system

Abstract

The system according to this embodiment aims to easily obtain nutritional information of food and to visually understand its effects. [Solution] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a generation unit. The reception unit scans the barcode of the food. The analysis unit analyzes the barcode information acquired by the reception unit and identifies the nutritional components. The provision unit provides the nutritional component information identified by the analysis unit. The generation unit generates what the user would look like if they continued to eat the food for three months, based on the nutritional component information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have presented challenges in easily obtaining nutritional information about food and visually understanding its effects.

[0005] The system according to this embodiment aims to easily obtain nutritional information of food and to visually understand its effects. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a generation unit. The reception unit scans the barcode of the food. The analysis unit analyzes the barcode information acquired by the reception unit and identifies the nutritional components. The provision unit provides the nutritional component information identified by the analysis unit. The generation unit generates what the user would look like if they continued to eat the food for three months, based on the nutritional component information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows easy acquisition of nutritional information about foods and visual understanding of their effects. [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 of form 1) A food analysis system according to an embodiment of the present invention scans food barcodes and analyzes the food's calories and ingredients using AI. This system allows users to instantly obtain nutritional information for a food simply by scanning the barcode. Based on the analysis results, the AI ​​then uses image generation AI to display what the user would look like if they continued to eat that food for three months. This helps prevent overeating and improve the effectiveness of their diet. For example, a user scans a food barcode using an app. For example, the user may use a smartphone camera to read the barcode of a food item purchased at a supermarket. This barcode information is then sent to AI. The AI ​​then analyzes the barcode information and analyzes the food's calories and ingredients. For example, based on the information obtained from the barcode, the food's nutritional components, such as calories, protein, fat, carbohydrates, vitamins, and minerals, are identified. The analysis results are then provided to the user. Based on the analysis results, the image generation AI then generates what the user would look like if they continued to eat that food for three months. For example, the system simulates how the user would gain weight and change their body shape if they continued to eat high-calorie foods. The simulation results are then displayed to the user as an image. This system allows users to review their eating habits and prevent overeating. It also improves the effectiveness of dieting. For example, by selecting healthy foods, users can help manage their weight and maintain their health. The food analysis system allows users to instantly obtain nutritional information about a food simply by scanning its barcode, and uses image generation AI to display what they would look like if they continued to eat that food for three months.

[0029] A food analysis system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a generation unit. The reception unit scans the barcode of a food product. For example, a user can use a smartphone camera to read the barcode of a food product purchased at a supermarket. The reception unit transmits the barcode information to an AI. The analysis unit analyzes the barcode information to identify the food's calories and ingredients. For example, the analysis unit identifies the food's nutritional components, such as calories, protein, fat, carbohydrates, vitamins, and minerals, based on the information obtained from the barcode. The analysis unit can use AI to analyze the food's nutritional components in detail. The provision unit provides the user with the nutritional component information identified by the analysis unit. For example, the nutritional component information can be displayed on the user's smartphone. The provision unit can use AI to provide the user with appropriate nutritional component information. Based on the nutritional component information provided by the provision unit, the generation unit generates a user's appearance if the user continues to eat that food for three months. For example, the system simulates how the user's weight gain and body shape change would occur if the user continued to eat high-calorie foods. The generation unit can use a generation AI to realistically simulate the user's appearance. As a result, the food analysis system of the embodiment allows a user to instantly obtain nutritional information about a food simply by scanning the food's barcode, and uses image generation AI to display what the user would look like if they continued to eat that food for three months.

[0030] The reception unit can analyze the user's past scan history and select the optimal scan method. For example, the reception unit can prioritize and suggest scan methods that the user has used frequently in the past. For example, the reception unit can select the most efficient scan method from the user's past scan history. The reception unit can also analyze the user's past scan history and suggest a scan method with the least amount of errors. In this way, the optimal scan method can be selected by analyzing the user's past scan history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past scan history data into a generation AI and have the generation AI select the optimal scan method.

