system

Abstract

The system according to the embodiment aims to analyze the nutrients in meals and support health management. [Solution] The system according to the embodiment comprises an analysis unit, an analysis unit, a proposal unit, an advertising unit, a trend analysis unit, and an alarm unit. The analysis unit analyzes photos of meals. The analysis unit analyzes nutrients based on the information on meals and ingredients analyzed by the analysis unit. The proposal unit proposes future menus based on the analysis results obtained by the analysis unit. The advertising unit displays advertisements related to the menus proposed by the proposal unit. The trend analysis unit analyzes disease trends based on data on hospital visit history, medical history, and health checkups. The alarm unit issues an alarm based on the analysis results obtained by the trend analysis 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 make it difficult to efficiently analyze dietary nutrients and manage health, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the nutrients in meals and support health management. [Means for solving the problem]

[0006] The system according to the embodiment comprises an analysis unit, an analysis unit, a proposal unit, an advertising unit, a trend analysis unit, and an alarm unit. The analysis unit analyzes photos of meals. The analysis unit analyzes nutrients based on the information on meals and ingredients analyzed by the analysis unit. The proposal unit proposes future menus based on the analysis results obtained by the analysis unit. The advertising unit displays advertisements related to the menus proposed by the proposal unit. The trend analysis unit analyzes disease trends based on data on hospital visit history, medical history, and health checkups. The alarm unit issues an alarm based on the analysis results obtained by the trend analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the nutrients in meals and support health management. [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 support system according to an embodiment of the present invention provides dietary health support and household chore support through meal plan suggestions. In this health support system, users submit photos of their daily meals, which AI analyzes to identify the meals and ingredients. If the analysis results are inaccurate, the user can correct them as needed. Next, the AI ​​analyzes the nutritional information based on the analyzed meal and ingredient information and suggests up to three future meal plans. Furthermore, the system displays support advertisements related to the proposed meal plans, such as delivery services and co-op home delivery. Furthermore, by pre-entering information about hospital visits, medical history, and health checkup results, the AI ​​can analyze disease trends and issue alarms as needed. For example, a user submits photos of their daily meals. To do this, the user takes photos of their meals and uploads them to the system. For example, the user takes photos of breakfast, lunch, and dinner and reports them to the system. This information is then input into the AI. The AI ​​then analyzes the input photos and identifies the meals and ingredients. The AI ​​uses image analysis technology to identify the meals and ingredients contained in the photos. For example, it identifies ingredients such as vegetables, meat, and fish in the photos and analyzes the nutrients of each. If the analysis results are inaccurate, the user can correct them as needed. Based on the analysis results, the AI ​​analyzes nutrients. Based on the identified meal and ingredient information, the AI ​​analyzes each nutrient. For example, it analyzes nutrients such as vitamins, minerals, protein, lipids, and carbohydrates to evaluate the user's nutritional balance. Next, the AI ​​suggests up to three future meal plans. Based on the nutrient analysis results, the AI ​​proposes optimal meals for the user. For example, it proposes breakfast, lunch, and dinner menus that take nutritional balance into consideration. It also displays support advertisements related to the proposed menus, such as delivery services and co-op delivery. In addition, by allowing users to enter their hospital history, medical history, and health checkup results in advance, the AI ​​analyzes disease trends. Based on the input information, the AI ​​evaluates the user's health condition and issues alarms as necessary. For example, it can detect abnormalities in blood pressure or blood sugar levels and issue alarms to alert the user. This supports the user's health through daily meals and reduces the burden of housework through meal suggestions. In this way, the health support system supports the user's health and reduces the burden of housework.

[0029] A health support system according to an embodiment includes an analysis unit, a suggestion unit, an advertising unit, a trend analysis unit, and an alarm unit. The analysis unit analyzes photos of meals. For example, the analysis unit inputs photos of meals taken by a user into an AI and identifies the meals and ingredients using image analysis technology. For example, the analysis unit identifies ingredients such as vegetables, meat, and fish appearing in the photos and analyzes the nutrients of each. The analysis unit identifies the meals and ingredients included in the photos using image recognition technology, for example. Furthermore, if the analysis unit finds any deficiencies in the analysis results, the user can correct them as appropriate. For example, the analysis unit can reanalyze the information corrected by the user to obtain accurate results. The analysis unit analyzes nutrients based on the information on the meals and ingredients analyzed by the analysis unit. For example, the analysis unit analyzes each nutrient based on the information on the identified meals and ingredients. For example, the analysis unit analyzes nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates, and evaluates the user's nutritional balance. For example, the analysis unit evaluates the user's nutritional balance based on the nutrient analysis results. The suggestion unit suggests future menus based on the analysis results obtained by the analysis unit. The suggestion unit suggests optimal menus for the user based on, for example, nutrient analysis results. For example, the suggestion unit suggests breakfast, lunch, and dinner menus that take nutritional balance into consideration. The suggestion unit suggests, for example, up to three future menus. The advertising unit displays advertisements related to the menus suggested by the suggestion unit. The advertising unit displays, for example, support advertisements for delivery services related to the proposed menus, such as co-op home delivery. The advertising unit displays, for example, advertisements related to the user. The trend analysis unit analyzes disease trends based on data on hospital visit history, medical history, and health checkups. The trend analysis unit analyzes disease trends based on, for example, hospital visit history, medical history, and health checkup results entered by the user. The trend analysis unit evaluates the user's health condition based on, for example, the entered information. The alarm unit issues an alarm based on the analysis results obtained by the trend analysis unit. The alarm unit can detect abnormalities in blood pressure or blood sugar levels, and issue an alarm to alert the user. The alarm unit issues an alarm based on, for example, the user's health condition. As a result, the health support system according to the embodiment can support the user's health and reduce the burden of housework.

[0030] When analyzing food photos, the analysis unit can apply correction algorithms to accommodate different lighting conditions and shooting angles. For example, when lighting is low, the analysis unit automatically adjusts the brightness of the image to improve analysis accuracy. The analysis unit can also correct image distortion to perform accurate analysis when the shooting angle is oblique, for example. The analysis unit can also adjust the contrast of the image to make it easier to identify ingredients when lighting is too strong, for example. This improves analysis accuracy by adapting to different lighting conditions and shooting angles. The correction algorithm is realized using techniques such as lighting correction, angle correction, and color correction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on lighting conditions and shooting angles into the generation AI and have the generation AI apply the correction algorithm.

[0031] When analyzing a photo of a meal, the analysis unit can acquire additional information to identify the freshness of ingredients and cooking methods. The analysis unit, for example, estimates freshness from the color and texture of ingredients and reflects this in the analysis results. The analysis unit can also identify the cooking method (grilling, boiling, frying, etc.) and consider changes in nutrients based on this. The analysis unit can also identify cooking details, for example, from how ingredients are cut and presented, and reflect this in the analysis results. This improves the accuracy of the analysis results by identifying the freshness of ingredients and cooking methods. Acquisition of additional information is achieved using technology such as ingredient freshness information and cooking method data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the freshness of ingredients and cooking methods into the generation AI and cause the generation AI to acquire the additional information.

[0032] When analyzing food photos, the analysis unit can improve the accuracy of the analysis by referring to the user's past meal history. For example, the analysis unit can easily identify similar ingredients based on data on meals the user has eaten in the past. The analysis unit can also, for example, prioritize frequently used ingredients for analysis based on the user's past meal history. The analysis unit can also, for example, analyze the user's past meal patterns to improve the accuracy of the analysis results. By referring to the past meal history, the analysis accuracy is improved. Referencing the past meal history is achieved using technologies such as database structure and search algorithms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past meal history data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0033] The analysis unit can take into account the user's geographical food culture when analyzing food photos. For example, the analysis unit prioritizes identifying ingredients and dishes unique to the user's region. The analysis unit can also customize the analysis results based on the user's geographical food culture. The analysis unit can also reflect ingredients and dishes related to the user's regional food culture in the analysis results. This makes the analysis results more accurate by taking geographical food culture into consideration. Consideration of geographical food culture can be achieved, for example, by techniques such as regional ingredients, cooking methods, and eating habits. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical food culture data into a generation AI and have the generation AI perform the analysis.

