System

Abstract

An object of a system according to an embodiment is to analyze meal data, visualize health risks, and provide optimal nutrition advice.SOLUTION: A system includes a reception part, an analysis part, a visualization part, and a provision part. The reception unit inputs meal data. The analysis unit analyzes the meal data input by the reception unit. The visualization unit visualizes the risk on the basis of a result analyzed by the analysis unit. The providing unit provides optimal nutrition advice based on the risk visualized by the visualizing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately visualize health risks based on dietary data or provide optimal nutritional advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze dietary data, visualize health risks, and provide optimal nutritional advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a visualization unit, and a provision unit. The reception unit inputs dietary data. The analysis unit analyzes the dietary data input by the reception unit. The visualization unit visualizes risks based on the results of the analysis by the analysis unit. The provision unit provides optimal nutritional advice based on the risks visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze dietary data, visualize health risks, and provide optimal nutritional advice. [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 system according to an embodiment of the present invention uses a generating AI to analyze individual dietary data, visualize the risk of AGE (Advanced Glycation End-products) accumulation due to high-temperature cooking, and provide optimal nutritional advice. This system supports healthy aging through personalized recommendations related to the user's diet and lifestyle. For example, a user inputs dietary data, and a generating AI analyzes the data to assess risk and visually display the risk. Furthermore, the generating AI provides optimal nutritional advice based on the user's dietary data and lifestyle. This allows users to scientifically understand the impact of their diet on their health and maintain optimal nutritional balance. This allows the system to scientifically analyze the user's dietary data and provide optimal nutritional advice for healthy aging. For example, a user inputs dietary data, and a generating AI analyzes the data to assess risk and visually display the risk. Furthermore, the generating AI provides optimal nutritional advice based on the user's dietary data and lifestyle. This allows users to scientifically understand the impact of their diet on their health and maintain optimal nutritional balance.

[0029] The nutrition advice system according to the embodiment includes a reception unit, an analysis unit, a visualization unit, and a provision unit. The reception unit receives dietary data from a user. The dietary data includes, but is not limited to, the types of ingredients, cooking methods, and intake amounts. The reception unit receives information such as the types of dishes eaten by the user, cooking methods, and amounts of ingredients. The analysis unit uses a generation AI to analyze the dietary data received by the reception unit. The analysis is performed based on, but is not limited to, the frequency of high-temperature cooking and the types of ingredients. For example, the generation AI evaluates the risk of AGE accumulation due to high-temperature cooking based on the dietary data. The visualization unit visualizes the risk based on the analysis results of the analysis unit. The visualization indicates the risk in an easily understandable manner, for example, by displaying high risk in red and low risk in green. The provision unit provides optimal nutritional advice based on the risk visualized by the visualization unit. The advice may, for example, recommend low-temperature cooking, such as steaming or stewing, to reduce the risk of AGE accumulation. As a result, the nutrition advice system according to the embodiment can promote healthy aging by analyzing the dietary data of the user, visualizing risks, and providing optimal nutrition advice.

[0030] The analysis unit includes an evaluation unit that evaluates risk based on the frequency of high-temperature cooking or the type of ingredients. The evaluation unit evaluates risk, for example, based on the frequency of high-temperature cooking. For example, if high-temperature cooking such as fried or grilled foods is frequent, the evaluation unit evaluates that there is a high risk of AGE accumulation. The evaluation unit can also evaluate risk based on the type of ingredients. For example, if there is a lot of meat or fatty ingredients, the evaluation unit evaluates that there is a high risk of AGE accumulation. The evaluation unit can also evaluate risk based on both the frequency of high-temperature cooking and the type of ingredients. For example, if there is a lot of fried foods and a lot of meat, the evaluation unit evaluates that there is an even higher risk. This allows the analysis unit to evaluate risk more accurately by evaluating risk based on the frequency of high-temperature cooking and the type of ingredients.

[0031] The visualization unit includes a display unit that visualizes risks by color coding or graph display. The display unit, for example, visualizes risks by color coding. For example, high risks are displayed in red, and low risks are displayed in green. The display unit can also visualize risks by graph display. For example, risk fluctuations are displayed in a line graph. The display unit can also visualize risks by heat map display. For example, high risk areas are displayed in red, and low risk areas are displayed in blue. In this way, by visualizing risks by color coding or graph display, the visualization unit makes it easier for users to intuitively understand risks.

[0032] The providing unit includes an advice unit that recommends low-temperature cooking or specific ingredients. The advice unit, for example, recommends low-temperature cooking. For example, it recommends low-temperature cooking such as steaming or stewing. The advice unit can also recommend specific ingredients. For example, it can recommend ingredients that are considered to be good for health, such as vegetables and fruits. The advice unit can also advise avoiding specific ingredients. For example, it can advise avoiding high-fat ingredients and processed foods. In this way, by recommending low-temperature cooking or specific ingredients, the providing unit can reduce the risk of AGE accumulation and promote healthy aging.

[0033] The reception unit can analyze the user's past meal data input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit can analyze the time periods in which the user has previously input data and suggest the optimal input timing. The reception unit can also automatically complete specific meal data from the user's past input history. For example, similar meal data can be automatically completed based on meal data that the user has previously input. In this way, by analyzing the user's past input history, the optimal input method can be selected and input efficiency can be improved.

[0034] The reception unit can perform filtering based on the user's current health condition and allergy information when inputting meal data. For example, the reception unit displays a warning if the user attempts to input an ingredient to which the user is allergic. For example, the reception unit displays a warning message if the user attempts to input an ingredient to which the user is allergic. The reception unit can also recommend specific ingredients and cooking methods based on the user's current health condition. For example, it recommends specific ingredients and cooking methods based on the user's health condition. The reception unit can also refer to the user's health data and evaluate whether the input meal data is appropriate. For example, it evaluates whether the input meal data is appropriate based on the user's health data. In this way, appropriate meal data can be input by filtering based on the user's health condition and allergy information.

