system

Abstract

The system according to the embodiment aims to propose an optimal meal plan based on an individual's health condition and to suggest how to arrange ingredients and how to cook them. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, an arrangement unit, and a presentation unit. The collection unit collects health status values. The analysis unit analyzes the values ​​collected by the collection unit. The proposal unit proposes an appropriate meal plan based on the analysis results obtained by the analysis unit. The arrangement unit arranges the necessary ingredients based on the meal plan proposed by the proposal unit. The presentation unit presents cooking methods based on the meal plan proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose meal plans based on individual health conditions, arrange ingredients, or provide cooking methods, so there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal meal plan based on an individual's health condition and to suggest how to arrange ingredients and how to cook them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, an arrangement unit, and a presentation unit. The collection unit collects health status values. The analysis unit analyzes the values ​​collected by the collection unit. The proposal unit proposes an appropriate meal plan based on the analysis results obtained by the analysis unit. The arrangement unit arranges the necessary ingredients based on the meal plan proposed by the proposal unit. The presentation unit presents cooking methods based on the meal plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal meal plan based on an individual's health condition and suggest how to arrange ingredients and how to cook them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention allows a user to input various health status values ​​and then proposes optimal meals, arranges ingredients, and suggests cooking methods, thereby bringing the user closer to an ideal state of health. In this health management system, a user inputs various health status values ​​(e.g., weight, blood pressure, blood sugar level, etc.), and the system analyzes these values ​​and proposes an optimal meal plan for the user. Furthermore, the system arranges necessary ingredients and suggests cooking methods based on the proposed meal plan. This allows the user to approach an ideal state of health. For example, a user inputs various health status values, such as weight, blood pressure, and blood sugar level. This information is entered into the system. The system then analyzes the input information. The system evaluates the user's health status and proposes an optimal meal plan. For example, if the user is gaining weight, a calorie-restricted meal plan is proposed. Similarly, if the user's blood pressure is high, a low-sodium meal plan is proposed. Based on the proposed meal plan, the system arranges the necessary ingredients. For example, the ingredients required for the proposed meal plan can be ordered online. This allows the user to easily obtain the necessary ingredients. The system also suggests cooking methods based on the proposed meal plan. For example, it displays specific recipes and cooking procedures, allowing users to easily prepare healthy meals. This system allows users to eat optimal meals tailored to their own health condition, bringing them closer to their ideal health. For example, it makes weight management and blood pressure management easier, improving overall health. This allows the health management system to propose optimal meal plans based on the user's health condition, arrange ingredients, and suggest cooking methods, bringing them closer to their ideal health.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a disposition unit, and a presentation unit. The collection unit collects health condition values ​​entered by a user. For example, the collection unit can collect values ​​such as weight, blood pressure, and blood glucose level entered by the user. The collection unit can also automatically collect health condition values ​​through a wearable device or a smartphone app. For example, the collection unit can collect data such as heart rate and step count from a wearable device. The collection unit can also collect values ​​manually entered by the user. For example, the collection unit can collect weight and blood pressure values ​​entered by the user into an app. The analysis unit analyzes the values ​​collected by the collection unit to evaluate the user's health condition. For example, the analysis unit can evaluate the user's health condition based on the collected values ​​such as weight, blood pressure, and blood glucose level. The analysis unit can also track changes in the user's health condition based on the collected data. For example, the analysis unit can compare the collected data with past data to evaluate whether the user's weight is increasing or decreasing. The analysis unit can also evaluate the user's health risk based on the collected data. For example, the analysis unit can assess the risk of high blood pressure for a user with high blood pressure. The suggestion unit proposes an optimal meal plan to the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose a calorie-restricted meal plan to a user who is gaining weight. The suggestion unit can also propose a low-salt meal plan to a user with high blood pressure. The suggestion unit can also propose a meal plan according to the user's health goals. For example, the suggestion unit can propose a low-calorie meal plan to a user who is trying to lose weight. The arrangement unit arranges necessary ingredients based on the meal plan proposed by the proposal unit. For example, the arrangement unit can order ingredients needed for the proposed meal plan online. The arrangement unit can also arrange the necessary ingredients using a supermarket or food delivery service near the user. The arrangement unit can also arrange ingredients according to the user's preferences. For example, the arrangement unit can arrange organic ingredients that the user prefers.The presentation unit presents specific cooking methods based on the meal plan proposed by the proposal unit. For example, the presentation unit can display specific recipes and cooking steps. The presentation unit can also show cooking methods in the form of videos. The presentation unit can also present the time and tools required for cooking. For example, the presentation unit can display the time required for cooking and the cooking utensils to be used. This allows the user to easily prepare healthy meals. As a result, the health management system according to the embodiment can propose an optimal meal plan based on the user's health condition, arrange ingredients, and present cooking methods, thereby bringing the user closer to an ideal health state.

[0030] The collection unit can analyze the user's past health data and select an appropriate collection method. For example, the collection unit selects a collection method that will obtain the most accurate data from the user's past health data. For example, the collection unit can adjust the collection frequency based on the user's past health data. The collection unit can also analyze the user's past health data and select a collection method for a specific time period. For example, the collection unit can select a method for collecting health status values ​​for a specific time period based on the user's past health data. This allows for more accurate data to be collected by selecting the optimal collection method based on the past health data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health data into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting the health status values, the collection unit can filter them based on the user's current living situation or activity level. For example, if the user is exercising, the collection unit collects the health status values ​​after the user exercises. For example, if the user is resting, the collection unit can collect the health status values ​​after the user rests. Furthermore, if the user is working, the collection unit can collect the health status values ​​after work. For example, if the user is working, the collection unit can collect the health status values ​​after work. This allows for more appropriate data to be collected by filtering the data according to the user's living situation or activity level. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity data into a generation AI and have the generation AI perform filtering.