[0034] When analyzing nutrients, the analysis unit can evaluate nutritional value based on the origin and production method of the ingredients. The analysis unit can evaluate differences in nutritional value based on, for example, information about the origin of the ingredients. The analysis unit can also evaluate nutritional value based on, for example, the production method of the ingredients (organic cultivation, pesticide-free cultivation, etc.). The analysis unit can also evaluate changes in nutritional value by taking into account, for example, the distribution route of the ingredients. This enables more accurate nutrient analysis by evaluating nutritional value based on the origin and production method of the ingredients. Evaluation of the origin and production method of ingredients is achieved using, for example, techniques such as organic cultivation and regional specialty products. 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 data on the origin and production method of ingredients into the generation AI and have the generation AI evaluate nutritional value.

[0035] The analysis unit can provide analysis results by taking into account the user's allergy information when analyzing nutrients. For example, the analysis unit can identify ingredients containing allergens based on the user's allergy information and exclude them from the analysis results. The analysis unit can also suggest alternative ingredients by taking into account the user's allergy information. For example, the analysis unit can exclude nutrients from ingredients containing allergens from the analysis results based on the user's allergy information. This allows analysis results that do not contain allergens to be provided by taking into account the user's allergy information. Taking into account allergy information is achieved using technology such as the user's allergy history and medical data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's allergy information into the generation AI and cause the generation AI to provide analysis results based on the allergy information.

[0036] The analysis unit can evaluate nutritional balance based on the user's age and gender when analyzing nutrients. The analysis unit can evaluate the amount of nutrients needed based on the user's age, for example. The analysis unit can also evaluate nutritional balance based on the user's gender, for example. The analysis unit can also suggest an optimal nutritional balance, taking the user's age and gender into consideration, for example. This enables more appropriate nutrient analysis by evaluating nutritional balance based on the user's age and gender. The evaluation of nutritional balance based on age and gender can be achieved using techniques such as recommended nutrient amounts and nutritional standards for each age. 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 data on the user's age and gender into the generation AI and have the generation AI evaluate the nutritional balance.

[0037] When analyzing nutrients, the analysis unit can provide analysis results taking into account the user's amount of exercise and lifestyle habits. The analysis unit, for example, evaluates the amount of nutrients needed based on the user's amount of exercise. The analysis unit can also evaluate nutritional balance taking into account the user's lifestyle habits (such as meal times and frequency). The analysis unit can also propose an optimal nutritional balance taking into account the user's amount of exercise and lifestyle habits. This enables more appropriate nutrient analysis by taking into account the user's amount of exercise and lifestyle habits. Consideration of the amount of exercise and lifestyle habits can be achieved using techniques such as exercise frequency, daily rhythm, and eating habits. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the user's amount of exercise and lifestyle habits into the generation AI and have the generation AI provide the analysis results.

[0038] When proposing a menu, the suggestion unit can suggest an optimal menu by referring to the user's past meal history. The suggestion unit, for example, proposes a menu that takes nutritional balance into consideration based on the user's past meal history. The suggestion unit can also propose a menu that includes the user's favorite ingredients based on the user's past meal history. The suggestion unit can also propose an optimal menu by analyzing the user's past meal patterns, for example. This makes it possible to suggest a more appropriate menu by referring to the past meal history. Referencing the past meal history is achieved by techniques such as a database structure and a search algorithm. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past meal history data into a generation AI and have the generation AI suggest an optimal menu.

[0039] When proposing a menu, the suggestion unit can make the suggestion taking into consideration ingredients according to the season and weather. The suggestion unit, for example, proposes a menu using seasonal ingredients. The suggestion unit can also propose hot or cold dishes according to the weather, for example. The suggestion unit can also propose a menu that takes nutritional balance into consideration based on the season and weather, for example. This makes it possible to propose a more appropriate menu by taking into consideration ingredients according to the season and weather. Consideration of ingredients according to the season and weather is achieved by, for example, techniques such as seasonal ingredients and seasonal nutritional values. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the season and weather into the generation AI and cause the generation AI to execute a menu suggestion.

[0040] The suggestion unit can make a menu suggestion taking into consideration the user's geographical food culture. For example, the suggestion unit can suggest a menu including ingredients and dishes unique to the user's region. The suggestion unit can also suggest an optimal menu based on the user's geographical food culture. The suggestion unit can also suggest ingredients and dishes related to the user's regional food culture. This allows for more appropriate menu suggestions by taking geographical food culture into consideration. Consideration of geographical food culture is achieved, for example, by techniques such as regional ingredients, cooking methods, and eating habits. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical food culture data into a generation AI and cause the generation AI to suggest a menu.

[0041] The suggestion unit can make a suggestion while taking into consideration the user's budget and the availability of ingredients when proposing a menu. The suggestion unit, for example, suggests a menu using ingredients according to the user's budget. The suggestion unit can also suggest a menu using ingredients available in the user's area, for example. The suggestion unit can also suggest an optimal menu by taking into consideration the user's budget and the availability of ingredients. This makes it possible to suggest a more realistic menu by taking into consideration the user's budget and the availability of ingredients. Consideration of the budget and the availability of ingredients can be achieved, for example, by using technology such as the price of ingredients, the time and place when they are available, etc. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the budget and the availability of ingredients into the generation AI and have the generation AI execute the menu suggestion.

[0042] When displaying an advertisement, the advertising unit can display a highly relevant advertisement by referring to the user's past purchase history. The advertising unit, for example, displays advertisements related to products the user has previously purchased. The advertising unit can also display advertisements that suggest products that the user may be interested in based on the user's past purchase history. The advertising unit can also analyze the user's purchase history and preferentially display highly relevant advertisements. This allows more relevant advertisements to be displayed by referring to the past purchase history. The reference to the past purchase history is achieved using technologies such as a database structure and a search algorithm. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without using AI. For example, the advertising unit can input past purchase history data into a generation AI and cause the generation AI to display highly relevant advertisements.

[0043] The advertising unit can customize advertisements based on the user's current lifestyle and areas of interest when displaying advertisements. For example, the advertising unit displays relevant advertisements based on the user's current lifestyle (e.g., parenting, student, etc.). The advertising unit can also customize advertisements based on the user's areas of interest (e.g., health, fitness, etc.). The advertising unit can also display optimal advertisements, for example, taking into account the user's lifestyle and areas of interest. This enables more effective advertisement display by customizing advertisements based on the user's current lifestyle and areas of interest. Consideration of the current lifestyle and areas of interest can be achieved using techniques such as daily rhythms, hobbies, and interests. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without AI. For example, the advertising unit can input data on the user's current lifestyle and areas of interest into a generation AI and have the generation AI customize the advertisement.

[0044] The advertising unit can display highly relevant advertisements by taking into account the user's geographical location information when displaying advertisements. For example, the advertising unit can display advertisements for nearby stores and services based on the user's current location. The advertising unit can also display region-specific advertisements based on the user's geographical location information. The advertising unit can also display optimal advertisements by taking into account the user's location information. This allows more relevant advertisements to be displayed by taking into account the geographical location information. Taking into account the geographical location information is achieved using technologies such as GPS data and location information services. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without using AI. For example, the advertising unit can input geographical location information data into a generation AI and cause the generation AI to display highly relevant advertisements.