[0035] When inputting meal data, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs meal data using voice recognition technology. For example, if the user selects voice input, the reception unit inputs meal data using voice recognition technology. Furthermore, if the user selects image input, the reception unit can automatically extract meal data using image analysis technology. For example, if the user selects image input, the reception unit automatically extracts meal data using image analysis technology. Furthermore, if the user selects text input, the reception unit can also provide an input completion function to simplify input. For example, if the user selects text input, the reception unit provides an input completion function to simplify input. This allows the optimum input means to be selected according to the user's input method, thereby improving input efficiency.

[0036] When inputting meal data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting data related to ingredients and dishes of that area. For example, when the user is in a specific area, the reception unit prioritizes inputting data related to ingredients and dishes of that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting meal data of the travel destination. For example, when the user is traveling, the reception unit prioritizes inputting meal data of the travel destination. Furthermore, when the user is at home, the reception unit can also input data about meals eaten at home in the past by referring to it. For example, when the user is at home, the reception unit inputs data about meals eaten at home in the past by referring to it. In this way, highly relevant data can be input preferentially by taking into account the user's geographical location information.

[0037] The reception unit can analyze the user's social media activities and input related data when inputting meal data. For example, the reception unit automatically analyzes photos of meals shared by the user on social media and inputs them as meal data. For example, the reception unit automatically analyzes photos of meals shared by the user on social media and inputs them as meal data. The reception unit can also analyze the content of the user's social media posts and input related meal data. For example, the reception unit can analyze the content of the user's social media posts and input related meal data. The reception unit can also input related meal data by referring to the activities of the user's friends on social media. For example, the reception unit can input related meal data by referring to the activities of the user's friends on social media. In this way, related data can be input efficiently by analyzing the user's social media activities.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting meal data. The reception unit, for example, improves the input method based on feedback on meal data previously input by the user. For example, the input method is improved based on feedback on meal data previously input by the user. The reception unit can also customize the input interface by referring to feedback previously provided by the user. For example, the input interface is customized by referring to feedback previously provided by the user. The reception unit can also analyze the user's past feedback and suggest an optimal input method. For example, the reception unit analyzes the user's past feedback and suggests an optimal input method. In this way, the input method can be optimized by reflecting the user's past feedback, and input efficiency can be improved.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the meal data. For example, the analysis unit performs a detailed analysis on meal data that is frequently cooked at high temperatures. For example, the analysis unit performs a detailed analysis on meal data that is frequently cooked at high temperatures. The analysis unit can also perform a concise analysis on meal data that is frequently cooked at low temperatures. For example, the analysis unit performs a concise analysis on meal data that is frequently cooked at low temperatures. The analysis unit can also perform a detailed analysis on meal data that contains a large amount of a specific ingredient. For example, the analysis unit performs a detailed analysis on meal data that contains a large amount of a specific ingredient. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the meal data.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the food category. For example, the analysis unit applies an algorithm that evaluates the risk of AGE accumulation to fried foods. For example, the analysis unit applies an algorithm that evaluates the risk of AGE accumulation to fried foods. The analysis unit can also apply an algorithm that evaluates the nutritional balance to simmered foods. For example, the analysis unit applies an algorithm that evaluates the nutritional balance to simmered foods. The analysis unit can also apply an algorithm that evaluates the vitamin and mineral content to salads. For example, the analysis unit applies an algorithm that evaluates the vitamin and mineral content to salads. In this way, by applying different analysis algorithms depending on the food category, more accurate analysis is possible.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, compares the analysis results with the user's past dietary data and adjusts the analysis algorithm. For example, it compares the analysis results with the user's past dietary data and adjusts the analysis algorithm. The analysis unit can also improve the accuracy of the analysis results based on the user's past feedback. For example, it improves the accuracy of the analysis results based on the user's past feedback. The analysis unit can also correct the current analysis results by referring to the user's past analysis results. For example, it corrects the current analysis results by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the meal data. For example, the analysis unit prioritizes analysis of recently submitted meal data. For example, the analysis unit prioritizes analysis of recently submitted meal data. The analysis unit can also prioritize analysis of meal data submitted during a specific time period. For example, the analysis unit prioritizes analysis of meal data submitted during a specific time period. The analysis unit can also prioritize analysis of meal data specified by the user. For example, the analysis unit prioritizes analysis of meal data specified by the user. This enables efficient analysis by determining the priority of analysis based on the time of submission of the meal data.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the meal data. For example, the analysis unit prioritizes analyzing meal data that is frequently cooked at high temperatures. For example, the analysis unit prioritizes analyzing meal data that is frequently cooked at high temperatures. The analysis unit can also prioritize analyzing meal data that contains a large amount of a specific ingredient. For example, the analysis unit prioritizes analyzing meal data that contains a large amount of a specific ingredient. The analysis unit can also prioritize analyzing meal data related to the user's health condition. For example, the analysis unit prioritizes analyzing meal data related to the user's health condition. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the meal data.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. The analysis unit can also use concise terms if the user has only general knowledge. For example, if the user has only general knowledge, the analysis unit uses concise terms. The analysis unit can also select optimal terms based on the user's past feedback. For example, the analysis unit selects optimal terms based on the user's past feedback. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0045] The visualization unit can improve the accuracy of visualization by taking into account the interrelationships between the meal data during visualization. For example, the visualization unit emphasizes and visualizes meal data that is frequently cooked at high temperatures. For example, the visualization unit emphasizes and visualizes meal data that is frequently cooked at high temperatures. The visualization unit can also emphasize and visualize meal data that contains a large amount of a specific ingredient. For example, the visualization unit emphasizes and visualizes meal data that contains a large amount of a specific ingredient. The visualization unit can also emphasize and visualize meal data related to the user's health condition. For example, the visualization unit emphasizes and visualizes meal data related to the user's health condition. In this way, the accuracy of visualization can be improved by taking into account the interrelationships between the meal data.

[0046] The visualization unit can perform visualization while taking into consideration attribute information of the person who submitted the meal data. The visualization unit selects an appropriate visualization method, for example, according to the age of the user. For example, an appropriate visualization method is selected according to the age of the user. The visualization unit can also select an appropriate visualization method according to the gender of the user. For example, an appropriate visualization method is selected according to the gender of the user. The visualization unit can also select an appropriate visualization method according to the health condition of the user. For example, an appropriate visualization method is selected according to the health condition of the user. In this way, by taking into consideration attribute information of the person who submitted the meal data, it is possible to provide a more appropriate visualization result.