[0031] When scanning a barcode, the reception unit can perform filtering based on the user's current meal plan and health condition. For example, if the user is on a diet, the reception unit can filter out high-calorie foods. For example, if the user needs to consume a specific nutrient, the reception unit can preferentially display foods containing that nutrient. The reception unit can also filter out foods that should be avoided based on the user's health condition. This makes it possible to provide more appropriate food information by filtering based on the user's current meal plan and health condition. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's meal plan and health condition data into the generation AI and have the generation AI perform the filtering.

[0032] When scanning a barcode, the reception unit can prioritize scanning highly relevant foods based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize scanning foods that are popular in that area. For example, if the user is traveling, the reception unit can prioritize scanning local specialties. Furthermore, if the user is in a specific store, the reception unit can prioritize scanning foods sold in that store. In this way, highly relevant foods can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant foods.

[0033] When scanning a barcode, the reception unit can analyze the user's social media activity and scan related foods. For example, the reception unit can prioritize scanning foods that the user is talking about on social media. For example, it can prioritize scanning foods recommended by the user's followers. The reception unit can also prioritize scanning foods that the user has shared on social media. In this way, related foods can be scanned by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI select related foods.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the food. For example, in the case of a high-calorie food, the analysis unit performs a detailed nutritional analysis. For example, in the case of a low-calorie food, the analysis unit can perform a brief nutritional analysis. Furthermore, in the case of a food in which a specific nutrient is important, the analysis unit can also perform an analysis that focuses on that nutrient. In this way, by adjusting the level of detail of the analysis based on the importance of the food, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the food category. For example, in the case of processed foods, the analysis unit can apply an analysis algorithm for additives. For example, in the case of fresh foods, the analysis unit can apply an analysis algorithm for freshness. Furthermore, in the case of beverages, the analysis unit can also apply an analysis algorithm for sugar and caffeine. In this way, by applying different analysis algorithms depending on the food category, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0036] During analysis, the analysis unit can identify nutritional components based on the food's place of origin information. The analysis unit, for example, identifies nutritional components of food produced in a specific region. For example, the analysis unit can identify nutritional components taking into account the climatic conditions of the production area. The analysis unit can also identify nutritional components taking into account the farming method of the production area. This allows for more accurate identification of nutritional components by taking into account the food's place of origin information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food's place of origin information data into the generation AI and have the generation AI identify the nutritional components.

[0037] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the food. The analysis unit can improve the accuracy of the analysis by, for example, referring to the latest nutritional research. For example, the analysis unit can improve the accuracy of the analysis by referring to academic papers on the ingredients of the food. The analysis unit can also improve the accuracy of the analysis by referring to literature on the health effects of the food. In this way, the accuracy of the analysis is improved by referring to literature related to the food. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0038] The providing unit can provide appropriate information by referring to the user's past dietary history when providing the information. The providing unit can provide optimal information based on, for example, the nutritional components the user has previously consumed. For example, it can provide information that takes nutritional balance into consideration based on the user's past dietary history. The providing unit can also analyze the user's past dietary history and provide healthy information. This allows optimal information to be provided by referring to the user's past dietary history. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past dietary history data into the generation AI and cause the generation AI to provide appropriate information.

[0039] The information provider can customize the information based on the user's current health status at the time of delivery. For example, if the user is on a diet, the provider can provide information on low-calorie foods. For example, if the user needs to consume a specific nutrient, the provider can provide information on foods containing that nutrient. The provider can also provide information on foods to avoid based on the user's health status. This allows for more appropriate information to be provided by customizing the information based on the user's current health status. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's health status data into a generating AI and have the generating AI perform the information customization.

[0040] The information provider can provide appropriate information based on the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can provide information on food available in that region. For example, if the user is traveling, it can provide information on local specialties. The information provider can also provide information on food sold in a specific store if the user is in that store. This allows for optimal information provision by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing appropriate information.

