and the nervous system, but are not limited to such examples. The evaluation unit uses the generation AI to apply an optimal evaluation method according to the category of physiological symptoms. For example, the generation AI inputs the user's symptom data and applies an evaluation method according to the category. In addition, the evaluation unit can also apply an evaluation method that takes the user's physical strength level into consideration for evaluation regarding exercise. This allows the evaluation unit to apply an optimal evaluation method according to the category of physiological symptoms. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs the user's symptom data to the generation AI and causes the generation AI to execute a process of applying the evaluation method.

[0051] The evaluation unit can weight the evaluation based on the occurrence time of the physiological symptoms at the time of evaluation. For example, the evaluation unit increases the weighting of the evaluation in the period when the physiological symptoms frequently occur. The occurrence time of the physiological symptoms includes, but is not limited to, for example, a season, a time period, a specific event, etc. The evaluation unit uses the generation AI to analyze the occurrence time of the physiological symptoms and performs the evaluation with optimal weighting. For example, the generation AI inputs the symptom data of the user and determines the weighting of the evaluation based on the occurrence time. In addition, the evaluation unit can also lower the weighting of the evaluation in the period when the physiological symptoms rarely occur. This allows the evaluation unit to perform the evaluation with optimal weighting according to the occurrence time of the physiological symptoms. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the symptom data of the user to the generation AI and cause the generation AI to execute the processing of weighting the evaluation.

[0052] The evaluation unit can improve the accuracy of the evaluation by referring to related literature on physiological symptoms at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to, for example, the latest research papers on physiological symptoms. The related literature includes, for example, academic papers, specialized books, etc., but is not limited to such examples. The evaluation unit uses the generation AI to analyze the related literature and improve the accuracy of the evaluation. For example, the generation AI takes related literature data as an input and optimizes the evaluation algorithm. The evaluation unit can also improve the accuracy of the evaluation algorithm based on past literature. This allows the evaluation unit to improve the accuracy of the evaluation based on the related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input related literature data to the generation AI and cause the generation AI to execute processing to improve the accuracy of the evaluation.

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

[0054] The collection unit can refer to the user's past medical records when collecting information about the user's physiological symptoms. For example, information about diagnoses and prescribed medications that the user has received in the past can be collected and associated with the user's current physiological symptoms. The collection unit can also collect the user's family history and genetic information to evaluate the impact of genetic factors on the physiological symptoms. Furthermore, the collection unit can collect the user's lifestyle habits (e.g., diet, exercise, sleep patterns) and analyze the impact of these factors on the physiological symptoms. This allows the collection unit to collect comprehensive information about the user's physiological symptoms and make more accurate suggestions.

[0055] The suggestion unit can make suggestions suited to a specific season or climate according to the user's physiological symptoms. For example, in cold seasons, it can suggest foods and exercises that will warm the body. In humid seasons, it can also suggest aromas and supplements to combat humidity. Furthermore, in seasons with a lot of pollen, it can suggest foods and exercises to combat allergies. This allows the suggestion unit to make optimal suggestions according to the season and climate.

[0056] When collecting information on the user's physiological symptoms, the collection unit can filter the information based on the user's occupation and living environment. For example, for a user who does a lot of desk work, it is possible to preferentially collect information on methods for improving physiological symptoms caused by sitting for long periods of time. Also, for a user who is raising children, it is possible to preferentially collect information on physiological symptoms related to child-rearing stress. Furthermore, for a user who often works night shifts, it is possible to preferentially collect information on nighttime physiological symptoms. This allows the collection unit to collect optimal information according to the user's occupation and living environment.

[0057] When collecting information on the user's physiological symptoms, the collection unit can filter the information based on the user's hobbies and interests. For example, for a user who likes music, information on music therapy can be preferentially collected. Also, for a user who likes sports, information on exercise therapy can be preferentially collected. Furthermore, for a user who likes cooking, information on diet therapy can be preferentially collected. This allows the collection unit to collect optimal information according to the user's hobbies and interests.

[0058] When collecting information on the user's physiological symptoms, the collection unit can filter the information based on the user's geographical location information. For example, for a user living in an urban area, information on physiological symptoms related to urban life can be preferentially collected. Also, for a user living in a rural area, information on physiological symptoms related to farm work can be preferentially collected. Furthermore, information on physiological symptoms related to the climate and environment specific to a particular region can be preferentially collected. This allows the collection unit to collect optimal information according to the user's geographical location information.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The collection unit collects information on physiological symptoms. Information on physiological symptoms includes, but is not limited to, headache, stomachache, fatigue, etc. The collection unit can collect information by, for example, a questionnaire, a sensor, user input, or the like. Step 2: The suggestion unit makes suggestions based on the information collected by the collection unit. Suggestions include, but are not limited to, for example, improving lifestyle habits and visiting a medical institution. The suggestion unit uses a generation AI to analyze the collected information and generate optimal suggestions. For example, the generation AI inputs the user's physiological symptom data and generates suggestions based on past data and research papers. Step 3: The providing unit provides the content proposed by the suggesting unit to the user. The providing method includes, but is not limited to, for example, notification, email, in-app message, etc. The providing unit uses the generating AI to provide the information to the user in the most optimal way. For example, the generating AI selects the most optimal providing method based on the user's usage history and current situation.