[0032] When collecting health status values, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit prioritizes collecting health status values ​​specific to high altitudes. For example, when the user is in an urban area, the collection unit can prioritize collecting health status values ​​specific to urban areas. Furthermore, when the user is traveling, the collection unit can also prioritize collecting health status values ​​related to the environment of the travel destination. For example, when the user is traveling, the collection unit can prioritize collecting health status values ​​related to the environment of the travel destination. In this way, by taking the user's geographical location information into account, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant data.

[0033] When collecting health status values, the collection unit can analyze the user's social media activity and collect related data. For example, if the user posts about health on social media, the collection unit collects values ​​related to the content. For example, if the user posts about exercise on social media, the collection unit can collect values ​​related to the content. Furthermore, if the user posts about diet on social media, the collection unit can also collect values ​​related to the content. For example, if the user posts about diet on social media, the collection unit can collect values ​​related to the content. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health condition. For example, the analysis unit performs a detailed analysis on important health condition values. For example, the analysis unit can perform a simplified analysis on less important health condition values. The analysis unit can also gradually adjust the level of detail of the analysis depending on the importance of the health condition. For example, the analysis unit can determine whether to perform a detailed analysis or a simplified analysis depending on the importance of the health condition. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis depending on the importance of the health condition. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health condition data to a generation AI and cause the generation AI to adjust the level of detail of the analysis depending on the importance.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the health condition. For example, the analysis unit can apply an analysis algorithm specialized for weight management to values ​​related to weight. For example, the analysis unit can apply an analysis algorithm specialized for blood pressure management to values ​​related to blood glucose levels. Furthermore, the analysis unit can also apply an analysis algorithm specialized for blood glucose level management to values ​​related to blood glucose levels. For example, the analysis unit can apply an analysis algorithm specialized for blood glucose level management to values ​​related to blood glucose levels. This allows for applying an appropriate analysis algorithm depending on the classification of the health condition, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health condition data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the classification.

[0036] During analysis, the analysis unit can determine the priority of analysis based on when the health status values ​​were acquired. For example, the analysis unit prioritizes analysis of recently acquired health status values. For example, the analysis unit can prioritize the most recent values ​​while referring to health status values ​​acquired in the past. The analysis unit can also gradually adjust the priority of analysis depending on when the health status values ​​were acquired. For example, the analysis unit can gradually adjust the priority of analysis depending on when the health status values ​​were acquired. This enables analysis that prioritizes the most recent data by determining the priority of analysis based on when the health status values ​​were acquired. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input health status data into a generation AI and have the generation AI determine the priority of analysis based on the time of acquisition.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relationships between health conditions. For example, the analysis unit prioritizes analysis of health condition values ​​that are highly correlated. For example, the analysis unit can postpone analysis of health condition values ​​that are less correlated. The analysis unit can also gradually adjust the order of analysis according to the correlation between health conditions. For example, the analysis unit can gradually adjust the order of analysis according to the correlation between health conditions. In this way, by adjusting the order of analysis based on the correlation between health conditions, highly correlated data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health condition data to a generation AI and cause the generation AI to adjust the order of analysis based on the relationships.

[0038] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the meal plan. For example, the proposal unit can make a detailed proposal for an important meal plan. For example, the proposal unit can make a simplified proposal for a less important meal plan. The proposal unit can also gradually adjust the level of detail of the proposal depending on the importance of the meal plan. For example, the proposal unit can determine whether to make a detailed proposal or a simplified proposal depending on the importance of the meal plan. As a result, important plans can be proposed in detail by adjusting the level of detail of the proposal depending on the importance of the meal plan. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input meal plan data into a generation AI and cause the generation AI to adjust the level of detail of the proposal depending on the importance.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the classification of the meal plan. For example, for a meal plan related to dieting, the proposal unit can apply a proposal algorithm specialized for dieting. For example, for a meal plan related to high blood pressure, the proposal unit can apply a proposal algorithm specialized for high blood pressure management. Furthermore, for a meal plan related to diabetes, the proposal unit can apply a proposal algorithm specialized for diabetes management. For example, for a meal plan related to diabetes, the proposal unit can apply a proposal algorithm specialized for diabetes management. This allows for more accurate proposals to be provided by applying an appropriate proposal algorithm depending on the classification of the meal plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input meal plan data into a generation AI and cause the generation AI to apply a proposal algorithm depending on the classification.

[0040] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the meal plan. For example, the proposal unit can prioritize the most recently submitted meal plan. For example, the proposal unit can refer to previously submitted meal plans and emphasize the most recent plan. The proposal unit can also gradually adjust the priority of the proposal depending on the time of submission of the meal plan. For example, the proposal unit can gradually adjust the priority of the proposal depending on the time of submission of the meal plan. This enables proposals that emphasize the most recent plan by determining the priority of the proposal based on the time of submission of the meal plan. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or without AI. For example, the proposal unit can input meal plan data into a generation AI and cause the generation AI to determine the priority of the proposal based on the time of submission.

[0041] The suggestion unit can adjust the order of suggestions based on the relationships between meal plans when suggesting them. For example, the suggestion unit prioritizes suggesting meal plans that are highly related to each other. For example, the suggestion unit can postpone suggesting meal plans that are less related. The suggestion unit can also gradually adjust the order of suggestions based on the relevance of the meal plans. For example, the suggestion unit can gradually adjust the order of suggestions based on the relevance of the meal plans. As a result, by adjusting the order of suggestions based on the relevance of the meal plans, highly related plans can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input meal plan data into a generation AI and cause the generation AI to adjust the order of suggestions based on the relationships.