[0045] When displaying an advertisement, the advertising unit can analyze the user's social media activity and display relevant advertisements. The advertising unit, for example, customizes advertisements based on the user's social media interests. The advertising unit can also analyze the user's social media activity history and display highly relevant advertisements. The advertising unit can also display optimal advertisements based on the user's following and likes on social media. This allows for more relevant advertisements to be displayed by analyzing social media activity. Analysis of social media activity is achieved using techniques such as post content, number of likes, and number of followers. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without AI. For example, the advertising unit can input social media activity data into a generation AI and cause the generation AI to display relevant advertisements.

[0046] During the trend analysis, the trend analysis unit can perform analysis based on the user's family history and genetic information. For example, the trend analysis unit can evaluate the risk of a genetic disease based on the user's family history. The trend analysis unit can also analyze trends in specific diseases based on the user's genetic information. The trend analysis unit can also propose an optimal health management plan, for example, by taking the user's family history and genetic information into consideration. This enables more accurate disease trend analysis by taking the family history and genetic information into consideration. Taking the family history and genetic information into consideration can be achieved using techniques such as family medical history and genetic test results. Some or all of the above-described processing in the trend analysis unit can be performed using, for example, AI, or without AI. For example, the trend analysis unit can input data on the family history and genetic information into the generation AI and have the generation AI perform the analysis.

[0047] The trend analysis unit can perform trend analysis by taking into account the user's lifestyle habits and environmental factors. The trend analysis unit can analyze disease trends by taking into account, for example, the user's lifestyle habits (e.g., smoking, drinking, etc.). The trend analysis unit can also evaluate health risks based on the user's environmental factors (e.g., living environment, work environment, etc.). The trend analysis unit can also propose an optimal health management plan by taking into account the user's lifestyle habits and environmental factors. This enables more accurate disease trend analysis by taking into account lifestyle habits and environmental factors. Consideration of lifestyle habits and environmental factors can be achieved using techniques such as exercise frequency, eating habits, and living environment. Some or all of the above-mentioned processing in the trend analysis unit can be performed using, for example, AI, or without AI. For example, the trend analysis unit can input data on lifestyle habits and environmental factors into a generation AI and have the generation AI perform the analysis.

[0048] The trend analysis unit can improve the accuracy of the trend analysis by referring to the user's past health checkup data. For example, the trend analysis unit evaluates the user's current health condition based on the user's past health checkup data. The trend analysis unit can also analyze changes in health risks based on the user's past health checkup data. For example, the trend analysis unit can also propose an optimal health management plan by referring to the user's past health checkup data. This enables more accurate trend analysis by referring to past health checkup data. Referencing past health checkup data is achieved using techniques such as diagnostic results, test items, and test dates. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or without AI. For example, the trend analysis unit can input past health checkup data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0049] During trend analysis, the trend analysis unit can perform analysis based on the user's geographical environmental factors. The trend analysis unit can evaluate health risks, for example, by taking into account the user's living environment (e.g., urban area, rural area, etc.). The trend analysis unit can also analyze health risks based on the user's work environment (e.g., office, factory, etc.). The trend analysis unit can also propose an optimal health management plan, for example, by taking into account the user's geographical environmental factors. This enables more accurate trend analysis by taking into account geographical environmental factors. Consideration of geographical environmental factors can be achieved using techniques such as the local climate and the level of environmental pollution. Some or all of the above-described processing in the trend analysis unit can be performed using, for example, AI, or without AI. For example, the trend analysis unit can input data on geographical environmental factors into a generation AI and have the generation AI perform the analysis.

[0050] When displaying an alarm, the alarm unit can improve the accuracy of the alarm by referring to the user's past health data. The alarm unit, for example, evaluates current health risks based on the user's past health data. The alarm unit can also analyze changes in health risks from the user's past health data. The alarm unit can also display an optimal alarm by referring to the user's past health data. This enables more accurate alarm display by referencing past health data. Referencing past health data is achieved using techniques such as diagnosis results, test items, and test dates. Some or all of the above-described processing in the alarm unit may be performed using, for example, AI, or may be performed without AI. For example, the alarm unit can input past health data into a generation AI and have the generation AI improve the accuracy of the alarm.

[0051] The alarm unit can customize the alarm by taking into account the user's current living situation and environmental factors when displaying the alarm. The alarm unit customizes the alarm based on, for example, the user's current living situation (e.g., childcare, student, etc.). The alarm unit can also adjust the alarm based on, for example, the user's environmental factors (e.g., living environment, work environment, etc.). The alarm unit can also display an optimal alarm by taking into account the user's living situation and environmental factors. This enables a more appropriate alarm display by taking into account the current living situation and environmental factors. Consideration of the current living situation and environmental factors can be achieved by techniques such as lifestyle rhythm, living environment, and work situation. Some or all of the above-mentioned processing in the alarm unit may be performed using, for example, AI, or may be performed without using AI. For example, the alarm unit can input data on the user's current living situation and environmental factors into a generation AI and have the generation AI customize the alarm.

[0052] The alarm unit can display the alarm taking into consideration the user's geographical location information when displaying the alarm. The alarm unit, for example, displays a relevant alarm based on the user's current location. The alarm unit can also display an optimal alarm based on the user's geographical location information. The alarm unit can also adjust the alarm display content, for example, taking into consideration the user's location information. This enables a more appropriate alarm display by taking into consideration the geographical location information. Consideration of the geographical location information is achieved, for example, by technologies such as GPS data and location information services. Some or all of the above-described processing in the alarm unit may be performed using, for example, AI, or may be performed without using AI. For example, the alarm unit can input geographical location information data to a generation AI and cause the generation AI to display the alarm.

[0053] When displaying an alarm, the alarm unit can analyze the user's social media activity and display related alarms. The alarm unit can display related alarms based on, for example, the user's social media activity history. The alarm unit can also customize alarms based on, for example, the user's social media interests. The alarm unit can also display the most appropriate alarm based on, for example, the user's social media following and like information. This allows for more relevant alarms to be displayed by analyzing social media activity. Analysis of social media activity is achieved using techniques such as post content, number of likes, and number of followers. Some or all of the above-described processing in the alarm unit can be performed using, for example, AI, or without AI. For example, the alarm unit can input social media activity data into a generation AI and cause the generation AI to display related alarms.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When analyzing a user's meal photos, the analysis unit can evaluate the nutritional value not only based on the nutritional value of the ingredients but also based on the place of production and production method of the ingredients. For example, the analysis unit can evaluate differences in nutritional value based on information about the place of production of the ingredients. The analysis unit can also evaluate nutritional value based on the production method of the ingredients (organic cultivation, pesticide-free cultivation, etc.). Furthermore, the analysis unit can evaluate changes in nutritional value taking into account the distribution route of the ingredients. This allows for more accurate nutrient analysis by evaluating nutritional value based on the place of production and production method of the ingredients.

[0056] When analyzing food photos, the analysis unit can apply correction algorithms to accommodate different lighting conditions and shooting angles. For example, if the lighting is low, the brightness of the image can be automatically adjusted to improve analysis accuracy. Also, if the photo is taken at an oblique angle, image distortion can be corrected to ensure accurate analysis. Furthermore, if the lighting is too strong, the contrast of the image can be adjusted to make it easier to identify ingredients. This improves analysis accuracy by adapting to different lighting conditions and shooting angles.

[0057] When analyzing photos of meals, the analysis unit can obtain additional information to determine the freshness of ingredients and cooking methods. For example, freshness can be estimated from the color and texture of ingredients and reflected in the analysis results. It can also identify cooking methods (grilling, boiling, frying, etc.) and consider changes in nutrients based on that information. It can also identify cooking details from the way ingredients are cut and presented and reflect these in the analysis results. This improves the accuracy of the analysis results by identifying the freshness of ingredients and cooking methods.