[0061] (Example 2) The menstrual symptom support system according to the embodiment of the present invention is a system for supporting women who suffer from menstrual symptoms every month. This system has a function of collecting information about menstrual symptoms, making suggestions, and providing the information to the user. Specifically, first, as a support for menstrual symptoms, the system collects the voices of people with similar symptoms and provides them to the user. This allows the user to feel that they are not alone and to feel relieved. In addition, a chat room is proposed, providing a place where the user can interact with other people. Next, as suggestions for improving menstrual symptoms, suggestions are made for diet, exercise, supplements, music, aroma, and hospitals. These suggestions are made based on past utilization results, effect measurement reports, and evaluations by research papers. This allows the user to obtain highly reliable information. For example, if a specific diet has the effect of reducing menstrual pain, the system suggests that diet. Also, if a specific exercise has the effect of improving menstrual symptoms, the system suggests that exercise. This system aims to provide a specific method for the user to deal with their menstrual symptoms and improve the quality of the user's life. As a result, the menstrual symptom support system can provide specific measures for the user's menstrual symptoms and improve the quality of the user's life.

[0062] The physiological symptom support system according to the embodiment includes a collection unit, a suggestion unit, and a provision unit. The collection unit collects information on physiological symptoms. The information on physiological symptoms includes, for example, headache, abdominal pain, fatigue, etc., but is not limited to such examples. The collection unit can collect information by, for example, a questionnaire, a sensor, user input, etc. The suggestion unit makes a suggestion based on the information collected by the collection unit. The suggestion includes, for example, improving lifestyle habits, visiting a medical institution, etc., but is not limited to such examples. The suggestion unit uses a generation AI to analyze the collected information and generate an optimal suggestion. For example, the generation AI inputs physiological symptom data of a user and generates a suggestion based on past data and research papers. The provision unit provides the content proposed by the suggestion unit to the user. The provision method includes, for example, notification, email, in-app message, etc., but is not limited to such examples. The provision unit uses the generation AI to provide information to the user in an optimal manner. For example, the generation AI selects an optimal provision method based on the user's usage history and current situation. As a result, the physiological symptom support system according to the embodiment can efficiently collect, suggest, and provide information on physiological symptoms.

[0063] The collection unit collects information on physiological symptoms. Information on physiological symptoms includes, but is not limited to, for example, headache, stomachache, and fatigue. The collection unit can collect information by, for example, a questionnaire, a sensor, user input, and other methods. Specifically, the questionnaire is provided in the form of an online form or a question within an app, and the user can enter details of their symptoms, their frequency, intensity, and the circumstances when they occurred. The sensor is a sensor built into a wearable device or a smartphone, and automatically collects physiological data such as heart rate, body temperature, activity level, and sleep pattern. This makes it possible to detect even minute changes that the user is not aware of. The user input is a method in which the user manually records symptoms through an app or web portal, and is particularly suitable for recording acute symptoms and reactions to specific triggers in detail. The collection unit can combine these methods to collect data from multiple angles and comprehensively grasp the user's physiological symptoms. Furthermore, the collected data is securely stored in a cloud server and updated in real time, so that analysis based on the latest information is always possible. This enables the collection unit to efficiently and accurately collect information regarding the user's physiological symptoms, and smoothly provide the data to the suggestion unit, which is the next step.

[0064] The suggestion unit makes suggestions based on the information collected by the collection unit. Suggestions include, but are not limited to, for example, improving lifestyle habits and visiting a medical institution. The suggestion unit uses the generation AI to analyze the collected information and generate optimal suggestions. Specifically, the generation AI inputs the user's physiological symptom data and generates suggestions based on past data and research papers. The generation AI first analyzes the user's symptom data and identifies symptom patterns and trends. Next, it refers to past databases and the latest research papers to compare case studies and treatments of other users with similar symptoms. For example, for a user who frequently suffers from headaches, it can suggest lifestyle improvements such as stress management, improving diet, and ensuring adequate sleep. In addition, if a particular symptom is severe or no improvement is seen, it is recommended that the user see a specialist. The generation AI customizes these suggestions to suit the individual situation of the user and provides the most effective approach. Furthermore, the generation AI can continuously improve the content of the suggestions by collecting user feedback and evaluating the effectiveness of the suggestions. As a result, the suggestion unit can provide optimal measures for the user's physiological symptoms and support the user's health management.