[0042] When making arrangements, the arrangement unit can analyze the user's past ingredient purchase history and select an appropriate arrangement method. The arrangement unit, for example, selects the most efficient arrangement method from the user's past ingredient purchase history. For example, the arrangement unit can adjust the arrangement frequency based on the user's past ingredient purchase history. The arrangement unit can also analyze the user's past ingredient purchase history and select an arrangement method for a specific time period. For example, the arrangement unit can select a method for arranging ingredients for a specific time period based on the user's past ingredient purchase history. This enables efficient arrangements by selecting the optimal arrangement method based on the past ingredient purchase history. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can input the user's past ingredient purchase history into the generation AI and have the generation AI select the optimal arrangement method.

[0043] The arrangement unit can adjust the means of arrangement based on the user's current living situation when making arrangements. For example, if the user is busy, the arrangement unit provides a means for making arrangements quickly. For example, if the user is relaxed, the arrangement unit can provide detailed arrangement means. Furthermore, if the user is traveling, the arrangement unit can also provide arrangement means at the travel destination. For example, if the user is traveling, the arrangement unit can provide arrangement means at the travel destination. This enables more appropriate arrangements by customizing the means of arrangement according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the arrangement unit can input the user's living situation data into a generation AI and have the generation AI adjust the means.

[0044] When making arrangements, the arrangement unit can select an appropriate arrangement method based on the user's geographical location information. For example, if the user is in an urban area, the arrangement unit selects an arrangement method specific to urban areas. For example, if the user is in the suburbs, the arrangement unit can select an arrangement method specific to suburbs. Furthermore, if the user is traveling, the arrangement unit can also select an arrangement method for the travel destination. For example, if the user is traveling, the arrangement unit can select an arrangement method for the travel destination. This makes it possible to provide the optimal arrangement method by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the arrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the arrangement unit can input the user's geographical location data into the generation AI and have the generation AI select an appropriate arrangement method.

[0045] When making arrangements, the arrangement unit can analyze the user's social media activity and suggest arrangement methods. For example, if the user mentions specific ingredients on social media, the arrangement unit arranges those ingredients. For example, if the user mentions a specific dish on social media, the arrangement unit can arrange the ingredients needed for that dish. Furthermore, if the user mentions a specific meal plan on social media, the arrangement unit can also arrange the ingredients needed for that plan. For example, if the user mentions a specific meal plan on social media, the arrangement unit can arrange the ingredients needed for that plan. In this way, by analyzing social media activity, related ingredients can be arranged. Some or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can input the user's social media data into a generation AI and have the generation AI suggest means.

[0046] When presenting a cooking method, the presentation unit can select an appropriate presentation method by referring to the user's past cooking history. The presentation unit, for example, presents the most efficient cooking method based on the user's past cooking history. For example, the presentation unit can simplify cooking procedures based on the user's past cooking history. The presentation unit can also refer to the user's past cooking history and preferentially present a specific cooking method. For example, the presentation unit can preferentially present a specific cooking method based on the user's past cooking history. This enables efficient cooking by selecting the optimal presentation method based on the past cooking history. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past cooking history data into the generation AI and cause the generation AI to select an appropriate presentation method.

[0047] When presenting a cooking method, the presentation unit can adjust the presentation means based on the user's current living situation. For example, if the user is busy, the presentation unit can present a quick cooking method. For example, if the user is relaxed, the presentation unit can present detailed cooking steps. Furthermore, if the user is traveling, the presentation unit can also present cooking methods at the travel destination. For example, if the user is traveling, the presentation unit can present cooking methods at the travel destination. This allows the presentation means to be customized according to the user's living situation, making it possible to provide a more appropriate cooking method. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the user's living situation data into the generation AI and cause the generation AI to adjust the means.

[0048] When presenting cooking methods, the presentation unit can select an appropriate presentation method based on the user's geographical location information. For example, if the user is in an urban area, the presentation unit can present cooking methods specific to urban areas. For example, if the user is in the suburbs, the presentation unit can present cooking methods specific to suburban areas. Furthermore, if the user is traveling, the presentation unit can also present cooking methods at the travel destination. For example, if the user is traveling, the presentation unit can present cooking methods at the travel destination. This makes it possible to provide optimal cooking methods by taking the user's geographical location information into consideration. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's geographical location data into the generation AI and cause the generation AI to select an appropriate presentation method.

[0049] When presenting a cooking method, the presentation unit can analyze the user's social media activity and suggest a means to present it. For example, if the user mentions a specific dish on social media, the presentation unit can present a cooking method for that dish. For example, if the user mentions specific ingredients on social media, the presentation unit can present a cooking method using those ingredients. Furthermore, if the user mentions a specific cooking method on social media, the presentation unit can preferentially present that cooking method. For example, if the user mentions a specific cooking method on social media, the presentation unit can preferentially present that cooking method. In this way, related cooking methods can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's social media data into the generation AI and cause the generation AI to suggest a means.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The health management system can further collect the user's sleep data and analyze it with an analysis unit. For example, the collection unit can collect the user's sleep time and sleep quality. The analysis unit can evaluate the user's health condition based on the collected sleep data and propose an optimal meal plan. For example, a meal plan suitable for replenishing energy can be proposed to a sleep-deprived user. The arrangement unit can also arrange ingredients with a relaxing effect based on the user's sleep data. This allows for more comprehensive health management by taking the user's sleep data into consideration.

[0052] The analysis unit can analyze the user's past health data and select an appropriate analysis method. For example, it can select an analysis method that will obtain the most accurate data from the user's past health data. The analysis unit can also adjust the frequency of analysis based on the user's past health data. This allows for more accurate data analysis by selecting the optimal analysis method based on the user's past health data.

[0053] The arrangement unit can analyze the user's past ingredient purchase history and select an appropriate arrangement method. For example, the most efficient arrangement method can be selected from the user's past ingredient purchase history. The arrangement unit can also adjust the arrangement frequency based on the user's past ingredient purchase history. This allows for efficient arrangement by selecting the optimal arrangement method based on the user's past ingredient purchase history.