[0058] When analyzing food photos, the analysis unit can improve the accuracy of the analysis by referring to the user's past meal history. For example, it can make it easier to identify similar ingredients based on data on meals the user has eaten in the past. It can also prioritize frequently used ingredients for analysis based on the user's past meal history. It can also analyze the user's past meal patterns to improve the accuracy of the analysis results. By referring to the past meal history, the analysis accuracy can be improved.

[0059] When analyzing food photos, the analysis unit can take into account the user's geographical food culture. For example, it can prioritize identifying ingredients and dishes unique to the user's region. It can also customize the analysis results based on the user's geographical food culture. Furthermore, it can reflect ingredients and dishes related to the user's regional food culture in the analysis results. This makes the analysis results more accurate by taking geographical food culture into account.

[0060] When analyzing nutrients, the analysis unit can evaluate nutritional value based on the origin and production method of ingredients. For example, differences in nutritional value can be evaluated based on information about the origin of ingredients. Nutritional value can also be evaluated based on the production method of ingredients (organic cultivation, pesticide-free, etc.). Furthermore, changes in nutritional value can be evaluated taking into account the distribution route of ingredients. This allows for more accurate nutrient analysis by evaluating nutritional value based on the origin and production method of ingredients.

[0061] When analyzing nutrients, the analysis unit can provide analysis results that take into account the user's allergy information. For example, ingredients containing allergens can be identified and excluded from the analysis results based on the user's allergy information. Alternative ingredients can also be suggested based on the user's allergy information. Furthermore, nutrients from ingredients containing allergens can be excluded from the analysis results based on the user's allergy information. This allows analysis results that do not include allergens to be provided by taking the user's allergy information into account.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The analysis unit analyzes the photo of the meal. The photo of the meal taken by the user is input into the AI, which uses image analysis technology to identify the meal and its ingredients. For example, it identifies ingredients such as vegetables, meat, and fish in the photo and analyzes the nutritional value of each. If there are any errors in the analysis results, the user can make corrections as necessary, and the corrected information can be analyzed again. Step 2: The analysis unit analyzes nutrients based on the information on the meals and ingredients analyzed by the analysis unit. Based on the information on the identified meals and ingredients, the analysis unit analyzes nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates, and evaluates the user's nutritional balance. Step 3: The proposal unit proposes future menus based on the analysis results obtained by the analysis unit. Based on the nutrient analysis results, the unit proposes the optimal menu for the user, and proposes up to three menus for breakfast, lunch, and dinner that take nutritional balance into consideration. Step 4: The advertising unit displays advertisements related to the menu proposed by the suggestion unit. Support advertisements such as delivery and co-op delivery related to the proposed menu are displayed, and advertisements related to the user are displayed. Step 5: The trend analysis unit analyzes disease trends based on the hospital visit history, medical history, and health checkup data. Based on the hospital visit history, medical history, and health checkup data entered by the user, the trend of disease is analyzed and the user's health condition is evaluated. Step 6: The alarm unit issues an alarm based on the analysis results obtained by the trend analysis unit. It can detect abnormalities in blood pressure or blood sugar levels and issue an alarm to alert the user, and issues an alarm depending on the user's health condition.

[0064] (Example 2) A health support system according to an embodiment of the present invention provides dietary health support and household chore support through meal plan suggestions. In this health support system, users submit photos of their daily meals, which AI analyzes to identify the meals and ingredients. If the analysis results are inaccurate, the user can correct them as needed. Next, the AI ​​analyzes the nutritional information based on the analyzed meal and ingredient information and suggests up to three future meal plans. Furthermore, the system displays support advertisements related to the proposed meal plans, such as delivery services and co-op home delivery. Furthermore, by pre-entering information about hospital visits, medical history, and health checkup results, the AI ​​can analyze disease trends and issue alarms as needed. For example, a user submits photos of their daily meals. To do this, the user takes photos of their meals and uploads them to the system. For example, the user takes photos of breakfast, lunch, and dinner and reports them to the system. This information is then input into the AI. The AI ​​then analyzes the input photos and identifies the meals and ingredients. The AI ​​uses image analysis technology to identify the meals and ingredients contained in the photos. For example, it identifies ingredients such as vegetables, meat, and fish in the photos and analyzes the nutrients of each. If the analysis results are inaccurate, the user can correct them as needed. Based on the analysis results, the AI ​​analyzes nutrients. Based on the identified meal and ingredient information, the AI ​​analyzes each nutrient. For example, it analyzes nutrients such as vitamins, minerals, protein, lipids, and carbohydrates to evaluate the user's nutritional balance. Next, the AI ​​suggests up to three future meal plans. Based on the nutrient analysis results, the AI ​​proposes optimal meals for the user. For example, it proposes breakfast, lunch, and dinner menus that take nutritional balance into consideration. It also displays support advertisements related to the proposed menus, such as delivery services and co-op delivery. In addition, by allowing users to enter their hospital history, medical history, and health checkup results in advance, the AI ​​analyzes disease trends. Based on the input information, the AI ​​evaluates the user's health condition and issues alarms as necessary. For example, it can detect abnormalities in blood pressure or blood sugar levels and issue alarms to alert the user. This supports the user's health through daily meals and reduces the burden of housework through meal suggestions. In this way, the health support system supports the user's health and reduces the burden of housework.

[0065] A health support system according to an embodiment includes an analysis unit, a suggestion unit, an advertising unit, a trend analysis unit, and an alarm unit. The analysis unit analyzes photos of meals. For example, the analysis unit inputs photos of meals taken by a user into an AI and identifies the meals and ingredients using image analysis technology. For example, the analysis unit identifies ingredients such as vegetables, meat, and fish appearing in the photos and analyzes the nutrients of each. The analysis unit identifies the meals and ingredients included in the photos using image recognition technology, for example. Furthermore, if the analysis unit finds any deficiencies in the analysis results, the user can correct them as appropriate. For example, the analysis unit can reanalyze the information corrected by the user to obtain accurate results. The analysis unit analyzes nutrients based on the information on the meals and ingredients analyzed by the analysis unit. For example, the analysis unit analyzes each nutrient based on the information on the identified meals and ingredients. For example, the analysis unit analyzes nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates, and evaluates the user's nutritional balance. For example, the analysis unit evaluates the user's nutritional balance based on the nutrient analysis results. The suggestion unit suggests future menus based on the analysis results obtained by the analysis unit. The suggestion unit suggests optimal menus for the user based on, for example, nutrient analysis results. For example, the suggestion unit suggests breakfast, lunch, and dinner menus that take nutritional balance into consideration. The suggestion unit suggests, for example, up to three future menus. The advertising unit displays advertisements related to the menus suggested by the suggestion unit. The advertising unit displays, for example, support advertisements for delivery services related to the proposed menus, such as co-op home delivery. The advertising unit displays, for example, advertisements related to the user. The trend analysis unit analyzes disease trends based on data on hospital visit history, medical history, and health checkups. The trend analysis unit analyzes disease trends based on, for example, hospital visit history, medical history, and health checkup results entered by the user. The trend analysis unit evaluates the user's health condition based on, for example, the entered information. The alarm unit issues an alarm based on the analysis results obtained by the trend analysis unit. The alarm unit can detect abnormalities in blood pressure or blood sugar levels, and issue an alarm to alert the user. The alarm unit issues an alarm based on, for example, the user's health condition. As a result, the health support system according to the embodiment can support the user's health and reduce the burden of housework.