[0047] The visualization unit can weight the visualization based on the frequency of submission of meal data during visualization. The visualization unit, for example, emphasizes and visualizes frequently submitted meal data. For example, the visualization unit emphasizes and visualizes frequently submitted meal data. The visualization unit can also emphasize and visualize meal data submitted during a specific time period. For example, the visualization unit emphasizes and visualizes meal data submitted during a specific time period. The visualization unit can also emphasize and visualize meal data specified by the user. For example, the visualization unit emphasizes and visualizes meal data specified by the user. In this way, by weighting the visualization based on the frequency of submission of meal data, important data can be displayed with emphasis.

[0048] The visualization unit can perform visualization while taking into consideration the geographical distribution of the meal data. For example, the visualization unit displays data on meals eaten by a user in a specific region on a map. For example, data on meals eaten by a user in a specific region on a map. The visualization unit can also geographically visualize data on meals eaten by a user while traveling. For example, data on meals eaten by a user while traveling is visualized geographically. The visualization unit can also geographically visualize data on meals eaten in the vicinity of the user's home. For example, data on meals eaten in the vicinity of the user's home is visualized geographically. In this way, by taking into consideration the geographical distribution of the meal data, visualization that reflects region-specific information is possible.

[0049] The visualization unit can improve the accuracy of visualization by referring to literature related to the dietary data during visualization. The visualization unit, for example, performs visualization by referring to scientific research results related to the dietary data. For example, the visualization unit performs visualization by referring to scientific research results related to the dietary data. The visualization unit can also perform visualization by referring to nutritional literature related to the dietary data. For example, the visualization unit can perform visualization by referring to nutritional literature related to the dietary data. The visualization unit can also perform visualization by referring to health guidelines related to the dietary data. For example, the visualization unit can perform visualization by referring to health guidelines related to the dietary data. In this way, by referring to literature related to the dietary data, visualization based on scientific evidence is possible.

[0050] The visualization unit can perform visualization taking into consideration the market value of the meal data when visualizing. For example, the visualization unit emphasizes and visualizes meal data that uses expensive ingredients. For example, the visualization unit emphasizes and visualizes meal data that uses expensive ingredients. The visualization unit can also emphasize and visualize meal data that uses ingredients with high market values. For example, the visualization unit emphasizes and visualizes meal data that uses ingredients with high market values. The visualization unit can also visualize meal data based on price information of ingredients purchased by the user. For example, the visualization unit visualizes meal data based on price information of ingredients purchased by the user. In this way, by taking into consideration the market value of the meal data, visualization that reflects an economic perspective is possible.

[0051] The providing unit can improve the accuracy of advice by taking into account the interrelationships between dietary data when providing the advice. For example, the providing unit provides advice recommending low-temperature cooking for dietary data that includes a lot of high-temperature cooking. For example, the providing unit provides advice recommending low-temperature cooking for dietary data that includes a lot of high-temperature cooking. The providing unit can also provide advice recommending a balanced diet for dietary data that includes a lot of specific ingredients. For example, the providing unit provides advice recommending a balanced diet for dietary data that includes a lot of specific ingredients. The providing unit can also provide appropriate nutritional advice based on dietary data related to the user's health condition. For example, the providing unit provides appropriate nutritional advice based on dietary data related to the user's health condition. In this way, more accurate advice can be provided by taking into account the interrelationships between dietary data.

[0052] When providing the advice, the providing unit can take into consideration attribute information of the person who submitted the dietary data. The providing unit can provide appropriate nutritional advice, for example, according to the age of the user. For example, appropriate nutritional advice is provided according to the age of the user. The providing unit can also provide appropriate nutritional advice according to the gender of the user. For example, appropriate nutritional advice is provided according to the gender of the user. The providing unit can also provide appropriate nutritional advice according to the health condition of the user. For example, appropriate nutritional advice is provided according to the health condition of the user. In this way, more appropriate advice can be provided by taking into consideration the attribute information of the person who submitted the dietary data.

[0053] The providing unit can weight the advice based on the frequency of submission of meal data when providing the advice. The providing unit, for example, provides detailed advice based on frequently submitted meal data. For example, detailed advice is provided based on frequently submitted meal data. The providing unit can also provide appropriate advice based on meal data submitted in a specific time period. For example, appropriate advice is provided based on meal data submitted in a specific time period. The providing unit can also provide focused advice based on meal data specified by the user. For example, focused advice is provided based on meal data specified by the user. In this way, by weighting the advice based on the frequency of submission of meal data, it is possible to provide advice based on important data.

[0054] When providing advice, the providing unit can take into consideration the geographical distribution of the dietary data. The providing unit can provide region-specific nutritional advice based on, for example, data on meals eaten by the user in a specific region. For example, region-specific nutritional advice can be provided based on data on meals eaten by the user in a specific region. The providing unit can also provide nutritional advice at a travel destination based on data on meals eaten by the user while traveling. For example, nutritional advice at a travel destination can be provided based on data on meals eaten by the user while traveling. The providing unit can also provide appropriate nutritional advice based on data on meals eaten in the vicinity of the user's home. For example, appropriate nutritional advice can be provided based on data on meals eaten in the vicinity of the user's home. In this way, by taking into consideration the geographical distribution of the dietary data, advice that reflects region-specific information can be provided.

[0055] The providing unit can improve the accuracy of advice by referring to literature related to the dietary data when providing the advice. The providing unit, for example, provides advice by referring to scientific research results related to the dietary data. For example, the advice is provided by referring to scientific research results related to the dietary data. The providing unit can also provide advice by referring to nutritional literature related to the dietary data. For example, the advice is provided by referring to nutritional literature related to the dietary data. The providing unit can also provide advice by referring to health guidelines related to the dietary data. For example, the advice is provided by referring to health guidelines related to the dietary data. In this way, by referring to literature related to the dietary data, it is possible to provide advice that is based on scientific evidence.