[0054] The presentation unit can select an appropriate presentation method by referring to the user's past cooking history. For example, the presentation unit can present the most efficient cooking method based on the user's past cooking history. The presentation unit can also simplify the cooking procedure based on the user's past cooking history. This allows for efficient cooking by selecting the optimal presentation method based on the user's past cooking history.

[0055] The presentation unit can select an appropriate presentation method based on the user's geographical location information. For example, if the user is in an urban area, cooking methods specific to urban areas can be presented. Also, if the user is in a suburban area, cooking methods specific to suburban areas can be presented. In this way, the optimal cooking method can be provided by taking the user's geographical location information into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects health condition values ​​entered by the user. For example, the collection unit can collect values ​​such as weight, blood pressure, and blood sugar level entered by the user. The collection unit can also collect health condition values ​​automatically through a wearable device or a smartphone app. For example, the collection unit can collect data such as heart rate and number of steps from a wearable device. The collection unit can also collect values ​​manually entered by the user. For example, the collection unit can collect weight and blood pressure values ​​entered by the user into an app. Step 2: The analysis unit analyzes the numerical values ​​collected by the collection unit and evaluates the user's health condition. For example, the analysis unit can evaluate the user's health condition based on collected numerical values ​​such as weight, blood pressure, and blood sugar level. The analysis unit can also track changes in the user's health condition based on the collected data. For example, the analysis unit can compare the user's weight with past data to evaluate whether the user is gaining or losing weight. Furthermore, the analysis unit can evaluate the user's health risk based on the collected data. For example, the analysis unit can evaluate the risk of high blood pressure for a user with high blood pressure. Step 3: The suggestion unit suggests an optimal meal plan to the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest a calorie-restricted meal plan to a user who is gaining weight. The suggestion unit can also suggest a low-salt meal plan to a user who has high blood pressure. Furthermore, the suggestion unit can also suggest a meal plan according to the user's health goals. For example, the suggestion unit can suggest a low-calorie meal plan to a user who is trying to lose weight. Step 4: The arrangement unit arranges the necessary ingredients based on the meal plan proposed by the proposal unit. For example, the arrangement unit can order the ingredients necessary for the proposed meal plan online. The arrangement unit can also arrange the necessary ingredients using a supermarket or food delivery service near the user. Furthermore, the arrangement unit can arrange ingredients according to the user's preferences. For example, the arrangement unit can arrange organic ingredients that the user prefers. Step 5: The presentation unit presents specific cooking methods based on the meal plan proposed by the proposal unit. For example, the presentation unit can display specific recipes and cooking steps. The presentation unit can also show cooking methods in the form of videos. Furthermore, the presentation unit can present the time and tools required for cooking. For example, the presentation unit can display the time required for cooking and the cooking utensils to be used. This allows the user to easily prepare healthy meals.

[0058] (Example 2) A health management system according to an embodiment of the present invention allows a user to input various health status values ​​and then proposes optimal meals, arranges ingredients, and suggests cooking methods, thereby bringing the user closer to an ideal state of health. In this health management system, a user inputs various health status values ​​(e.g., weight, blood pressure, blood sugar level, etc.), and the system analyzes these values ​​and proposes an optimal meal plan for the user. Furthermore, the system arranges necessary ingredients and suggests cooking methods based on the proposed meal plan. This allows the user to approach an ideal state of health. For example, a user inputs various health status values, such as weight, blood pressure, and blood sugar level. This information is entered into the system. The system then analyzes the input information. The system evaluates the user's health status and proposes an optimal meal plan. For example, if the user is gaining weight, a calorie-restricted meal plan is proposed. Similarly, if the user's blood pressure is high, a low-sodium meal plan is proposed. Based on the proposed meal plan, the system arranges the necessary ingredients. For example, the ingredients required for the proposed meal plan can be ordered online. This allows the user to easily obtain the necessary ingredients. The system also suggests cooking methods based on the proposed meal plan. For example, it displays specific recipes and cooking procedures, allowing users to easily prepare healthy meals. This system allows users to eat optimal meals tailored to their own health condition, bringing them closer to their ideal health. For example, it makes weight management and blood pressure management easier, improving overall health. This allows the health management system to propose optimal meal plans based on the user's health condition, arrange ingredients, and suggest cooking methods, bringing them closer to their ideal health.

[0059] A health management system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a disposition unit, and a presentation unit. The collection unit collects health condition values ​​entered by a user. For example, the collection unit can collect values ​​such as weight, blood pressure, and blood glucose level entered by the user. The collection unit can also automatically collect health condition values ​​through a wearable device or a smartphone app. For example, the collection unit can collect data such as heart rate and step count from a wearable device. The collection unit can also collect values ​​manually entered by the user. For example, the collection unit can collect weight and blood pressure values ​​entered by the user into an app. The analysis unit analyzes the values ​​collected by the collection unit to evaluate the user's health condition. For example, the analysis unit can evaluate the user's health condition based on the collected values ​​such as weight, blood pressure, and blood glucose level. The analysis unit can also track changes in the user's health condition based on the collected data. For example, the analysis unit can compare the collected data with past data to evaluate whether the user's weight is increasing or decreasing. The analysis unit can also evaluate the user's health risk based on the collected data. For example, the analysis unit can assess the risk of high blood pressure for a user with high blood pressure. The suggestion unit proposes an optimal meal plan to the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose a calorie-restricted meal plan to a user who is gaining weight. The suggestion unit can also propose a low-salt meal plan to a user with high blood pressure. The suggestion unit can also propose a meal plan according to the user's health goals. For example, the suggestion unit can propose a low-calorie meal plan to a user who is trying to lose weight. The arrangement unit arranges necessary ingredients based on the meal plan proposed by the proposal unit. For example, the arrangement unit can order ingredients needed for the proposed meal plan online. The arrangement unit can also arrange the necessary ingredients using a supermarket or food delivery service near the user. The arrangement unit can also arrange ingredients according to the user's preferences. For example, the arrangement unit can arrange organic ingredients that the user prefers.The presentation unit presents specific cooking methods based on the meal plan proposed by the proposal unit. For example, the presentation unit can display specific recipes and cooking steps. The presentation unit can also show cooking methods in the form of videos. The presentation unit can also present the time and tools required for cooking. For example, the presentation unit can display the time required for cooking and the cooking utensils to be used. This allows the user to easily prepare healthy meals. As a result, the health management system according to the embodiment can propose an optimal meal plan based on the user's health condition, arrange ingredients, and present cooking methods, thereby bringing the user closer to an ideal health state.