[0065] The providing unit provides the content proposed by the suggesting unit to the user. The providing method includes, for example, notification, email, in-app message, etc., but is not limited to such examples. The providing unit uses the generating AI to provide information to the user in an optimal manner. Specifically, the generating AI selects the optimal providing method based on the user's usage history and current situation. For example, for a user who uses a smartphone on a daily basis, the suggestion is provided through an in-app message or a push notification, and for a user who frequently checks email, detailed suggested content is sent by email. In addition, for suggestions with high urgency or important notifications, SMS or voice calls are used in combination to reliably transmit information. Furthermore, the providing unit can continuously improve by collecting user feedback and evaluating the effectiveness of the providing method. For example, whether the user accepted the suggestion, the reaction to the suggestion, and the execution status are monitored, and reflected in the next suggestion. As a result, the providing unit can provide information to the user at the optimal timing and method, and promote the execution of the suggestion. As a result, the physiological symptom support system according to the embodiment can efficiently collect, propose, and provide information on physiological symptoms, and can comprehensively support the user's health management.

[0066] The suggestion unit can suggest a chat room. For example, the suggestion unit suggests a chat room as a place where the user can interact with other people. The chat room includes, for example, text chat, voice chat, video chat, and the like, but is not limited to such examples. The suggestion unit can use the generation AI to suggest an optimal chat room according to the user's situation and needs. For example, the generation AI suggests a chat room where people with similar symptoms gather based on the user's input data. The suggestion unit can also suggest how to use the chat room and rules. This allows the user to interact with other people and share information.

[0067] The evaluation unit can perform evaluation using effectiveness measurement reports and research papers. The evaluation unit performs evaluation using, for example, past utilization results, effectiveness measurement reports, and research papers. The effectiveness measurement reports include, for example, but are not limited to, questionnaire results and experimental data. The research papers include, for example, but are not limited to, peer-reviewed papers and academic conference presentations. The evaluation unit uses the generation AI to analyze the collected data and perform a highly reliable evaluation. For example, the generation AI inputs questionnaire results and experimental data and performs evaluation using statistical analysis and machine learning algorithms. The evaluation unit can also evaluate the effectiveness of the proposal based on research papers. This allows the user to obtain highly reliable information.

[0068] The suggestion unit can make suggestions regarding food, exercise, supplements, music, aromas, and hospitals. The suggestion unit makes suggestions regarding food, exercise, supplements, music, aromas, and hospitals, for example, according to the physiological symptoms of the user. The suggestions regarding food include, for example, nutritional balance, calorie restriction, and the like, but are not limited to such examples. The suggestions regarding exercise include, for example, aerobic exercise, muscle training, and the like, but are not limited to such examples. The suggestions regarding supplements include, for example, vitamins, minerals, and the like, but are not limited to such examples. The suggestions regarding music include, for example, relaxing music, music for improving concentration, and the like, but are not limited to such examples. The suggestions regarding aromas include, for example, lavender, peppermint, and the like, but are not limited to such examples. The suggestions regarding hospitals include, for example, introductions to specialist departments, reservation methods, and the like, but are not limited to such examples. The suggestion unit uses the generation AI to analyze the physiological symptom data of the user and generate optimal suggestions. For example, the generation AI makes suggestions based on the user's input data, referring to past data and research papers. This allows the user to receive a variety of improvement suggestions.

[0069] The collection unit can collect opinions of people with similar symptoms. The collection unit collects opinions of people with similar symptoms, for example, by using an online forum or a questionnaire survey. Methods for collecting opinions include, but are not limited to, text input, voice input, video input, and the like. The collection unit uses a generation AI to analyze the collected opinions and provide them to the user. For example, the generation AI classifies the collected opinions and extracts the voices of people with similar symptoms. In addition, the collection unit can provide information that gives the user a sense of security based on the collected opinions. This allows the user to feel that he or she is not alone and to feel a sense of security.

[0070] The collection unit can estimate the user's emotion and adjust the timing of collection of information on physiological symptoms based on the estimated user's emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the change in facial expression. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates the emotion score based on the fluctuation of the heart rate. This allows the collection unit to collect information at an optimal timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0071] The collection unit can analyze the user's past physiological symptom data and select an information collection method. The collection unit selects the most effective information collection method based on, for example, physiological symptom data recorded by the user in the past. Past physiological symptom data includes, for example, diaries, medical records, etc., but is not limited to such examples. The collection unit uses the generation AI to analyze the past data and select the optimal information collection method. For example, the generation AI inputs the user's past data, predicts the timing when a specific symptom will appear, and collects information according to that timing. The collection unit can also suggest the optimal information collection method for a specific symptom based on the past data. This allows the collection unit to select the optimal information collection method based on the past data.