[0056] The providing unit can provide advice taking into consideration the market value of the dietary data when providing the advice. The providing unit can, for example, provide appropriate nutritional advice based on dietary data using expensive ingredients. For example, the providing unit can provide appropriate nutritional advice based on dietary data using expensive ingredients. The providing unit can also provide appropriate nutritional advice based on dietary data using ingredients with high market value. For example, the providing unit can provide appropriate nutritional advice based on dietary data using ingredients with high market value. The providing unit can also provide appropriate nutritional advice based on price information of ingredients purchased by the user. For example, the providing unit can provide appropriate nutritional advice based on price information of ingredients purchased by the user. In this way, by taking into consideration the market value of the dietary data, advice that reflects an economic perspective is possible.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past meal data input history and select the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze the time periods in which the user has previously input data and suggest the optimal input timing. The reception unit can also automatically complete specific meal data from the user's past input history. This allows the optimal input method to be selected by analyzing the user's past input history, improving input efficiency.

[0059] When inputting meal data, the reception unit can filter the data based on the user's current health condition and allergy information. For example, if the user attempts to input an ingredient to which the user has an allergy, a warning is displayed. Specific ingredients and cooking methods can also be recommended based on the user's current health condition. The reception unit can also refer to the user's health data and evaluate whether the input meal data is appropriate. This allows appropriate meal data to be input by filtering based on the user's health condition and allergy information.

[0060] When inputting meal data, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the meal data is input using voice recognition technology. If the user selects image input, the meal data can also be automatically extracted using image analysis technology. Furthermore, if the user selects text input, an input completion function can be provided to simplify input. This allows the most appropriate input means to be selected depending on the user's input method, thereby improving input efficiency.

[0061] When inputting meal data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data related to ingredients and dishes from that area can be input preferentially. If the user is traveling, meal data from the travel destination can also be input preferentially. Furthermore, if the user is at home, data on meals eaten at home in the past can also be input based on the user's previous meal data. In this way, highly relevant data can be input preferentially by taking into account the user's geographical location information.

[0062] When inputting meal data, the reception unit can analyze the user's social media activity and input related data. For example, it can automatically analyze photos of meals shared by the user on social media and input them as meal data. It can also analyze the content of the user's social media posts and input related meal data. It can also input related meal data by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently input related data by analyzing the user's social media activity.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The user inputs meal data into the reception unit. The meal data includes, for example, the types of ingredients, cooking methods, and intake amounts. The user inputs information such as the types of food eaten, cooking methods, and amounts of ingredients. Step 2: The analysis unit uses the generation AI to analyze the dietary data entered by the reception unit. The analysis is based on the frequency of high-temperature cooking and the type of ingredients. For example, the generation AI evaluates the risk of AGE accumulation due to high-temperature cooking based on the dietary data. Step 3: The visualization section visualizes the risks based on the results of the analysis by the analysis section, showing the risks in a visually easy-to-understand manner, such as by displaying high risks in red and low risks in green. Step 4: The provider provides optimal nutrition advice based on the risks visualized by the visualizer. For example, the advice may recommend low-temperature cooking, such as steaming or stewing, to reduce the risk of AGE accumulation.

[0065] (Example 2) A system according to an embodiment of the present invention uses a generating AI to analyze individual dietary data, visualize the risk of AGE (Advanced Glycation End-products) accumulation due to high-temperature cooking, and provide optimal nutritional advice. This system supports healthy aging through personalized recommendations related to the user's diet and lifestyle. For example, a user inputs dietary data, and a generating AI analyzes the data to assess risk and visually display the risk. Furthermore, the generating AI provides optimal nutritional advice based on the user's dietary data and lifestyle. This allows users to scientifically understand the impact of their diet on their health and maintain optimal nutritional balance. This allows the system to scientifically analyze the user's dietary data and provide optimal nutritional advice for healthy aging. For example, a user inputs dietary data, and a generating AI analyzes the data to assess risk and visually display the risk. Furthermore, the generating AI provides optimal nutritional advice based on the user's dietary data and lifestyle. This allows users to scientifically understand the impact of their diet on their health and maintain optimal nutritional balance.

[0066] The nutrition advice system according to the embodiment includes a reception unit, an analysis unit, a visualization unit, and a provision unit. The reception unit receives dietary data from a user. The dietary data includes, but is not limited to, the types of ingredients, cooking methods, and intake amounts. The reception unit receives information such as the types of dishes eaten by the user, cooking methods, and amounts of ingredients. The analysis unit uses a generation AI to analyze the dietary data received by the reception unit. The analysis is performed based on, but is not limited to, the frequency of high-temperature cooking and the types of ingredients. For example, the generation AI evaluates the risk of AGE accumulation due to high-temperature cooking based on the dietary data. The visualization unit visualizes the risk based on the analysis results of the analysis unit. The visualization indicates the risk in an easily understandable manner, for example, by displaying high risk in red and low risk in green. The provision unit provides optimal nutritional advice based on the risk visualized by the visualization unit. The advice may, for example, recommend low-temperature cooking, such as steaming or stewing, to reduce the risk of AGE accumulation. As a result, the nutrition advice system according to the embodiment can promote healthy aging by analyzing the dietary data of the user, visualizing risks, and providing optimal nutrition advice.

[0067] The analysis unit includes an evaluation unit that evaluates risk based on the frequency of high-temperature cooking or the type of ingredients. The evaluation unit evaluates risk, for example, based on the frequency of high-temperature cooking. For example, if high-temperature cooking such as fried or grilled foods is frequent, the evaluation unit evaluates that there is a high risk of AGE accumulation. The evaluation unit can also evaluate risk based on the type of ingredients. For example, if there is a lot of meat or fatty ingredients, the evaluation unit evaluates that there is a high risk of AGE accumulation. The evaluation unit can also evaluate risk based on both the frequency of high-temperature cooking and the type of ingredients. For example, if there is a lot of fried foods and a lot of meat, the evaluation unit evaluates that there is an even higher risk. This allows the analysis unit to evaluate risk more accurately by evaluating risk based on the frequency of high-temperature cooking and the type of ingredients.

[0068] The visualization unit includes a display unit that visualizes risks by color coding or graph display. The display unit, for example, visualizes risks by color coding. For example, high risks are displayed in red, and low risks are displayed in green. The display unit can also visualize risks by graph display. For example, risk fluctuations are displayed in a line graph. The display unit can also visualize risks by heat map display. For example, high risk areas are displayed in red, and low risk areas are displayed in blue. In this way, by visualizing risks by color coding or graph display, the visualization unit makes it easier for users to intuitively understand risks.