[0060] The collection unit can estimate the user's emotions and adjust the timing of collecting health status values ​​based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects health status values ​​during a relaxed time period. For example, if the user is relaxed, the collection unit can collect health status values ​​at a normal collection timing. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing and collect health status values ​​quickly. For example, if the user is in a hurry, the collection unit can collect health status values ​​in a short time. This allows the health status values ​​to be collected at a more appropriate timing by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0061] The collection unit can analyze the user's past health data and select an appropriate collection method. For example, the collection unit selects a collection method that will obtain the most accurate data from the user's past health data. For example, the collection unit can adjust the collection frequency based on the user's past health data. The collection unit can also analyze the user's past health data and select a collection method for a specific time period. For example, the collection unit can select a method for collecting health status values ​​for a specific time period based on the user's past health data. This allows for more accurate data to be collected by selecting the optimal collection method based on the past health data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health data into the generation AI and have the generation AI select the optimal collection method.

[0062] When collecting the health status values, the collection unit can filter them based on the user's current living situation or activity level. For example, if the user is exercising, the collection unit collects the health status values ​​after the user exercises. For example, if the user is resting, the collection unit can collect the health status values ​​after the user rests. Furthermore, if the user is working, the collection unit can collect the health status values ​​after work. For example, if the user is working, the collection unit can collect the health status values ​​after work. This allows for more appropriate data to be collected by filtering the data according to the user's living situation or activity level. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity data into a generation AI and have the generation AI perform filtering.

[0063] The collection unit can estimate the user's emotions and determine the priority of the health status values ​​to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related values. For example, if the user is relaxed, the collection unit can collect overall health status values ​​evenly. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important values. For example, if the user is in a hurry, the collection unit can prioritize collecting only important values. This allows important data to be collected preferentially by determining the priority of the values ​​to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] When collecting health status values, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit prioritizes collecting health status values ​​specific to high altitudes. For example, when the user is in an urban area, the collection unit can prioritize collecting health status values ​​specific to urban areas. Furthermore, when the user is traveling, the collection unit can also prioritize collecting health status values ​​related to the environment of the travel destination. For example, when the user is traveling, the collection unit can prioritize collecting health status values ​​related to the environment of the travel destination. In this way, by taking the user's geographical location information into account, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant data.

[0065] When collecting health status values, the collection unit can analyze the user's social media activity and collect related data. For example, if the user posts about health on social media, the collection unit collects values ​​related to the content. For example, if the user posts about exercise on social media, the collection unit can collect values ​​related to the content. Furthermore, if the user posts about diet on social media, the collection unit can also collect values ​​related to the content. For example, if the user posts about diet on social media, the collection unit can collect values ​​related to the content. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0066] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, if the user is in a hurry, the analysis unit can provide a summary analysis result in a short time. This allows for adjusting the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health condition. For example, the analysis unit performs a detailed analysis on important health condition values. For example, the analysis unit can perform a simplified analysis on less important health condition values. The analysis unit can also gradually adjust the level of detail of the analysis depending on the importance of the health condition. For example, the analysis unit can determine whether to perform a detailed analysis or a simplified analysis depending on the importance of the health condition. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis depending on the importance of the health condition. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health condition data to a generation AI and cause the generation AI to adjust the level of detail of the analysis depending on the importance.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the health condition. For example, the analysis unit can apply an analysis algorithm specialized for weight management to values ​​related to weight. For example, the analysis unit can apply an analysis algorithm specialized for blood pressure management to values ​​related to blood glucose levels. Furthermore, the analysis unit can also apply an analysis algorithm specialized for blood glucose level management to values ​​related to blood glucose levels. For example, the analysis unit can apply an analysis algorithm specialized for blood glucose level management to values ​​related to blood glucose levels. This allows for applying an appropriate analysis algorithm depending on the classification of the health condition, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health condition data into the generation AI and cause the generation AI to apply an analysis algorithm depending on the classification.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] During analysis, the analysis unit can determine the priority of analysis based on when the health status values ​​were acquired. For example, the analysis unit prioritizes analysis of recently acquired health status values. For example, the analysis unit can prioritize the most recent values ​​while referring to health status values ​​acquired in the past. The analysis unit can also gradually adjust the priority of analysis depending on when the health status values ​​were acquired. For example, the analysis unit can gradually adjust the priority of analysis depending on when the health status values ​​were acquired. This enables analysis that prioritizes the most recent data by determining the priority of analysis based on when the health status values ​​were acquired. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input health status data into a generation AI and have the generation AI determine the priority of analysis based on the time of acquisition.

[0071] During analysis, the analysis unit can adjust the order of analysis based on the relationships between health conditions. For example, the analysis unit prioritizes analysis of health condition values ​​that are highly correlated. For example, the analysis unit can postpone analysis of health condition values ​​that are less correlated. The analysis unit can also gradually adjust the order of analysis according to the correlation between health conditions. For example, the analysis unit can gradually adjust the order of analysis according to the correlation between health conditions. In this way, by adjusting the order of analysis based on the correlation between health conditions, highly correlated data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health condition data to a generation AI and cause the generation AI to adjust the order of analysis based on the relationships.