[0066] The analysis unit can estimate the user's emotions and adjust the accuracy of food photo analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can increase the analysis accuracy and strengthen the automatic correction function, thereby reducing the user's effort. Furthermore, for example, if the user is relaxed, the analysis unit can maintain the analysis accuracy at normal levels and provide an option for the user to manually correct the results. Furthermore, for example, if the user is in a hurry, the analysis unit can increase the analysis accuracy to provide quicker results and reduce the user's confirmation time. This allows for more appropriate analysis results to be provided by adjusting the analysis accuracy 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis accuracy based on the emotion.

[0067] When analyzing food photos, the analysis unit can apply correction algorithms to accommodate different lighting conditions and shooting angles. For example, when lighting is low, the analysis unit automatically adjusts the brightness of the image to improve analysis accuracy. The analysis unit can also correct image distortion to perform accurate analysis when the shooting angle is oblique, for example. The analysis unit can also adjust the contrast of the image to make it easier to identify ingredients when lighting is too strong, for example. This improves analysis accuracy by adapting to different lighting conditions and shooting angles. The correction algorithm is realized using techniques such as lighting correction, angle correction, and color correction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on lighting conditions and shooting angles into the generation AI and have the generation AI apply the correction algorithm.

[0068] When analyzing a photo of a meal, the analysis unit can acquire additional information to identify the freshness of ingredients and cooking methods. The analysis unit, for example, estimates freshness from the color and texture of ingredients and reflects this in the analysis results. The analysis unit can also identify the cooking method (grilling, boiling, frying, etc.) and consider changes in nutrients based on this. The analysis unit can also identify cooking details, for example, from how ingredients are cut and presented, and reflect this in the analysis results. This improves the accuracy of the analysis results by identifying the freshness of ingredients and cooking methods. Acquisition of additional information is achieved using technology such as ingredient freshness information and cooking method data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the freshness of ingredients and cooking methods into the generation AI and cause the generation AI to acquire the additional information.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, for example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, for example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, resulting in a display that is easy for the user to view. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method based on the emotion.

[0070] When analyzing food photos, the analysis unit can improve the accuracy of the analysis by referring to the user's past meal history. For example, the analysis unit can easily identify similar ingredients based on data on meals the user has eaten in the past. The analysis unit can also, for example, prioritize frequently used ingredients for analysis based on the user's past meal history. The analysis unit can also, for example, analyze the user's past meal patterns to improve the accuracy of the analysis results. By referring to the past meal history, the analysis accuracy is improved. Referencing the past meal history is achieved using technologies such as database structure and search algorithms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past meal history data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0071] The analysis unit can take into account the user's geographical food culture when analyzing food photos. For example, the analysis unit prioritizes identifying ingredients and dishes unique to the user's region. The analysis unit can also customize the analysis results based on the user's geographical food culture. The analysis unit can also reflect ingredients and dishes related to the user's regional food culture in the analysis results. This makes the analysis results more accurate by taking geographical food culture into consideration. Consideration of geographical food culture can be achieved, for example, by techniques such as regional ingredients, cooking methods, and eating habits. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input geographical food culture data into a generation AI and have the generation AI perform the analysis.

[0072] The analysis unit can estimate the user's emotions and adjust the nutrient analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can highlight nutrient deficiencies and suggest remedial measures. The analysis unit can also provide detailed nutrient analysis results when the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide nutrient analysis results that focus on the key points. This allows for adjusting the nutrient analysis results according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generative AI and cause the generative AI to adjust the analysis results based on the emotion.

[0073] When analyzing nutrients, the analysis unit can evaluate nutritional value based on the origin and production method of the ingredients. The analysis unit can evaluate differences in nutritional value based on, for example, information about the origin of the ingredients. The analysis unit can also evaluate nutritional value based on, for example, the production method of the ingredients (organic cultivation, pesticide-free cultivation, etc.). The analysis unit can also evaluate changes in nutritional value by taking into account, for example, the distribution route of the ingredients. This enables more accurate nutrient analysis by evaluating nutritional value based on the origin and production method of the ingredients. Evaluation of the origin and production method of ingredients is achieved using, for example, techniques such as organic cultivation and regional specialty products. 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 data on the origin and production method of ingredients into the generation AI and have the generation AI evaluate nutritional value.

[0074] The analysis unit can provide analysis results by taking into account the user's allergy information when analyzing nutrients. For example, the analysis unit can identify ingredients containing allergens based on the user's allergy information and exclude them from the analysis results. The analysis unit can also suggest alternative ingredients by taking into account the user's allergy information. For example, the analysis unit can exclude nutrients from ingredients containing allergens from the analysis results based on the user's allergy information. This allows analysis results that do not contain allergens to be provided by taking into account the user's allergy information. Taking into account allergy information is achieved using technology such as the user's allergy history and medical data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's allergy information into the generation AI and cause the generation AI to provide analysis results based on the allergy information.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, resulting in a display that is easy for the user to view. The emotion estimation is achieved using an emotion estimation function, such as 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 can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method based on the emotion.

[0076] The analysis unit can evaluate nutritional balance based on the user's age and gender when analyzing nutrients. The analysis unit can evaluate the amount of nutrients needed based on the user's age, for example. The analysis unit can also evaluate nutritional balance based on the user's gender, for example. The analysis unit can also suggest an optimal nutritional balance, taking the user's age and gender into consideration, for example. This enables more appropriate nutrient analysis by evaluating nutritional balance based on the user's age and gender. The evaluation of nutritional balance based on age and gender can be achieved using techniques such as recommended nutrient amounts and nutritional standards for each age. 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 data on the user's age and gender into the generation AI and have the generation AI evaluate the nutritional balance.

[0077] When analyzing nutrients, the analysis unit can provide analysis results taking into account the user's amount of exercise and lifestyle habits. The analysis unit, for example, evaluates the amount of nutrients needed based on the user's amount of exercise. The analysis unit can also evaluate nutritional balance taking into account the user's lifestyle habits (such as meal times and frequency). The analysis unit can also propose an optimal nutritional balance taking into account the user's amount of exercise and lifestyle habits. This enables more appropriate nutrient analysis by taking into account the user's amount of exercise and lifestyle habits. Consideration of the amount of exercise and lifestyle habits can be achieved using techniques such as exercise frequency, daily rhythm, and eating habits. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the user's amount of exercise and lifestyle habits into the generation AI and have the generation AI provide the analysis results.

[0078] The suggestion unit can estimate the user's emotions and adjust the presentation method of the menu suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide a simple and highly visible suggestion method. Furthermore, for example, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. Furthermore, for example, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This allows the suggestion method of the menu suggestion to be adjusted according to the user's emotions, making it easier for the user to read. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method based on the emotion.

[0079] When proposing a menu, the suggestion unit can suggest an optimal menu by referring to the user's past meal history. The suggestion unit, for example, proposes a menu that takes nutritional balance into consideration based on the user's past meal history. The suggestion unit can also propose a menu that includes the user's favorite ingredients based on the user's past meal history. The suggestion unit can also propose an optimal menu by analyzing the user's past meal patterns, for example. This makes it possible to suggest a more appropriate menu by referring to the past meal history. Referencing the past meal history is achieved by techniques such as a database structure and a search algorithm. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past meal history data into a generation AI and have the generation AI suggest an optimal menu.

[0080] When proposing a menu, the suggestion unit can make the suggestion taking into consideration ingredients according to the season and weather. The suggestion unit, for example, proposes a menu using seasonal ingredients. The suggestion unit can also propose hot or cold dishes according to the weather, for example. The suggestion unit can also propose a menu that takes nutritional balance into consideration based on the season and weather, for example. This makes it possible to propose a more appropriate menu by taking into consideration ingredients according to the season and weather. Consideration of ingredients according to the season and weather is achieved by, for example, techniques such as seasonal ingredients and seasonal nutritional values. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the season and weather into the generation AI and cause the generation AI to execute a menu suggestion.