[0069] The providing unit includes an advice unit that recommends low-temperature cooking or specific ingredients. The advice unit, for example, recommends low-temperature cooking. For example, it recommends low-temperature cooking such as steaming or stewing. The advice unit can also recommend specific ingredients. For example, it can recommend ingredients that are considered to be good for health, such as vegetables and fruits. The advice unit can also advise avoiding specific ingredients. For example, it can advise avoiding high-fat ingredients and processed foods. In this way, by recommending low-temperature cooking or specific ingredients, the providing unit can reduce the risk of AGE accumulation and promote healthy aging.

[0070] The reception unit can estimate the user's emotions and adjust the timing of inputting meal data based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input meal data during a time when the user is able to relax. For example, if the user is relaxed, the reception unit prompts the user to input data immediately after eating. The reception unit can also set a reminder so that the user can postpone inputting meal data if the user is busy. For example, if the user is busy, the reception unit sends a reminder to input the data later. This allows the timing of inputting meal data to be adjusted according to the user's emotions, thereby allowing the data to be input at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The reception unit can analyze the user's past meal data input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit can analyze the time periods in which the user has previously input data and suggest the optimal input timing. The reception unit can also automatically complete specific meal data from the user's past input history. For example, similar meal data can be automatically completed based on meal data that the user has previously input. In this way, by analyzing the user's past input history, the optimal input method can be selected and input efficiency can be improved.

[0072] The reception unit can perform filtering based on the user's current health condition and allergy information when inputting meal data. For example, the reception unit displays a warning if the user attempts to input an ingredient to which the user is allergic. For example, the reception unit displays a warning message if the user attempts to input an ingredient to which the user is allergic. The reception unit can also recommend specific ingredients and cooking methods based on the user's current health condition. For example, it recommends specific ingredients and cooking methods based on the user's health condition. The reception unit can also refer to the user's health data and evaluate whether the input meal data is appropriate. For example, it evaluates whether the input meal data is appropriate based on the user's health data. In this way, appropriate meal data can be input by filtering based on the user's health condition and allergy information.

[0073] When inputting meal data, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs meal data using voice recognition technology. For example, if the user selects voice input, the reception unit inputs meal data using voice recognition technology. Furthermore, if the user selects image input, the reception unit can automatically extract meal data using image analysis technology. For example, if the user selects image input, the reception unit automatically extracts meal data using image analysis technology. Furthermore, if the user selects text input, the reception unit can also provide an input completion function to simplify input. For example, if the user selects text input, the reception unit provides an input completion function to simplify input. This allows the optimum input means to be selected according to the user's input method, thereby improving input efficiency.

[0074] The reception unit can estimate the user's emotions and determine the priority of meal data to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes input of meal data that can be easily input. For example, when the user is feeling stressed, the reception unit prioritizes input of meal data that can be easily input. Furthermore, when the user is relaxed, the reception unit can prompt the user to input detailed meal data. For example, when the user is relaxed, the reception unit prompts the user to input detailed meal data. Furthermore, when the user is in a hurry, the reception unit can prioritize input of only important meal data. For example, when the user is in a hurry, the reception unit prioritizes input of only important meal data. In this way, by determining the priority of meal data to be input according to the user's emotions, important data can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When inputting meal data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting data related to ingredients and dishes of that area. For example, when the user is in a specific area, the reception unit prioritizes inputting data related to ingredients and dishes of that area. Furthermore, when the user is traveling, the reception unit can also prioritize inputting meal data of the travel destination. For example, when the user is traveling, the reception unit prioritizes inputting meal data of the travel destination. Furthermore, when the user is at home, the reception unit can also input data about meals eaten at home in the past by referring to it. For example, when the user is at home, the reception unit inputs data about meals eaten at home in the past by referring to it. In this way, highly relevant data can be input preferentially by taking into account the user's geographical location information.

[0076] The reception unit can analyze the user's social media activities and input related data when inputting meal data. For example, the reception unit automatically analyzes photos of meals shared by the user on social media and inputs them as meal data. For example, the reception unit automatically analyzes photos of meals shared by the user on social media and inputs them as meal data. The reception unit can also analyze the content of the user's social media posts and input related meal data. For example, the reception unit can analyze the content of the user's social media posts and input related meal data. The reception unit can also input related meal data by referring to the activities of the user's friends on social media. For example, the reception unit can input related meal data by referring to the activities of the user's friends on social media. In this way, related data can be input efficiently by analyzing the user's social media activities.

[0077] The reception unit can customize the input method by reflecting the user's past feedback when inputting meal data. The reception unit, for example, improves the input method based on feedback on meal data previously input by the user. For example, the input method is improved based on feedback on meal data previously input by the user. The reception unit can also customize the input interface by referring to feedback previously provided by the user. For example, the input interface is customized by referring to feedback previously provided by the user. The reception unit can also analyze the user's past feedback and suggest an optimal input method. For example, the reception unit analyzes the user's past feedback and suggests an optimal input method. In this way, the input method can be optimized by reflecting the user's past feedback, and input efficiency can be improved.