[0072] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting information on physiological symptoms. For example, when the user inputs the current living situation, the collection unit filters information on related physiological symptoms based on the information. The living situation includes, for example, occupation, home environment, lifestyle, etc., but is not limited to such examples. The collection unit uses the generation AI to filter information based on the user's living situation and field of interest. For example, the generation AI preferentially collects related information based on the user's input data. The collection unit can also preferentially collect related information based on the user's field of interest (e.g., diet and exercise). This allows the collection unit to provide information according to the user's living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's input data to the generation AI and have the generation AI perform filtering.

[0073] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects information that has a relaxing effect. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The collection unit uses a generation AI to estimate the user's emotion and preferentially collect optimal information. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. In addition, when the user is relaxed, the collection unit can also preferentially collect detailed information. This allows the collection unit to preferentially collect optimal information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data into the generation AI and have the generation AI execute a process of determining the priority of the information.

[0074] The collection unit can collect information on physiological symptoms with priority based on the geographical location information of the user when collecting information on the physiological symptoms. For example, when the user is in a specific area, the collection unit collects information on physiological symptoms related to the area with priority. The geographical location information includes, but is not limited to, for example, GPS data, address information, and the like. The collection unit uses the generation AI to analyze the geographical location information of the user and collects information related to the area with priority. For example, the generation AI inputs the user's location data and collects information related to the area. In addition, when the user is traveling, the collection unit can also collect information on physiological symptoms related to the climate and environment of the travel destination with priority. This allows the collection unit to collect optimal information based on the user's geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location data to the generation AI and cause the generation AI to collect information with high relevance.

[0075] The collection unit can analyze the user's social media activity and collect related information when collecting information on physiological symptoms. The collection unit, for example, collects information on related physiological symptoms based on information shared by the user on social media. Social media activities include, for example, but are not limited to, the content of posts, the number of likes, and the number of followers. The collection unit uses the generation AI to analyze the user's social media activity and collect related information. For example, the generation AI takes the user's posting data as input and preferentially collects information related to topics of interest. The collection unit can also collect information on related physiological symptoms based on information on accounts and groups the user follows. This allows the collection unit to collect optimal information based on the user's social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related information.

[0076] The suggestion unit can estimate the user's emotion and adjust the expression method of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a suggestion in gentle language. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The suggestion unit uses a generation AI to estimate the user's emotion and makes a suggestion in an optimal expression method. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. In addition, when the user is relaxed, the suggestion unit can also make a suggestion including a detailed explanation. This allows the suggestion unit to make a suggestion in an optimal expression method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can input a user's emotional data into the generation AI and cause the generation AI to execute a process of adjusting the way the suggestion is expressed.

[0077] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the physiological symptom at the time of suggestion. For example, the suggestion unit makes a detailed suggestion for a physiological symptom with a high level of importance. The importance of a physiological symptom includes, for example, the frequency and the impact of the symptom, but is not limited to such examples. The suggestion unit uses a generation AI to evaluate the importance of the physiological symptom and make an optimal suggestion. For example, the generation AI inputs symptom data of the user and calculates an importance score. In addition, the suggestion unit can also make a concise suggestion for a physiological symptom with a low level of importance. This allows the suggestion unit to make an optimal suggestion according to the importance of the physiological symptom. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit inputs symptom data of the user to the generation AI and causes the generation AI to execute a process of adjusting the level of detail of the suggestion.

[0078] The suggestion unit can apply different suggestion algorithms depending on the category of the physiological symptom at the time of suggestion. For example, the suggestion unit applies an algorithm that takes nutritional balance into consideration for suggestions regarding meals. The categories of physiological symptoms include, but are not limited to, for example, the digestive system, the respiratory system, and the nervous system. The suggestion unit uses the generation AI to apply an optimal suggestion algorithm depending on the category of the physiological symptom. For example, the generation AI inputs the symptom data of the user and applies an algorithm depending on the category. In addition, the suggestion unit can also apply an algorithm that takes the physical strength level of the user into consideration for suggestions regarding exercise. This allows the suggestion unit to make an optimal suggestion depending on the category of the physiological symptom. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit inputs the symptom data of the user to the generation AI and causes the generation AI to execute a process of applying the suggestion algorithm.

[0079] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a short and to the point suggestion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The suggestion unit uses a generation AI to estimate the user's emotion and makes a suggestion with an optimal length. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. In addition, when the user is relaxed, the suggestion unit can also make a longer suggestion including a detailed explanation. This allows the suggestion unit to make a suggestion with an optimal length according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI or may be performed without using an AI. For example, the suggestion unit can input user emotion data to the generation AI and cause the generation AI to execute a process of adjusting the length of the suggestion.