[0081] The suggestion unit can estimate the user's emotions and prioritize menu suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting simple and easy meals. Furthermore, for example, if the user is relaxed, the suggestion unit can prioritize suggesting meals that can be made over time. Furthermore, for example, if the user is in a hurry, the suggestion unit can prioritize suggesting meals that can be made in a short amount of time. This allows for more appropriate menu suggestions by prioritizing menu suggestions based on the user's emotions. The emotion estimation is achieved 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 suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities based on the emotions.

[0082] The suggestion unit can make a menu suggestion taking into consideration the user's geographical food culture. For example, the suggestion unit can suggest a menu including ingredients and dishes unique to the user's region. The suggestion unit can also suggest an optimal menu based on the user's geographical food culture. The suggestion unit can also suggest ingredients and dishes related to the user's regional food culture. This allows for more appropriate menu suggestions by taking geographical food culture into consideration. Consideration of geographical food culture is achieved, for example, by techniques such as regional ingredients, cooking methods, and eating habits. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical food culture data into a generation AI and cause the generation AI to suggest a menu.

[0083] The suggestion unit can make a suggestion while taking into consideration the user's budget and the availability of ingredients when proposing a menu. The suggestion unit, for example, suggests a menu using ingredients according to the user's budget. The suggestion unit can also suggest a menu using ingredients available in the user's area, for example. The suggestion unit can also suggest an optimal menu by taking into consideration the user's budget and the availability of ingredients. This makes it possible to suggest a more realistic menu by taking into consideration the user's budget and the availability of ingredients. Consideration of the budget and the availability of ingredients can be achieved, for example, by using technology such as the price of ingredients, the time and place when they are available, etc. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the budget and the availability of ingredients into the generation AI and have the generation AI execute the menu suggestion.

[0084] The advertising unit can estimate a user's emotions and adjust the advertisement display method based on the estimated user emotions. For example, when a user is feeling stressed, the advertising unit can display a simple, highly visible advertisement. For example, when a user is relaxed, the advertising unit can display an advertisement containing detailed information. For example, when a user is in a hurry, the advertising unit can display an advertisement that focuses on the main points. This enables more effective advertisement display by adjusting the advertisement display method 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 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 advertising unit can be performed using, for example, AI, or without AI. For example, the advertising unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0085] When displaying an advertisement, the advertising unit can display a highly relevant advertisement by referring to the user's past purchase history. The advertising unit, for example, displays advertisements related to products the user has previously purchased. The advertising unit can also display advertisements that suggest products that the user may be interested in based on the user's past purchase history. The advertising unit can also analyze the user's purchase history and preferentially display highly relevant advertisements. This allows more relevant advertisements to be displayed by referring to the past purchase history. The reference to the past purchase history is achieved using technologies such as a database structure and a search algorithm. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without using AI. For example, the advertising unit can input past purchase history data into a generation AI and cause the generation AI to display highly relevant advertisements.

[0086] The advertising unit can customize advertisements based on the user's current lifestyle and areas of interest when displaying advertisements. For example, the advertising unit displays relevant advertisements based on the user's current lifestyle (e.g., parenting, student, etc.). The advertising unit can also customize advertisements based on the user's areas of interest (e.g., health, fitness, etc.). The advertising unit can also display optimal advertisements, for example, taking into account the user's lifestyle and areas of interest. This enables more effective advertisement display by customizing advertisements based on the user's current lifestyle and areas of interest. Consideration of the current lifestyle and areas of interest can be achieved using techniques such as daily rhythms, hobbies, and interests. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without AI. For example, the advertising unit can input data on the user's current lifestyle and areas of interest into a generation AI and have the generation AI customize the advertisement.

[0087] The advertising unit can estimate a user's emotions and adjust the timing of advertisement display based on the estimated user emotions. For example, the advertising unit can reduce the frequency of advertisement display when the user is stressed. The advertising unit can also increase the frequency of advertisement display when the user is relaxed. The advertising unit can also temporarily refrain from displaying advertisements when the user is in a hurry. This enables more effective advertisement display by adjusting the advertisement display timing according to the user's emotions. The emotion estimation is realized 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 advertising unit can be performed using AI, for example, or without AI. For example, the advertising unit can input user emotion data into the generation AI and cause the generation AI to adjust the display timing based on the emotion.

[0088] The advertising unit can display highly relevant advertisements by taking into account the user's geographical location information when displaying advertisements. For example, the advertising unit can display advertisements for nearby stores and services based on the user's current location. The advertising unit can also display region-specific advertisements based on the user's geographical location information. The advertising unit can also display optimal advertisements by taking into account the user's location information. This allows more relevant advertisements to be displayed by taking into account the geographical location information. Taking into account the geographical location information is achieved using technologies such as GPS data and location information services. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without using AI. For example, the advertising unit can input geographical location information data into a generation AI and cause the generation AI to display highly relevant advertisements.

[0089] When displaying an advertisement, the advertising unit can analyze the user's social media activity and display relevant advertisements. The advertising unit, for example, customizes advertisements based on the user's social media interests. The advertising unit can also analyze the user's social media activity history and display highly relevant advertisements. The advertising unit can also display optimal advertisements based on the user's following and likes on social media. This allows for more relevant advertisements to be displayed by analyzing social media activity. Analysis of social media activity is achieved using techniques such as post content, number of likes, and number of followers. Some or all of the above-described processing in the advertising unit may be performed using, for example, AI, or may be performed without AI. For example, the advertising unit can input social media activity data into a generation AI and cause the generation AI to display relevant advertisements.

[0090] The trend analysis unit can estimate the user's emotions and adjust the disease trend analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the trend analysis unit can highlight stress-related illness trends. The trend analysis unit can also perform a detailed analysis of the user's overall health status when the user is relaxed. The trend analysis unit can also provide a more concise disease trend analysis result when the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the disease trend analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trend analysis unit can be performed using, for example, an AI, or without an AI. For example, the trend analysis unit can input the user's emotion data into the generative AI and cause the generative AI to adjust the analysis results based on the emotion.

[0091] During the trend analysis, the trend analysis unit can perform analysis based on the user's family history and genetic information. For example, the trend analysis unit can evaluate the risk of a genetic disease based on the user's family history. The trend analysis unit can also analyze trends in specific diseases based on the user's genetic information. The trend analysis unit can also propose an optimal health management plan, for example, by taking the user's family history and genetic information into consideration. This enables more accurate disease trend analysis by taking the family history and genetic information into consideration. Taking the family history and genetic information into consideration can be achieved using techniques such as family medical history and genetic test results. Some or all of the above-described processing in the trend analysis unit can be performed using, for example, AI, or without AI. For example, the trend analysis unit can input data on the family history and genetic information into the generation AI and have the generation AI perform the analysis.

[0092] The trend analysis unit can perform trend analysis by taking into account the user's lifestyle habits and environmental factors. The trend analysis unit can analyze disease trends by taking into account, for example, the user's lifestyle habits (e.g., smoking, drinking, etc.). The trend analysis unit can also evaluate health risks based on the user's environmental factors (e.g., living environment, work environment, etc.). The trend analysis unit can also propose an optimal health management plan by taking into account the user's lifestyle habits and environmental factors. This enables more accurate disease trend analysis by taking into account lifestyle habits and environmental factors. Consideration of lifestyle habits and environmental factors can be achieved using techniques such as exercise frequency, eating habits, and living environment. Some or all of the above-mentioned processing in the trend analysis unit can be performed using, for example, AI, or without AI. For example, the trend analysis unit can input data on lifestyle habits and environmental factors into a generation AI and have the generation AI perform the analysis.