[0078] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. For example, when the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, when the user is stressed, the analysis unit can provide concise analysis results. For example, when the user is stressed, the analysis unit provides concise analysis results. Furthermore, when the user is excited, the analysis unit can provide visually appealing analysis results. For example, when the user is excited, the analysis unit provides visually appealing analysis results. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the meal data. For example, the analysis unit performs a detailed analysis on meal data that is frequently cooked at high temperatures. For example, the analysis unit performs a detailed analysis on meal data that is frequently cooked at high temperatures. The analysis unit can also perform a concise analysis on meal data that is frequently cooked at low temperatures. For example, the analysis unit performs a concise analysis on meal data that is frequently cooked at low temperatures. The analysis unit can also perform a detailed analysis on meal data that contains a large amount of a specific ingredient. For example, the analysis unit performs a detailed analysis on meal data that contains a large amount of a specific ingredient. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the meal data.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the food category. For example, the analysis unit applies an algorithm that evaluates the risk of AGE accumulation to fried foods. For example, the analysis unit applies an algorithm that evaluates the risk of AGE accumulation to fried foods. The analysis unit can also apply an algorithm that evaluates the nutritional balance to simmered foods. For example, the analysis unit applies an algorithm that evaluates the nutritional balance to simmered foods. The analysis unit can also apply an algorithm that evaluates the vitamin and mineral content to salads. For example, the analysis unit applies an algorithm that evaluates the vitamin and mineral content to salads. In this way, by applying different analysis algorithms depending on the food category, more accurate analysis is possible.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, compares the analysis results with the user's past dietary data and adjusts the analysis algorithm. For example, it compares the analysis results with the user's past dietary data and adjusts the analysis algorithm. The analysis unit can also improve the accuracy of the analysis results based on the user's past feedback. For example, it improves the accuracy of the analysis results based on the user's past feedback. The analysis unit can also correct the current analysis results by referring to the user's past analysis results. For example, it corrects the current analysis results by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually appealing analysis result if the user is excited. For example, if the user is excited, the analysis unit provides a visually appealing analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide an optimal analysis result for the user. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the meal data. For example, the analysis unit prioritizes analysis of recently submitted meal data. For example, the analysis unit prioritizes analysis of recently submitted meal data. The analysis unit can also prioritize analysis of meal data submitted during a specific time period. For example, the analysis unit prioritizes analysis of meal data submitted during a specific time period. The analysis unit can also prioritize analysis of meal data specified by the user. For example, the analysis unit prioritizes analysis of meal data specified by the user. This enables efficient analysis by determining the priority of analysis based on the time of submission of the meal data.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the meal data. For example, the analysis unit prioritizes analyzing meal data that is frequently cooked at high temperatures. For example, the analysis unit prioritizes analyzing meal data that is frequently cooked at high temperatures. The analysis unit can also prioritize analyzing meal data that contains a large amount of a specific ingredient. For example, the analysis unit prioritizes analyzing meal data that contains a large amount of a specific ingredient. The analysis unit can also prioritize analyzing meal data related to the user's health condition. For example, the analysis unit prioritizes analyzing meal data related to the user's health condition. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the meal data.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. The analysis unit can also use concise terms if the user has only general knowledge. For example, if the user has only general knowledge, the analysis unit uses concise terms. The analysis unit can also select optimal terms based on the user's past feedback. For example, the analysis unit selects optimal terms based on the user's past feedback. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0086] The visualization unit can estimate the user's emotion and adjust the visualization criteria based on the estimated user's emotion. For example, when the user is relaxed, the visualization unit provides a detailed visualization result. For example, when the user is relaxed, the visualization unit provides a detailed visualization result. Furthermore, when the user is stressed, the visualization unit can provide a concise visualization result. For example, when the user is stressed, the visualization unit provides a concise visualization result. Furthermore, when the user is excited, the visualization unit can provide a visually appealing visualization result. For example, when the user is excited, the visualization unit provides a visually appealing visualization result. In this way, by adjusting the visualization criteria according to the user's emotion, it is possible to provide a visualization result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function using, 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.

[0087] The visualization unit can improve the accuracy of visualization by taking into account the interrelationships between the meal data during visualization. For example, the visualization unit emphasizes and visualizes meal data that is frequently cooked at high temperatures. For example, the visualization unit emphasizes and visualizes meal data that is frequently cooked at high temperatures. The visualization unit can also emphasize and visualize meal data that contains a large amount of a specific ingredient. For example, the visualization unit emphasizes and visualizes meal data that contains a large amount of a specific ingredient. The visualization unit can also emphasize and visualize meal data related to the user's health condition. For example, the visualization unit emphasizes and visualizes meal data related to the user's health condition. In this way, the accuracy of visualization can be improved by taking into account the interrelationships between the meal data.

[0088] The visualization unit can perform visualization while taking into consideration attribute information of the person who submitted the meal data. The visualization unit selects an appropriate visualization method, for example, according to the age of the user. For example, an appropriate visualization method is selected according to the age of the user. The visualization unit can also select an appropriate visualization method according to the gender of the user. For example, an appropriate visualization method is selected according to the gender of the user. The visualization unit can also select an appropriate visualization method according to the health condition of the user. For example, an appropriate visualization method is selected according to the health condition of the user. In this way, by taking into consideration attribute information of the person who submitted the meal data, it is possible to provide a more appropriate visualization result.

[0089] The visualization unit can weight the visualization based on the frequency of submission of meal data during visualization. The visualization unit, for example, emphasizes and visualizes frequently submitted meal data. For example, the visualization unit emphasizes and visualizes frequently submitted meal data. The visualization unit can also emphasize and visualize meal data submitted during a specific time period. For example, the visualization unit emphasizes and visualizes meal data submitted during a specific time period. The visualization unit can also emphasize and visualize meal data specified by the user. For example, the visualization unit emphasizes and visualizes meal data specified by the user. In this way, by weighting the visualization based on the frequency of submission of meal data, important data can be displayed with emphasis.

[0090] The visualization unit can estimate the user's emotion and adjust the order in which visualization results are displayed based on the estimated user's emotion. For example, if the user is relaxed, the visualization unit displays detailed visualization results first. For example, if the user is relaxed, the visualization unit displays detailed visualization results first. Furthermore, if the user is stressed, the visualization unit can also display concise visualization results first. For example, if the user is stressed, the visualization unit displays concise visualization results first. Furthermore, if the user is excited, the visualization unit can also display visually appealing visualization results first. For example, if the user is excited, the visualization unit displays visually appealing visualization results first. In this way, by adjusting the order in which visualization results are displayed according to the user's emotion, the results can be displayed in an order that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function using, 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.

[0091] The visualization unit can perform visualization while taking into consideration the geographical distribution of the meal data. For example, the visualization unit displays data on meals eaten by a user in a specific region on a map. For example, data on meals eaten by a user in a specific region on a map. The visualization unit can also geographically visualize data on meals eaten by a user while traveling. For example, data on meals eaten by a user while traveling is visualized geographically. The visualization unit can also geographically visualize data on meals eaten in the vicinity of the user's home. For example, data on meals eaten in the vicinity of the user's home is visualized geographically. In this way, by taking into consideration the geographical distribution of the meal data, visualization that reflects region-specific information is possible.