[0080] The suggestion unit can determine the priority of the suggestion based on the time of occurrence of the physiological symptoms at the time of suggestion. For example, the suggestion unit prioritizes the suggestion during the period when the physiological symptoms frequently occur. The time of occurrence of the physiological symptoms includes, but is not limited to, for example, a season, a time period, a specific event, etc. The suggestion unit uses the generation AI to analyze the time of occurrence of the physiological symptoms and makes an optimal suggestion. For example, the generation AI inputs the symptom data of the user and determines the priority of the suggestion based on the time of occurrence. In addition, the suggestion unit can lower the priority of the suggestion during the period when the physiological symptoms rarely occur. This allows the suggestion unit to make an optimal suggestion according to the time of occurrence of the physiological symptoms. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the symptom data of the user to the generation AI and cause the generation AI to execute a process of determining the priority of the suggestion.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of the physiological symptoms at the time of suggestion. For example, the suggestion unit prioritizes suggestions for highly relevant physiological symptoms. The relevance of physiological symptoms includes, but is not limited to, common causes and common symptoms. The suggestion unit uses a generation AI to evaluate the relevance of physiological symptoms and makes suggestions in an optimal order. For example, the generation AI inputs symptom data of the user and calculates a relevance score. The suggestion unit can also postpone the order of suggestions for less relevant physiological symptoms. This allows the suggestion unit to make suggestions in an optimal order according to the relevance of the physiological symptoms. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit inputs symptom data of the user to the generation AI and causes the generation AI to execute a process of adjusting the order of suggestions.

[0082] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides information in gentle language. Methods of estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The providing unit uses a generating AI to estimate the user's emotion and provide information in an optimal manner. For example, the generating AI receives the user's facial expression data as an input and calculates an emotion score. In addition, the providing unit can also provide detailed information when the user is relaxed. This allows the providing unit to provide information in an optimal manner according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generating AI. The generating AI is, for example, a text generating AI (for example, LLM) or a multimodal generating AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generating AI and cause the generating AI to execute a process of adjusting the method of providing information.

[0083] The providing unit can select the optimal information providing method by referring to the user's past usage history when providing information. The providing unit selects the optimal method based on, for example, the information providing method used by the user in the past. The past usage history includes, for example, an access log, a frequency of use, and the like, but is not limited to such examples. The providing unit uses the generation AI to analyze the user's past usage history and selects the optimal information providing method. For example, the generation AI inputs the user's usage history data and proposes the most effective information providing method. The providing unit can also propose the optimal information providing method to the user based on the past usage history. This allows the providing unit to provide information in the optimal method based on the past usage history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's usage history data to the generation AI and cause the generation AI to execute a process of selecting an information providing method.

[0084] The providing unit can customize the provided content based on the user's current living situation when providing information. For example, when the user inputs the current living situation, the providing unit customizes the provided content based on the information. The living situation includes, for example, occupation, home environment, and lifestyle, but is not limited to such examples. The providing unit uses the generating AI to analyze the living situation of the user and provide optimal information. For example, the generating AI provides information according to the living situation based on the user's input data. The providing unit can also provide optimal information according to the user's living situation (for example, how busy the user is at work or the home situation). This allows the providing unit to provide optimal information according to the user's living situation. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data to the generating AI and cause the generating AI to execute a process of customizing the provided content.

[0085] The providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit preferentially provides information that has a relaxing effect. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The providing unit uses a generating AI to estimate the user's emotion and preferentially provides optimal information. For example, the generating AI inputs the user's facial expression data and calculates an emotion score. In addition, when the user is relaxed, the providing unit can also preferentially provide detailed information. This allows the providing unit to preferentially provide optimal information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generating AI. The generating AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input a user's emotion data to the generating AI and cause the generating AI to execute a process of determining the priority of information provision.

[0086] The providing unit can select a method of providing information based on the geographical location information of the user when providing information. For example, when the user is in a specific area, the providing unit provides information related to the area with priority. The geographical location information includes, but is not limited to, for example, GPS data, address information, and the like. The providing unit uses the generating AI to analyze the geographical location information of the user and selects an optimal information providing method. For example, the generating AI inputs the user's location data and provides information related to the area. In addition, when the user is traveling, the providing unit can also provide information related to the climate and environment of the travel destination with priority. This allows the providing unit to provide optimal information based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location data to the generating AI and cause the generating AI to execute a process of selecting an information providing method.

[0087] The providing unit can adjust the content of the information provided by analyzing the social media activity of the user when providing the information. The providing unit provides related information based on, for example, information shared by the user on social media. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The providing unit uses the generating AI to analyze the social media activity of the user and provides optimal information. For example, the generating AI inputs the user's post data and provides information related to topics of interest preferentially. The providing unit can also provide related information based on information on accounts and groups the user follows. This allows the providing unit to provide optimal information based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to execute a process of adjusting the content of the provision.