[0072] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. For example, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points in a short amount of time. This allows for adjusting the way the suggestions are expressed based on the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the meal plan. For example, the proposal unit can make a detailed proposal for an important meal plan. For example, the proposal unit can make a simplified proposal for a less important meal plan. The proposal unit can also gradually adjust the level of detail of the proposal depending on the importance of the meal plan. For example, the proposal unit can determine whether to make a detailed proposal or a simplified proposal depending on the importance of the meal plan. As a result, important plans can be proposed in detail by adjusting the level of detail of the proposal depending on the importance of the meal plan. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input meal plan data into a generation AI and cause the generation AI to adjust the level of detail of the proposal depending on the importance.

[0074] When making a proposal, the proposal unit can apply different proposal algorithms depending on the classification of the meal plan. For example, for a meal plan related to dieting, the proposal unit can apply a proposal algorithm specialized for dieting. For example, for a meal plan related to high blood pressure, the proposal unit can apply a proposal algorithm specialized for high blood pressure management. Furthermore, for a meal plan related to diabetes, the proposal unit can apply a proposal algorithm specialized for diabetes management. For example, for a meal plan related to diabetes, the proposal unit can apply a proposal algorithm specialized for diabetes management. This allows for more accurate proposals to be provided by applying an appropriate proposal algorithm depending on the classification of the meal plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input meal plan data into a generation AI and cause the generation AI to apply a proposal algorithm depending on the classification.

[0075] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for adjusting the length of the suggestions according to the user's emotions to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the meal plan. For example, the proposal unit can prioritize the most recently submitted meal plan. For example, the proposal unit can refer to previously submitted meal plans and emphasize the most recent plan. The proposal unit can also gradually adjust the priority of the proposal depending on the time of submission of the meal plan. For example, the proposal unit can gradually adjust the priority of the proposal depending on the time of submission of the meal plan. This enables proposals that emphasize the most recent plan by determining the priority of the proposal based on the time of submission of the meal plan. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or without AI. For example, the proposal unit can input meal plan data into a generation AI and cause the generation AI to determine the priority of the proposal based on the time of submission.

[0077] The suggestion unit can adjust the order of suggestions based on the relationships between meal plans when suggesting them. For example, the suggestion unit prioritizes suggesting meal plans that are highly related to each other. For example, the suggestion unit can postpone suggesting meal plans that are less related. The suggestion unit can also gradually adjust the order of suggestions based on the relevance of the meal plans. For example, the suggestion unit can gradually adjust the order of suggestions based on the relevance of the meal plans. As a result, by adjusting the order of suggestions based on the relevance of the meal plans, highly related plans can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input meal plan data into a generation AI and cause the generation AI to adjust the order of suggestions based on the relationships.

[0078] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user emotions. For example, if the user is nervous, the dispatch unit can provide a simple and highly visible dispatch method. For example, if the user is relaxed, the dispatch unit can provide a detailed dispatch method. Furthermore, if the user is in a hurry, the dispatch unit can provide a dispatch method that focuses on the main points. For example, if the user is in a hurry, the dispatch unit can provide a dispatch method that focuses on the main points in a short amount of time. This allows the dispatch method to be adjusted according to the user's emotions, thereby providing a more appropriate dispatch method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dispatch unit may be performed using AI, for example, or without AI. For example, the dispatch unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0079] When making arrangements, the arrangement unit can analyze the user's past ingredient purchase history and select an appropriate arrangement method. The arrangement unit, for example, selects the most efficient arrangement method from the user's past ingredient purchase history. For example, the arrangement unit can adjust the arrangement frequency based on the user's past ingredient purchase history. The arrangement unit can also analyze the user's past ingredient purchase history and select an arrangement method for a specific time period. For example, the arrangement unit can select a method for arranging ingredients for a specific time period based on the user's past ingredient purchase history. This enables efficient arrangements by selecting the optimal arrangement method based on the past ingredient purchase history. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can input the user's past ingredient purchase history into the generation AI and have the generation AI select the optimal arrangement method.

[0080] The arrangement unit can adjust the means of arrangement based on the user's current living situation when making arrangements. For example, if the user is busy, the arrangement unit provides a means for making arrangements quickly. For example, if the user is relaxed, the arrangement unit can provide detailed arrangement means. Furthermore, if the user is traveling, the arrangement unit can also provide arrangement means at the travel destination. For example, if the user is traveling, the arrangement unit can provide arrangement means at the travel destination. This enables more appropriate arrangements by customizing the means of arrangement according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the arrangement unit can input the user's living situation data into a generation AI and have the generation AI adjust the means.

[0081] The arrangement unit can estimate the user's emotions and determine the order of arrangement based on the estimated user's emotions. For example, if the user is feeling stressed, the arrangement unit prioritizes arrangement of ingredients that are useful for stress reduction. For example, if the user is relaxed, the arrangement unit can arrange ingredients at a normal arrangement priority. Furthermore, if the user is in a hurry, the arrangement unit can prioritize arrangement of ingredients that can be arranged quickly. For example, if the user is in a hurry, the arrangement unit can prioritize arrangement of ingredients that can be arranged quickly. In this way, by determining the order of arrangement priorities according to the user's emotions, important ingredients can be arranged preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the arrangement unit may be performed using, for example, an AI. For example, the arrangement unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When making arrangements, the arrangement unit can select an appropriate arrangement method based on the user's geographical location information. For example, if the user is in an urban area, the arrangement unit selects an arrangement method specific to urban areas. For example, if the user is in the suburbs, the arrangement unit can select an arrangement method specific to suburbs. Furthermore, if the user is traveling, the arrangement unit can also select an arrangement method for the travel destination. For example, if the user is traveling, the arrangement unit can select an arrangement method for the travel destination. This makes it possible to provide the optimal arrangement method by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the arrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the arrangement unit can input the user's geographical location data into the generation AI and have the generation AI select an appropriate arrangement method.