[0092] The visualization unit can improve the accuracy of visualization by referring to literature related to the dietary data during visualization. The visualization unit, for example, performs visualization by referring to scientific research results related to the dietary data. For example, the visualization unit performs visualization by referring to scientific research results related to the dietary data. The visualization unit can also perform visualization by referring to nutritional literature related to the dietary data. For example, the visualization unit can perform visualization by referring to nutritional literature related to the dietary data. The visualization unit can also perform visualization by referring to health guidelines related to the dietary data. For example, the visualization unit can perform visualization by referring to health guidelines related to the dietary data. In this way, by referring to literature related to the dietary data, visualization based on scientific evidence is possible.

[0093] The visualization unit can perform visualization taking into consideration the market value of the meal data when visualizing. For example, the visualization unit emphasizes and visualizes meal data that uses expensive ingredients. For example, the visualization unit emphasizes and visualizes meal data that uses expensive ingredients. The visualization unit can also emphasize and visualize meal data that uses ingredients with high market values. For example, the visualization unit emphasizes and visualizes meal data that uses ingredients with high market values. The visualization unit can also visualize meal data based on price information of ingredients purchased by the user. For example, the visualization unit visualizes meal data based on price information of ingredients purchased by the user. In this way, by taking into consideration the market value of the meal data, visualization that reflects an economic perspective is possible.

[0094] The providing unit can estimate the user's emotions and determine the priority of advice to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing dietary advice. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing dietary advice. The providing unit can also provide detailed nutritional advice if the user is relaxed. For example, if the user is relaxed, the providing unit can provide detailed nutritional advice. The providing unit can also prioritize providing concise advice if the user is in a hurry. For example, if the user is in a hurry, the providing unit can prioritize concise advice. In this way, by determining the priority of advice according to the user's emotions, it is possible to provide optimal advice for the user. Emotion estimation is realized using an emotion estimation function using, 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.

[0095] The providing unit can improve the accuracy of advice by taking into account the interrelationships between dietary data when providing the advice. For example, the providing unit provides advice recommending low-temperature cooking for dietary data that includes a lot of high-temperature cooking. For example, the providing unit provides advice recommending low-temperature cooking for dietary data that includes a lot of high-temperature cooking. The providing unit can also provide advice recommending a balanced diet for dietary data that includes a lot of specific ingredients. For example, the providing unit provides advice recommending a balanced diet for dietary data that includes a lot of specific ingredients. The providing unit can also provide appropriate nutritional advice based on dietary data related to the user's health condition. For example, the providing unit provides appropriate nutritional advice based on dietary data related to the user's health condition. In this way, more accurate advice can be provided by taking into account the interrelationships between dietary data.

[0096] When providing the advice, the providing unit can take into consideration attribute information of the person who submitted the dietary data. The providing unit can provide appropriate nutritional advice, for example, according to the age of the user. For example, appropriate nutritional advice is provided according to the age of the user. The providing unit can also provide appropriate nutritional advice according to the gender of the user. For example, appropriate nutritional advice is provided according to the gender of the user. The providing unit can also provide appropriate nutritional advice according to the health condition of the user. For example, appropriate nutritional advice is provided according to the health condition of the user. In this way, more appropriate advice can be provided by taking into consideration the attribute information of the person who submitted the dietary data.

[0097] The providing unit can weight the advice based on the frequency of submission of meal data when providing the advice. The providing unit, for example, provides detailed advice based on frequently submitted meal data. For example, detailed advice is provided based on frequently submitted meal data. The providing unit can also provide appropriate advice based on meal data submitted in a specific time period. For example, appropriate advice is provided based on meal data submitted in a specific time period. The providing unit can also provide focused advice based on meal data specified by the user. For example, focused advice is provided based on meal data specified by the user. In this way, by weighting the advice based on the frequency of submission of meal data, it is possible to provide advice based on important data.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the advice to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit displays detailed advice. For example, when the user is relaxed, detailed advice is displayed. Furthermore, the providing unit can also display concise advice when the user is stressed. For example, when the user is stressed, concise advice is displayed. Furthermore, the providing unit can also display visually appealing advice when the user is excited. For example, when the user is excited, visually appealing advice is displayed. In this way, by adjusting the display method of the advice according to the user's emotions, it is possible to provide advice that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, 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.

[0099] When providing advice, the providing unit can take into consideration the geographical distribution of the dietary data. The providing unit can provide region-specific nutritional advice based on, for example, data on meals eaten by the user in a specific region. For example, region-specific nutritional advice can be provided based on data on meals eaten by the user in a specific region. The providing unit can also provide nutritional advice at a travel destination based on data on meals eaten by the user while traveling. For example, nutritional advice at a travel destination can be provided based on data on meals eaten by the user while traveling. The providing unit can also provide appropriate nutritional advice based on data on meals eaten in the vicinity of the user's home. For example, appropriate nutritional advice can be provided based on data on meals eaten in the vicinity of the user's home. In this way, by taking into consideration the geographical distribution of the dietary data, advice that reflects region-specific information can be provided.

[0100] The providing unit can improve the accuracy of advice by referring to literature related to the dietary data when providing the advice. The providing unit, for example, provides advice by referring to scientific research results related to the dietary data. For example, the advice is provided by referring to scientific research results related to the dietary data. The providing unit can also provide advice by referring to nutritional literature related to the dietary data. For example, the advice is provided by referring to nutritional literature related to the dietary data. The providing unit can also provide advice by referring to health guidelines related to the dietary data. For example, the advice is provided by referring to health guidelines related to the dietary data. In this way, by referring to literature related to the dietary data, it is possible to provide advice that is based on scientific evidence.