[0088] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria based on the estimated user's emotion. For example, when the user is feeling stressed, the evaluation unit relaxes the evaluation criteria to allow the user to relax. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The evaluation unit estimates the user's emotion using a generation AI and applies optimal evaluation criteria. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. In addition, when the user is relaxed, the evaluation unit can also apply detailed evaluation criteria. This allows the evaluation unit to apply optimal evaluation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to execute a process of adjusting the evaluation criteria.

[0089] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data at the time of evaluation. The evaluation unit selects an optimal evaluation algorithm based on, for example, past evaluation data. Past evaluation data includes, for example, questionnaire results, experimental data, etc., but is not limited to such examples. The evaluation unit analyzes past data using a generation AI and optimizes the evaluation algorithm. For example, the generation AI takes past evaluation data as input, extracts a specific pattern, and optimizes the evaluation algorithm. The evaluation unit can also improve the accuracy of the evaluation algorithm based on the past data. This allows the evaluation unit to apply an optimal evaluation algorithm based on the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input past evaluation data to the generation AI and cause the generation AI to execute a process of optimizing the evaluation algorithm.

[0090] The evaluation unit can apply different evaluation methods for each category of physiological symptoms at the time of evaluation. For example, the evaluation unit applies an evaluation method that takes nutritional balance into consideration for evaluation regarding meals. The categories of physiological symptoms include, for example, the digestive system, the respiratory system, and the nervous system, but are not limited to such examples. The evaluation unit uses the generation AI to apply an optimal evaluation method according to the category of physiological symptoms. For example, the generation AI inputs the user's symptom data and applies an evaluation method according to the category. In addition, the evaluation unit can also apply an evaluation method that takes the user's physical strength level into consideration for evaluation regarding exercise. This allows the evaluation unit to apply an optimal evaluation method according to the category of physiological symptoms. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs the user's symptom data to the generation AI and causes the generation AI to execute a process of applying the evaluation method.

[0091] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. For example, when the user is stressed, the evaluation unit provides a simple and highly visible display method. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, text analysis, and the like. The evaluation unit uses a generation AI to estimate the user's emotion and provides the evaluation result in an optimal display method. For example, the generation AI inputs the user's facial expression data and calculates an emotion score. In addition, when the user is relaxed, the evaluation unit can also provide a display method including detailed information. This allows the evaluation unit to provide the evaluation result in an optimal display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the evaluation unit can input a user's emotional data into the generation AI and cause the generation AI to execute a process of adjusting the way in which the evaluation results are displayed.

[0092] The evaluation unit can weight the evaluation based on the occurrence time of the physiological symptoms at the time of evaluation. For example, the evaluation unit increases the weighting of the evaluation in the period when the physiological symptoms frequently occur. The occurrence time of the physiological symptoms includes, but is not limited to, for example, a season, a time period, a specific event, etc. The evaluation unit uses the generation AI to analyze the occurrence time of the physiological symptoms and performs the evaluation with optimal weighting. For example, the generation AI inputs the symptom data of the user and determines the weighting of the evaluation based on the occurrence time. In addition, the evaluation unit can also lower the weighting of the evaluation in the period when the physiological symptoms rarely occur. This allows the evaluation unit to perform the evaluation with optimal weighting according to the occurrence time of the physiological symptoms. Part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the symptom data of the user to the generation AI and cause the generation AI to execute the processing of weighting the evaluation.

[0093] The evaluation unit can improve the accuracy of the evaluation by referring to related literature on physiological symptoms at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to, for example, the latest research papers on physiological symptoms. The related literature includes, for example, academic papers, specialized books, etc., but is not limited to such examples. The evaluation unit uses the generation AI to analyze the related literature and improve the accuracy of the evaluation. For example, the generation AI takes related literature data as an input and optimizes the evaluation algorithm. The evaluation unit can also improve the accuracy of the evaluation algorithm based on past literature. This allows the evaluation unit to improve the accuracy of the evaluation based on the related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input related literature data to the generation AI and cause the generation AI to execute processing to improve the accuracy of the evaluation.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The collection unit can refer to the user's past medical records when collecting information about the user's physiological symptoms. For example, information about diagnoses and prescribed medications that the user has received in the past can be collected and associated with the user's current physiological symptoms. The collection unit can also collect the user's family history and genetic information to evaluate the impact of genetic factors on the physiological symptoms. Furthermore, the collection unit can collect the user's lifestyle habits (e.g., diet, exercise, sleep patterns) and analyze the impact of these factors on the physiological symptoms. This allows the collection unit to collect comprehensive information about the user's physiological symptoms and make more accurate suggestions.

[0096] The suggestion unit can estimate the user's emotions and adjust the suggested chat room content based on the estimated user's emotions. For example, if the user is feeling stressed, a chat room with a relaxing effect can be suggested. Also, if the user is feeling lonely, a chat room where people with similar symptoms gather can be suggested. Furthermore, if the user has positive emotions, a chat room for sharing information and giving advice can be suggested. In this way, the suggestion unit can suggest the optimal chat room according to the user's emotions.