[0083] When making arrangements, the arrangement unit can analyze the user's social media activity and suggest arrangement methods. For example, if the user mentions specific ingredients on social media, the arrangement unit arranges those ingredients. For example, if the user mentions a specific dish on social media, the arrangement unit can arrange the ingredients needed for that dish. Furthermore, if the user mentions a specific meal plan on social media, the arrangement unit can also arrange the ingredients needed for that plan. For example, if the user mentions a specific meal plan on social media, the arrangement unit can arrange the ingredients needed for that plan. In this way, by analyzing social media activity, related ingredients can be arranged. Some or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can input the user's social media data into a generation AI and have the generation AI suggest means.

[0084] The presentation unit can estimate the user's emotions and adjust the presentation method of the cooking recipes based on the estimated user emotions. For example, if the user is nervous, the presentation unit can present simple, highly visible cooking recipes. For example, if the user is relaxed, the presentation unit can present detailed cooking recipes. Furthermore, if the user is in a hurry, the presentation unit can present cooking recipes that focus on the key points. For example, if the user is in a hurry, the presentation unit can present cooking recipes that focus on the key points in a short amount of time. This allows for adjusting the presentation method of the cooking recipes according to the user's emotions, thereby providing more appropriate cooking recipes. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When presenting a cooking method, the presentation unit can select an appropriate presentation method by referring to the user's past cooking history. The presentation unit, for example, presents the most efficient cooking method based on the user's past cooking history. For example, the presentation unit can simplify cooking procedures based on the user's past cooking history. The presentation unit can also refer to the user's past cooking history and preferentially present a specific cooking method. For example, the presentation unit can preferentially present a specific cooking method based on the user's past cooking history. This enables efficient cooking by selecting the optimal presentation method based on the past cooking history. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's past cooking history data into the generation AI and cause the generation AI to select an appropriate presentation method.

[0086] When presenting a cooking method, the presentation unit can adjust the presentation means based on the user's current living situation. For example, if the user is busy, the presentation unit can present a quick cooking method. For example, if the user is relaxed, the presentation unit can present detailed cooking steps. Furthermore, if the user is traveling, the presentation unit can also present cooking methods at the travel destination. For example, if the user is traveling, the presentation unit can present cooking methods at the travel destination. This allows the presentation means to be customized according to the user's living situation, making it possible to provide a more appropriate cooking method. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the user's living situation data into the generation AI and cause the generation AI to adjust the means.

[0087] The presentation unit can estimate the user's emotions and prioritize cooking methods based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit can prioritize cooking methods that help relieve stress. For example, if the user is relaxed, the presentation unit can present cooking methods with a normal priority. Furthermore, if the user is in a hurry, the presentation unit can prioritize cooking methods that allow for quick cooking. For example, if the user is in a hurry, the presentation unit can prioritize cooking methods that allow for quick cooking. This allows important cooking methods to be prioritized by prioritizing cooking methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] When presenting cooking methods, the presentation unit can select an appropriate presentation method based on the user's geographical location information. For example, if the user is in an urban area, the presentation unit can present cooking methods specific to urban areas. For example, if the user is in the suburbs, the presentation unit can present cooking methods specific to suburban areas. Furthermore, if the user is traveling, the presentation unit can also present cooking methods at the travel destination. For example, if the user is traveling, the presentation unit can present cooking methods at the travel destination. This makes it possible to provide optimal cooking methods by taking the user's geographical location information into consideration. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's geographical location data into the generation AI and cause the generation AI to select an appropriate presentation method.

[0089] When presenting a cooking method, the presentation unit can analyze the user's social media activity and suggest a means to present it. For example, if the user mentions a specific dish on social media, the presentation unit can present a cooking method for that dish. For example, if the user mentions specific ingredients on social media, the presentation unit can present a cooking method using those ingredients. Furthermore, if the user mentions a specific cooking method on social media, the presentation unit can preferentially present that cooking method. For example, if the user mentions a specific cooking method on social media, the presentation unit can preferentially present that cooking method. In this way, related cooking methods can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's social media data into the generation AI and cause the generation AI to suggest a means. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, arrangement unit, and presentation 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 numerical values ​​of the user's health status using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health status. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal meal plan based on the analysis results. The arrangement unit, for example, is realized by the control unit 46A of the smart device 14 and orders necessary ingredients online based on the proposed meal plan. The presentation unit, for example, displays specific cooking methods using the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, arrangement unit, and presentation 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 numerical values ​​of the user's health condition using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal meal plan based on the analysis results. The arrangement unit is realized, for example, by the control unit 46A of the smart glasses 214 and orders necessary ingredients online based on the proposed meal plan. The presentation unit displays specific cooking methods using, for example, the display or speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, arrangement unit, and presentation unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects numerical values ​​of the user's health condition using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal meal plan based on the analysis results. The arrangement unit, for example, is realized by the control unit 46A of the headset terminal 314 and orders necessary ingredients online based on the proposed meal plan. The presentation unit, for example, displays specific cooking methods using the display 343 or speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, arrangement unit, and presentation 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 numerical values ​​of the user's health status using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health status. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal meal plan based on the analysis results. The arrangement unit is realized, for example, by the control unit 46A of the robot 414 and orders the necessary ingredients online based on the proposed meal plan. The presentation unit displays specific cooking methods using, for example, the display and speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The health management system can further collect the user's sleep data and analyze it with an analysis unit. For example, the collection unit can collect the user's sleep time and sleep quality. The analysis unit can evaluate the user's health condition based on the collected sleep data and propose an optimal meal plan. For example, a meal plan suitable for replenishing energy can be proposed to a sleep-deprived user. The arrangement unit can also arrange ingredients with a relaxing effect based on the user's sleep data. This allows for more comprehensive health management by taking the user's sleep data into consideration.