[0101] The providing unit can provide advice taking into consideration the market value of the dietary data when providing the advice. The providing unit can, for example, provide appropriate nutritional advice based on dietary data using expensive ingredients. For example, the providing unit can provide appropriate nutritional advice based on dietary data using expensive ingredients. The providing unit can also provide appropriate nutritional advice based on dietary data using ingredients with high market value. For example, the providing unit can provide appropriate nutritional advice based on dietary data using ingredients with high market value. The providing unit can also provide appropriate nutritional advice based on price information of ingredients purchased by the user. For example, the providing unit can provide appropriate nutritional advice based on price information of ingredients purchased by the user. In this way, by taking into consideration the market value of the dietary data, advice that reflects an economic perspective is possible. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, visualization unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows a user to input dietary data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the dietary data using a generation AI. The visualization unit is realized by the output device 40 of the smart device 14, and visually displays the analysis results. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal nutritional advice and provides it to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, visualization unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input dietary data by voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the dietary data using a generation AI. The visualization unit is realized by the display of the smart glasses 214, and visually displays the analysis results. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal nutritional advice and provides it to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, visualization unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input dietary data by voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the dietary data using a generation AI. The visualization unit is realized by the display 343 of the headset-type terminal 314, and visually displays the analysis results. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal nutritional advice and provides it to the user through the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, visualization unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input dietary data by voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the dietary data using a generation AI. The visualization unit is realized by the display device of the robot 414, and visually displays the analysis results. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and generates optimal nutritional advice and provides it to the user through the speaker 240 of the robot 414.

[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] The reception unit can estimate the user's emotions and adjust the timing of meal data input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input meal data during a time when the user is able to relax. If the user is relaxed, the reception unit can prompt the user to input data immediately after eating. Furthermore, if the user is busy, a reminder can be set so that the input of meal data can be postponed. This allows the timing of meal data input to be adjusted according to the user's emotions, allowing data to be input at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. Also, if the user is excited, visually appealing analysis results can be provided. By adjusting the way the analysis is presented according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The visualization unit can estimate the user's emotions and adjust the visualization criteria based on the estimated user emotions. For example, if the user is relaxed, a detailed visualization result can be provided. If the user is stressed, a concise visualization result can be provided. Also, if the user is excited, a visually appealing visualization result can be provided. By adjusting the visualization criteria according to the user's emotions, it is possible to provide visualization results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] The providing unit can estimate the user's emotions and determine the priority of advice to provide based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize providing relaxing dietary advice. If the user is relaxed, it can also provide detailed nutritional advice. Also, if the user is in a hurry, it can prioritize providing concise advice. In this way, by determining the priority of advice according to the user's emotions, it is possible to provide the most suitable advice for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0107] The providing unit can estimate the user's emotions and adjust the display method of the advice to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed advice can be displayed. If the user is stressed, concise advice can be displayed. Also, if the user is excited, visually appealing advice can be displayed. In this way, by adjusting the display method of the advice according to the user's emotions, it is possible to provide advice that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0108] The reception unit can analyze the user's past meal data input history and select the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze the time periods in which the user has previously input data and suggest the optimal input timing. The reception unit can also automatically complete specific meal data from the user's past input history. This allows the optimal input method to be selected by analyzing the user's past input history, improving input efficiency.

[0109] When inputting meal data, the reception unit can filter the data based on the user's current health condition and allergy information. For example, if the user attempts to input an ingredient to which the user has an allergy, a warning is displayed. Specific ingredients and cooking methods can also be recommended based on the user's current health condition. The reception unit can also refer to the user's health data and evaluate whether the input meal data is appropriate. This allows appropriate meal data to be input by filtering based on the user's health condition and allergy information.

[0110] When inputting meal data, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the meal data is input using voice recognition technology. If the user selects image input, the meal data can also be automatically extracted using image analysis technology. Furthermore, if the user selects text input, an input completion function can be provided to simplify input. This allows the most appropriate input means to be selected depending on the user's input method, thereby improving input efficiency.

[0111] When inputting meal data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data related to ingredients and dishes from that area can be input preferentially. If the user is traveling, meal data from the travel destination can also be input preferentially. Furthermore, if the user is at home, data on meals eaten at home in the past can also be input based on the user's previous meal data. In this way, highly relevant data can be input preferentially by taking into account the user's geographical location information.

[0112] When inputting meal data, the reception unit can analyze the user's social media activity and input related data. For example, it can automatically analyze photos of meals shared by the user on social media and input them as meal data. It can also analyze the content of the user's social media posts and input related meal data. It can also input related meal data by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently input related data by analyzing the user's social media activity.

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

[0114] Step 1: The user inputs meal data into the reception unit. The meal data includes, for example, the types of ingredients, cooking methods, and intake amounts. The user inputs information such as the types of food eaten, cooking methods, and amounts of ingredients. Step 2: The analysis unit uses the generation AI to analyze the dietary data entered by the reception unit. The analysis is based on the frequency of high-temperature cooking and the type of ingredients. For example, the generation AI evaluates the risk of AGE accumulation due to high-temperature cooking based on the dietary data. Step 3: The visualization section visualizes the risks based on the results of the analysis by the analysis section, showing the risks in a visually easy-to-understand manner, such as by displaying high risks in red and low risks in green. Step 4: The provider provides optimal nutrition advice based on the risks visualized by the visualizer. For example, the advice may recommend low-temperature cooking, such as steaming or stewing, to reduce the risk of AGE accumulation.

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

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

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

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

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

[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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, 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. a reception unit for inputting meal data; an analysis unit that analyzes the dietary data input by the reception unit; a visualization unit that visualizes risks based on the results of the analysis by the analysis unit; a providing unit that provides optimal nutrition advice based on the risks visualized by the visualization unit; Equipped with A system characterized by:

2. The analysis unit Equipped with an evaluation unit that evaluates risks based on the frequency of high-temperature cooking or the type of food 2. The system of claim 1.

3. The visualization unit Equipped with a display that visualizes risks using color coding and graphs 2. The system of claim 1.

4. The providing unit Equipped with an advice section that recommends low-temperature cooking or specific ingredients 2. The system of claim 1.

5. The reception unit The system estimates the user's emotions and adjusts the timing of meal data input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past meal data entry history and select the optimal entry method 2. The system of claim 1.

7. The reception unit When entering meal data, filtering is performed based on the user's current health status and allergy information.

2. The system of claim 1.

8. The reception unit When inputting meal data, select the appropriate input method according to the user's input method.

2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize the food data to be input based on the estimated user emotions.

2. The system of claim 1.

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