[0097] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user has positive emotions, the evaluation results can be visually displayed in graphs or charts. This allows the evaluation unit to provide the evaluation results in an optimal display method according to the user's emotions.

[0098] The suggestion unit can make suggestions suited to a specific season or climate according to the user's physiological symptoms. For example, in cold seasons, it can suggest foods and exercises that will warm the body. In humid seasons, it can also suggest aromas and supplements to combat humidity. Furthermore, in seasons with a lot of pollen, it can suggest foods and exercises to combat allergies. This allows the suggestion unit to make optimal suggestions according to the season and climate.

[0099] The collection unit can estimate the user's emotions and adjust the timing of collecting opinions of people with similar symptoms based on the estimated user's emotions. For example, if the user is feeling stressed, opinions that have a relaxing effect can be preferentially collected. Also, if the user is feeling lonely, opinions that can gain sympathy can be preferentially collected. Furthermore, if the user has positive emotions, opinions that include encouragement and advice can be preferentially collected. This allows the collection unit to collect optimal opinions according to the user's emotions.

[0100] When collecting information on the user's physiological symptoms, the collection unit can filter the information based on the user's occupation and living environment. For example, for a user who does a lot of desk work, it is possible to preferentially collect information on methods for improving physiological symptoms caused by sitting for long periods of time. Also, for a user who is raising children, it is possible to preferentially collect information on physiological symptoms related to child-rearing stress. Furthermore, for a user who often works night shifts, it is possible to preferentially collect information on nighttime physiological symptoms. This allows the collection unit to collect optimal information according to the user's occupation and living environment.

[0101] The collection unit can estimate the user's emotions and adjust the timing of providing the analysis results of the past physiological symptom data based on the estimated user's emotions. For example, if the user is feeling stressed, information with a relaxing effect can be provided preferentially. Also, if the user is feeling lonely, information that can gain sympathy can be provided preferentially. Furthermore, if the user has positive emotions, information including encouragement and advice can be provided preferentially. This allows the collection unit to provide information at the optimal timing according to the user's emotions.

[0102] When collecting information on the user's physiological symptoms, the collection unit can filter the information based on the user's hobbies and interests. For example, for a user who likes music, information on music therapy can be preferentially collected. Also, for a user who likes sports, information on exercise therapy can be preferentially collected. Furthermore, for a user who likes cooking, information on diet therapy can be preferentially collected. This allows the collection unit to collect optimal information according to the user's hobbies and interests.

[0103] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, information that has a relaxing effect can be preferentially collected. Also, if the user is feeling lonely, information that can gain sympathy can be preferentially collected. Furthermore, if the user has positive emotions, information that includes encouragement and advice can be preferentially collected. This allows the collection unit to preferentially collect optimal information according to the user's emotions.

[0104] When collecting information on the user's physiological symptoms, the collection unit can filter the information based on the user's geographical location information. For example, for a user living in an urban area, information on physiological symptoms related to urban life can be preferentially collected. Also, for a user living in a rural area, information on physiological symptoms related to farm work can be preferentially collected. Furthermore, information on physiological symptoms related to the climate and environment specific to a particular region can be preferentially collected. This allows the collection unit to collect optimal information according to the user's geographical location information.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The collection unit collects information on physiological symptoms. Information on physiological symptoms includes, but is not limited to, headache, stomachache, fatigue, etc. The collection unit can collect information by, for example, a questionnaire, a sensor, user input, or the like. Step 2: The suggestion unit makes suggestions based on the information collected by the collection unit. Suggestions include, but are not limited to, for example, improving lifestyle habits and visiting a medical institution. The suggestion unit uses a generation AI to analyze the collected information and generate optimal suggestions. For example, the generation AI inputs the user's physiological symptom data and generates suggestions based on past data and research papers. Step 3: The providing unit provides the content proposed by the suggesting unit to the user. The providing method includes, but is not limited to, for example, notification, email, in-app message, etc. The providing unit uses the generating AI to provide the information to the user in the most optimal way. For example, the generating AI selects the most optimal providing method based on the user's usage history and current situation.

[0107] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements including the above-mentioned collection unit, suggestion unit, provision unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information using a sensor of the smart device 14 or a user input. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal suggestions using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14, and provides information to the user through notifications and in-app messages. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and performs evaluation based on effect measurement reports and research papers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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, the RAM 30, and the 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.

[0114] 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.

[0115] 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 the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] 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 performed in a secure state.

[0118] Fig. 4 shows an example of 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.

[0119] 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.