[0092] The collection unit can estimate the user's emotions and adjust the timing of collecting health condition values ​​based on the estimated user emotions. For example, if the user is feeling stressed, health condition values ​​can be collected during a relaxed time period. Also, if the user is in a hurry, the collection timing can be shortened to collect health condition values ​​quickly. In this way, by adjusting the collection timing according to the user's emotions, health condition values ​​can be collected at more appropriate times.

[0093] The analysis unit can analyze the user's past health data and select an appropriate analysis method. For example, it can select an analysis method that will obtain the most accurate data from the user's past health data. The analysis unit can also adjust the frequency of analysis based on the user's past health data. This allows for more accurate data analysis by selecting the optimal analysis method based on the user's past health data.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. On the other hand, if the user is relaxed, it can provide detailed analysis results. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide more appropriate analysis results.

[0095] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, simple and highly visible suggestions can be provided. Also, if the user is relaxed, detailed suggestions can be provided. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be provided.

[0096] The arrangement unit can analyze the user's past ingredient purchase history and select an appropriate arrangement method. For example, the most efficient arrangement method can be selected from the user's past ingredient purchase history. The arrangement unit can also adjust the arrangement frequency based on the user's past ingredient purchase history. This allows for efficient arrangement by selecting the optimal arrangement method based on the user's past ingredient purchase history.

[0097] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible dispatch method can be provided. Also, if the user is relaxed, a detailed dispatch method can be provided. In this way, by adjusting the dispatch method according to the user's emotions, a more appropriate dispatch method can be provided.

[0098] The presentation unit can select an appropriate presentation method by referring to the user's past cooking history. For example, the presentation unit can present the most efficient cooking method based on the user's past cooking history. The presentation unit can also simplify the cooking procedure based on the user's past cooking history. This allows for efficient cooking by selecting the optimal presentation method based on the user's past cooking history.

[0099] The presentation unit can estimate the user's emotions and adjust the presentation method of cooking methods based on the estimated user emotions. For example, if the user is nervous, simple and highly visible cooking methods can be presented. On the other hand, if the user is relaxed, detailed cooking methods can be presented. In this way, by adjusting the presentation method of cooking methods according to the user's emotions, more appropriate cooking methods can be provided.

[0100] The presentation unit can select an appropriate presentation method based on the user's geographical location information. For example, if the user is in an urban area, cooking methods specific to urban areas can be presented. Also, if the user is in a suburban area, cooking methods specific to suburban areas can be presented. In this way, the optimal cooking method can be provided by taking the user's geographical location information into consideration.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects health condition values ​​entered by the user. For example, the collection unit can collect values ​​such as weight, blood pressure, and blood sugar level entered by the user. The collection unit can also collect health condition values ​​automatically through a wearable device or a smartphone app. For example, the collection unit can collect data such as heart rate and number of steps from a wearable device. The collection unit can also collect values ​​manually entered by the user. For example, the collection unit can collect weight and blood pressure values ​​entered by the user into an app. Step 2: The analysis unit analyzes the numerical values ​​collected by the collection unit and evaluates the user's health condition. For example, the analysis unit can evaluate the user's health condition based on collected numerical values ​​such as weight, blood pressure, and blood sugar level. The analysis unit can also track changes in the user's health condition based on the collected data. For example, the analysis unit can compare the user's weight with past data to evaluate whether the user is gaining or losing weight. Furthermore, the analysis unit can evaluate the user's health risk based on the collected data. For example, the analysis unit can evaluate the risk of high blood pressure for a user with high blood pressure. Step 3: The suggestion unit suggests an optimal meal plan to the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit can suggest a calorie-restricted meal plan to a user who is gaining weight. The suggestion unit can also suggest a low-salt meal plan to a user who has high blood pressure. Furthermore, the suggestion unit can also suggest a meal plan according to the user's health goals. For example, the suggestion unit can suggest a low-calorie meal plan to a user who is trying to lose weight. Step 4: The arrangement unit arranges the necessary ingredients based on the meal plan proposed by the proposal unit. For example, the arrangement unit can order the ingredients necessary for the proposed meal plan online. The arrangement unit can also arrange the necessary ingredients using a supermarket or food delivery service near the user. Furthermore, the arrangement unit can arrange ingredients according to the user's preferences. For example, the arrangement unit can arrange organic ingredients that the user prefers. Step 5: The presentation unit presents specific cooking methods based on the meal plan proposed by the proposal unit. For example, the presentation unit can display specific recipes and cooking steps. The presentation unit can also show cooking methods in the form of videos. Furthermore, the presentation unit can present the time and tools required for cooking. For example, the presentation unit can display the time required for cooking and the cooking utensils to be used. This allows the user to easily prepare healthy meals.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] 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.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 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 that collects health status values; an analysis unit that analyzes the numerical values ​​collected by the collection unit; a suggestion unit that proposes an appropriate meal plan based on the analysis results obtained by the analysis unit; an arrangement unit that arranges necessary ingredients based on the meal plan proposed by the proposal unit; a presentation unit that presents cooking methods based on the meal plan proposed by the proposal unit; Equipped with A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of collecting health status values ​​based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze users' past health data and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit When collecting health metrics, filter them based on the user's current lifestyle or activity level.

2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and prioritize the health status values ​​to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit When collecting health metrics, prioritize the collection of relevant data based on the user's geographic location.

2. The system of claim 1.

7. The collecting unit When collecting health statistics, analyze users' social media activity and collect related data.

2. The system of claim 1.

8. The analysis unit Estimate user emotions and adjust the analysis presentation based on the estimated user emotions.

2. The system of claim 1.

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