[0093] The trend analysis unit can estimate the user's emotions and adjust the display method of the trend analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the trend analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the trend analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the trend analysis unit can provide a display method that focuses on the main points. This allows the display method of the trend analysis results to be adjusted according to the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, such as 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 trend analysis unit can be performed using, for example, an AI, or without an AI. For example, the trend analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0094] The trend analysis unit can improve the accuracy of the trend analysis by referring to the user's past health checkup data. For example, the trend analysis unit evaluates the user's current health condition based on the user's past health checkup data. The trend analysis unit can also analyze changes in health risks based on the user's past health checkup data. For example, the trend analysis unit can also propose an optimal health management plan by referring to the user's past health checkup data. This enables more accurate trend analysis by referring to past health checkup data. Referencing past health checkup data is achieved using techniques such as diagnostic results, test items, and test dates. Some or all of the above-described processing in the trend analysis unit may be performed using, for example, AI, or without AI. For example, the trend analysis unit can input past health checkup data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0095] During trend analysis, the trend analysis unit can perform analysis based on the user's geographical environmental factors. The trend analysis unit can evaluate health risks, for example, by taking into account the user's living environment (e.g., urban area, rural area, etc.). The trend analysis unit can also analyze health risks based on the user's work environment (e.g., office, factory, etc.). The trend analysis unit can also propose an optimal health management plan, for example, by taking into account the user's geographical environmental factors. This enables more accurate trend analysis by taking into account geographical environmental factors. Consideration of geographical environmental factors can be achieved using techniques such as the local climate and the level of environmental pollution. Some or all of the above-described processing in the trend analysis unit can be performed using, for example, AI, or without AI. For example, the trend analysis unit can input data on geographical environmental factors into a generation AI and have the generation AI perform the analysis.

[0096] The alarm unit can estimate the user's emotions and adjust the alarm display method based on the estimated user emotions. For example, if the user is feeling stressed, the alarm unit can display a simple, highly visible alarm. For example, if the user is relaxed, the alarm unit can also display an alarm that includes detailed information. For example, if the user is in a hurry, the alarm unit can also display an alarm that focuses on the main points. This allows the alarm display method to be adjusted according to the user's emotions, making it easier for the user to see. 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 alarm unit can be performed using, for example, AI, or without AI. For example, the alarm unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method based on the emotion.

[0097] When displaying an alarm, the alarm unit can improve the accuracy of the alarm by referring to the user's past health data. The alarm unit, for example, evaluates current health risks based on the user's past health data. The alarm unit can also analyze changes in health risks from the user's past health data. The alarm unit can also display an optimal alarm by referring to the user's past health data. This enables more accurate alarm display by referencing past health data. Referencing past health data is achieved using techniques such as diagnosis results, test items, and test dates. Some or all of the above-described processing in the alarm unit may be performed using, for example, AI, or may be performed without AI. For example, the alarm unit can input past health data into a generation AI and have the generation AI improve the accuracy of the alarm.

[0098] The alarm unit can customize the alarm by taking into account the user's current living situation and environmental factors when displaying the alarm. The alarm unit customizes the alarm based on, for example, the user's current living situation (e.g., childcare, student, etc.). The alarm unit can also adjust the alarm based on, for example, the user's environmental factors (e.g., living environment, work environment, etc.). The alarm unit can also display an optimal alarm by taking into account the user's living situation and environmental factors. This enables a more appropriate alarm display by taking into account the current living situation and environmental factors. Consideration of the current living situation and environmental factors can be achieved by techniques such as lifestyle rhythm, living environment, and work situation. Some or all of the above-mentioned processing in the alarm unit may be performed using, for example, AI, or may be performed without using AI. For example, the alarm unit can input data on the user's current living situation and environmental factors into a generation AI and have the generation AI customize the alarm.

[0099] The alarm unit can estimate the user's emotions and adjust the timing of alarm display based on the estimated user emotions. For example, the alarm unit can reduce the frequency of alarm display when the user is stressed. The alarm unit can also increase the frequency of alarm display when the user is relaxed. The alarm unit can also temporarily refrain from displaying the alarm when the user is in a hurry. This allows the alarm to be displayed at a more appropriate time by adjusting the timing of alarm display according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 alarm unit can be performed using, for example, an AI, or without an AI. For example, the alarm unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display timing based on the emotion.

[0100] The alarm unit can display the alarm taking into consideration the user's geographical location information when displaying the alarm. The alarm unit, for example, displays a relevant alarm based on the user's current location. The alarm unit can also display an optimal alarm based on the user's geographical location information. The alarm unit can also adjust the alarm display content, for example, taking into consideration the user's location information. This enables a more appropriate alarm display by taking into consideration the geographical location information. Consideration of the geographical location information is achieved, for example, by technologies such as GPS data and location information services. Some or all of the above-described processing in the alarm unit may be performed using, for example, AI, or may be performed without using AI. For example, the alarm unit can input geographical location information data to a generation AI and cause the generation AI to display the alarm.

[0101] When displaying an alarm, the alarm unit can analyze the user's social media activity and display related alarms. The alarm unit can display related alarms based on, for example, the user's social media activity history. The alarm unit can also customize alarms based on, for example, the user's social media interests. The alarm unit can also display the most appropriate alarm based on, for example, the user's social media following and like information. This allows for more relevant alarms to be displayed by analyzing social media activity. Analysis of social media activity is achieved using techniques such as post content, number of likes, and number of followers. Some or all of the above-described processing in the alarm unit can be performed using, for example, AI, or without AI. For example, the alarm unit can input social media activity data into a generation AI and cause the generation AI to display related alarms. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, suggestion unit, advertising unit, trend analysis unit, and alarm unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the smart device 14 and performs image analysis using the control unit 46A. The analysis unit analyzes nutrients using the specific processing unit 290 of the data processing device 12. For example, the suggestion unit suggests a menu using the specific processing unit 290 of the data processing device 12 and displays it on the display 40A of the smart device 14. For example, the advertising unit displays advertisements related to the display 40A of the smart device 14. For example, the trend analysis unit analyzes disease trends using the specific processing unit 290 of the data processing device 12. For example, the alarm unit detects an abnormality using the specific processing unit 290 of the data processing device 12 and issues an alarm through the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, suggestion unit, advertising unit, trend analysis unit, and alarm unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the smart glasses 214 and performs image analysis using the control unit 46A. The analysis unit performs nutrient analysis using the specific processing unit 290 of the data processing device 12, for example. The suggestion unit suggests a menu using the specific processing unit 290 of the data processing device 12 and displays it on the display of the smart glasses 214, for example. The advertising unit displays relevant advertisements on the display of the smart glasses 214, for example. The trend analysis unit analyzes disease trends using the specific processing unit 290 of the data processing device 12, for example. The alarm unit detects an abnormality using the specific processing unit 290 of the data processing device 12 and issues an alarm through the speaker 240 of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the analysis unit, suggestion unit, advertising unit, trend analysis unit, and alarm unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the headset terminal 314 and performs image analysis using the control unit 46A. The analysis unit analyzes nutrients using the specific processing unit 290 of the data processing device 12, for example. The suggestion unit suggests a menu using the specific processing unit 290 of the data processing device 12 and displays it on the display 343 of the headset terminal 314, for example. The advertising unit displays relevant advertisements on the display 343 of the headset terminal 314, for example. The trend analysis unit analyzes disease trends using the specific processing unit 290 of the data processing device 12, for example. The alarm unit detects an abnormality using the specific processing unit 290 of the data processing device 12 and issues an alarm through the speaker 240 of the headset terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the analysis unit, analysis unit, suggestion unit, advertising unit, trend analysis unit, and alarm unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the robot 414 and performs image analysis using the control unit 46A. The analysis unit performs nutrient analysis using the specific processing unit 290 of the data processing device 12, for example. The suggestion unit suggests a menu using the specific processing unit 290 of the data processing device 12 and displays it on the display of the robot 414, for example. The advertising unit displays relevant advertisements on the display of the robot 414, for example. The trend analysis unit analyzes disease trends using the specific processing unit 290 of the data processing device 12, for example. The alarm unit detects an abnormality using the specific processing unit 290 of the data processing device 12 and issues an alarm through the speaker 240 of the robot 414, for example.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When analyzing a user's meal photos, the analysis unit can evaluate the nutritional value not only based on the nutritional value of the ingredients but also based on the place of production and production method of the ingredients. For example, the analysis unit can evaluate differences in nutritional value based on information about the place of production of the ingredients. The analysis unit can also evaluate nutritional value based on the production method of the ingredients (organic cultivation, pesticide-free cultivation, etc.). Furthermore, the analysis unit can evaluate changes in nutritional value taking into account the distribution route of the ingredients. This allows for more accurate nutrient analysis by evaluating nutritional value based on the place of production and production method of the ingredients.