[0120] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0124] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned collection unit, suggestion unit, provision unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information using a sensor of the smart glasses 214 or a user input. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates optimal suggestions using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides information to the user through notifications and in-app messages. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on effect measurement reports and research papers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned collection unit, suggestion unit, provision unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information using a sensor of the headset type terminal 314 or a user input. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal suggestions using a generation AI. The provision unit is realized by the control unit 46A of the headset type terminal 314, and provides information to the user through notifications and in-app messages. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and performs evaluation based on effect measurement reports and research papers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0151] 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.

[0152] 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.

[0153] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 a voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0159] Each of the multiple elements including the above-mentioned collection unit, suggestion unit, provision unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information using a sensor of the robot 414 or a user input. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal suggestions using a generation AI. The provision unit is realized by the control unit 46A of the robot 414, and provides information to the user through notifications and in-app messages. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and performs evaluation based on effect measurement reports and research papers. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] 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 Universal Serial Bus (USB) 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.

[0169] In addition, 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 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0173] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A collection unit for collecting information regarding menstrual symptoms; a suggestion unit that makes a suggestion based on the information collected by the collection unit; a providing unit that provides the content proposed by the suggestion unit to a user; Equipped A system characterized by: (Appendix 2) The suggestion unit, Suggest a chat room 2. The system of claim 1, (Appendix 3) Equipped with an evaluation department that evaluates the program through effect measurement reports and research papers 2. The system of claim 1, (Appendix 4) The suggestion unit, Providing ways to make recommendations on diet, exercise, supplements, music, aromatherapy, and hospitals 2. The system of claim 1, (Appendix 5) The collecting unit includes: Gather opinions from people with similar symptoms 2. The system of claim 1, (Appendix 6) The collecting unit includes: To estimate a user's emotion and adjust a timing of collection of information on physiological symptoms based on the estimated emotion of the user. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze the user's past menstrual symptom data and select the method of collecting information 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting menstrual symptom information, filtering is performed based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting information on menstrual symptoms, the system prioritizes collection of information that is more relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information on menstrual symptoms, the social media activity of users is analyzed to collect related information. 2. The system of claim 1, (Appendix 12) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The suggestion unit, When making suggestions, the level of detail of the suggestions will be adjusted based on the importance of the menstrual symptoms 2. The system of claim 1, (Appendix 14) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of physiological symptoms. 2. The system of claim 1, (Appendix 15) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 16) The suggestion unit, When making suggestions, prioritize suggestions based on when menstrual symptoms occur 2. The system of claim 1, (Appendix 17) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of menstrual symptoms 2. The system of claim 1, (Appendix 18) The providing unit is Estimate the user's emotions and adjust the way information is provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 19) The providing unit is When providing information, the optimal method of providing the information is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 20) The providing unit is When providing information, tailor the offering based on the user's current life situation 2. The system of claim 1, (Appendix 21) The providing unit is Estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The providing unit is When providing information, the method of providing it is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 23) The providing unit is When providing information, analyze users' social media activity to tailor the content provided 2. The system of claim 1, (Appendix 24) The evaluation unit is Inferring a user's emotion and adjusting the evaluation criteria based on the estimated user's emotion 2. The system of claim 1, (Appendix 25) The evaluation unit is During evaluation, past evaluation data is referenced to optimize the evaluation algorithm 2. The system of claim 1, (Appendix 26) The evaluation unit is During the assessment, different assessment techniques are used for each category of physiological symptoms. 2. The system of claim 1, (Appendix 27) The evaluation unit is Inferring user emotion and adjusting the way rating results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 28) The evaluation unit is During the evaluation, the evaluation is weighted based on the timing of menstrual symptoms. 2. The system of claim 1, (Appendix 29) The evaluation unit is During the evaluation, refer to literature related to physiological symptoms to improve the accuracy of the evaluation. 2. The system of claim 1, [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting information regarding menstrual symptoms; a suggestion unit that makes a suggestion based on the information collected by the collection unit; a providing unit that provides the content proposed by the suggestion unit to a user; Equipped A system characterized by:

2. The suggestion unit, Suggest a chat room 2. The system of claim 1 .

3. Equipped with an evaluation department that evaluates the program through effect measurement reports and research papers 2. The system of claim 1 .

4. The suggestion unit, Providing ways to make recommendations on diet, exercise, supplements, music, aromatherapy, and hospitals 2. The system of claim 1 .

5. The collecting unit includes: Gather opinions from people with similar symptoms 2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotion and adjust a timing of collection of information on physiological symptoms based on the estimated emotion of the user.

2. The system of claim 1 .

7. The collecting unit includes: Analyze the user's past menstrual symptom data and select the method of collecting information 2. The system of claim 1 .

8. The collecting unit includes: When collecting menstrual symptom information, filtering is performed based on the user's current life situation and areas of interest.

2. The system of claim 1 .

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