[0104] The analysis unit can estimate the user's emotions and adjust the accuracy of food photo analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis accuracy can be increased and the automatic correction function can be strengthened, reducing the user's effort. Alternatively, if the user is relaxed, the analysis accuracy can be kept normal and an option can be provided for the user to manually correct the results. Furthermore, if the user is in a hurry, the analysis accuracy can be increased to provide quick results and reduce the user's confirmation time. This allows the system to provide more appropriate analysis results by adjusting the analysis accuracy according to the user's emotions.

[0105] When analyzing food photos, the analysis unit can apply correction algorithms to accommodate different lighting conditions and shooting angles. For example, if the lighting is low, the brightness of the image can be automatically adjusted to improve analysis accuracy. Also, if the photo is taken at an oblique angle, image distortion can be corrected to ensure accurate analysis. Furthermore, if the lighting is too strong, the contrast of the image can be adjusted to make it easier to identify ingredients. This improves analysis accuracy by adapting to different lighting conditions and shooting angles.

[0106] When analyzing photos of meals, the analysis unit can obtain additional information to determine the freshness of ingredients and cooking methods. For example, freshness can be estimated from the color and texture of ingredients and reflected in the analysis results. It can also identify cooking methods (grilling, boiling, frying, etc.) and consider changes in nutrients based on that information. It can also identify cooking details from the way ingredients are cut and presented and reflect these in the analysis results. This improves the accuracy of the analysis results by identifying the freshness of ingredients and cooking methods.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to see.

[0108] When analyzing food photos, the analysis unit can improve the accuracy of the analysis by referring to the user's past meal history. For example, it can make it easier to identify similar ingredients based on data on meals the user has eaten in the past. It can also prioritize frequently used ingredients for analysis based on the user's past meal history. It can also analyze the user's past meal patterns to improve the accuracy of the analysis results. By referring to the past meal history, the analysis accuracy can be improved.

[0109] When analyzing food photos, the analysis unit can take into account the user's geographical food culture. For example, it can prioritize identifying ingredients and dishes unique to the user's region. It can also customize the analysis results based on the user's geographical food culture. Furthermore, it can reflect ingredients and dishes related to the user's regional food culture in the analysis results. This makes the analysis results more accurate by taking geographical food culture into account.

[0110] The analysis unit can estimate the user's emotions and adjust the nutrient analysis results based on the estimated user emotions. For example, if the user is feeling stressed, it can highlight nutrient deficiencies and suggest remedial measures. If the user is relaxed, it can provide detailed nutrient analysis results. Furthermore, if the user is in a hurry, it can provide nutrient analysis results that focus on the main points. In this way, by adjusting the nutrient analysis results according to the user's emotions, it is possible to provide more appropriate analysis results.

[0111] When analyzing nutrients, the analysis unit can evaluate nutritional value based on the origin and production method of ingredients. For example, differences in nutritional value can be evaluated based on information about the origin of ingredients. Nutritional value can also be evaluated based on the production method of ingredients (organic cultivation, pesticide-free, etc.). Furthermore, changes in nutritional value can be evaluated taking into account the distribution route of ingredients. This allows for more accurate nutrient analysis by evaluating nutritional value based on the origin and production method of ingredients.

[0112] When analyzing nutrients, the analysis unit can provide analysis results that take into account the user's allergy information. For example, ingredients containing allergens can be identified and excluded from the analysis results based on the user's allergy information. Alternative ingredients can also be suggested based on the user's allergy information. Furthermore, nutrients from ingredients containing allergens can be excluded from the analysis results based on the user's allergy information. This allows analysis results that do not include allergens to be provided by taking the user's allergy information into account.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The analysis unit analyzes the photo of the meal. The photo of the meal taken by the user is input into the AI, which uses image analysis technology to identify the meal and its ingredients. For example, it identifies ingredients such as vegetables, meat, and fish in the photo and analyzes the nutritional value of each. If there are any errors in the analysis results, the user can make corrections as necessary, and the corrected information can be analyzed again. Step 2: The analysis unit analyzes nutrients based on the information on the meals and ingredients analyzed by the analysis unit. Based on the information on the identified meals and ingredients, the analysis unit analyzes nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates, and evaluates the user's nutritional balance. Step 3: The proposal unit proposes future menus based on the analysis results obtained by the analysis unit. Based on the nutrient analysis results, the unit proposes the optimal menu for the user, and proposes up to three menus for breakfast, lunch, and dinner that take nutritional balance into consideration. Step 4: The advertising unit displays advertisements related to the menu proposed by the suggestion unit. Support advertisements such as delivery and co-op delivery related to the proposed menu are displayed, and advertisements related to the user are displayed. Step 5: The trend analysis unit analyzes disease trends based on the hospital visit history, medical history, and health checkup data. Based on the hospital visit history, medical history, and health checkup data entered by the user, the trend of disease is analyzed and the user's health condition is evaluated. Step 6: The alarm unit issues an alarm based on the analysis results obtained by the trend analysis unit. It can detect abnormalities in blood pressure or blood sugar levels and issue an alarm to alert the user, and issues an alarm depending on the user's health condition.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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. an analysis unit that analyzes food photos; an analysis unit that analyzes nutrients based on the information on the meals and ingredients analyzed by the analysis unit; a proposal unit that proposes future menus based on the analysis results obtained by the analysis unit; an advertising unit that displays advertisements related to the menus proposed by the suggestion unit; A trend analysis department analyzes disease trends based on hospital visit history, medical history, and health checkup data, and an alarm unit that issues an alarm based on the analysis result obtained by the trend analysis unit; A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the accuracy of food photo analysis based on the estimated user emotions.

2. The system of claim 1.

3. The analysis unit When analyzing food photos, a correction algorithm is applied to account for different lighting conditions and shooting angles.

2. The system of claim 1.

4. The analysis unit When analyzing photos of meals, additional information is obtained to determine the freshness of ingredients and cooking methods.

2. The system of claim 1.

5. The analysis unit Inferring user emotions and adjusting the display of analysis results based on the estimated user emotions 2. The system of claim 1.

6. The analysis unit When analyzing food photos, the accuracy of the analysis can be improved by referring to the user's past meal history.

2. The system of claim 1.

7. The analysis unit When analyzing food photos, analysis is performed based on the user's geographical food culture.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting nutrient analysis results based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit When analyzing nutrients, evaluate the nutritional value of ingredients based on their origin and production method.

2. The system of claim 1.

10. The analysis unit When analyzing nutrients, provide analysis results taking into account the user's allergy information 2. The system of claim 1.

Images