[0041] At the time of providing, the providing unit can analyze the user's social media activity and provide the information. The providing unit can, for example, provide information on foods that the user is talking about on social media. For example, it can provide information on foods recommended by the user's followers. The providing unit can also provide information on foods that the user has shared on social media. This allows for more relevant information to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media activity data into the generation AI and have the generation AI provide the information.

[0042] At the time of generation, the generation unit can analyze the user's past dietary history to generate an appropriate image. The generation unit, for example, generates an image that reflects the effects of foods the user has consumed in the past. For example, it can generate an image of what would happen if the user continued to eat a healthy diet based on the user's past dietary history. The generation unit can also analyze the user's past dietary history to generate an image of what would happen if the user continued to binge eat. In this way, by analyzing the user's past dietary history, more appropriate images can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past dietary history data into the generation AI and cause the generation AI to generate an appropriate image.

[0043] The generation unit can customize the image based on the user's current health condition at the time of generation. For example, if the user is on a diet, the generation unit can generate an image that reflects weight loss. For example, if the user needs to ingest a specific nutrient, the generation unit can generate an image that reflects the effects of that nutrient. The generation unit can also generate an image that reflects a healthy body shape based on the user's health condition. This allows for more appropriate image generation by customizing the image based on the user's current health condition. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health condition data into the generation AI and have the generation AI customize the image.

[0044] At the time of generation, the generation unit can generate an appropriate image based on the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate an image that reflects the local specialty. For example, if the user is traveling, the generation unit can generate an image with the scenery of the travel destination as the background. Furthermore, if the user is in a specific store, the generation unit can also generate an image that reflects the products of that store. This allows for more appropriate image generation by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information data into the generation AI and cause the generation AI to generate an appropriate image.

[0045] At the time of generation, the generation unit can generate an image by analyzing the user's social media activity. The generation unit, for example, generates an image that reflects foods that the user is talking about on social media. For example, it can generate an image that reflects foods recommended by the user's followers. The generation unit can also generate an image that reflects foods that the user has shared on social media. This allows for more relevant image generation by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to generate an image.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's past dietary history and detect deficiencies of specific nutrients. For example, it can identify vitamin or mineral deficiencies based on data on foods the user has previously consumed. The analysis unit then identifies foods to compensate for this deficiency, and the provision unit can suggest those foods to the user. This allows the user to receive specific advice on improving their nutritional balance. The generation unit can also generate images that simulate changes in the user's health condition if the user ingests the suggested foods.

[0048] The providing unit can detect specific eating patterns based on the user's past eating history and provide nutritional advice based on this. For example, if the user has frequently consumed high-calorie foods in the past, the providing unit can suggest low-calorie substitute foods. The analysis unit then performs a detailed analysis of the nutritional components of these substitute foods, and the providing unit can provide this information to the user. The generating unit can also generate an image simulating changes in the user's health condition if the user consumes the suggested substitute foods.

[0049] The reception unit can suggest foods that address local health issues based on the user's geographic location information. For example, if vitamin D deficiency is a problem in a particular region, the reception unit can suggest foods that are high in vitamin D to users in that region. The analysis unit then performs a detailed analysis of the nutritional components of these foods, and the provision unit can provide that information to the user. The generation unit can also generate an image that simulates changes in the user's health status if the user ingests the suggested foods.

[0050] The providing unit can analyze the user's social media activity and identify foods that the user is interested in. For example, it can prioritize and suggest foods that the user frequently mentions on social media. Next, the analysis unit can perform a detailed analysis of the nutritional components of these foods, and the providing unit can provide that information to the user. The generating unit can also generate an image that simulates changes in the user's health status if the user consumes the suggested foods.

[0051] The analysis unit can detect excessive intake of specific nutrients based on the user's past dietary history. For example, if the user has consumed excessive amounts of sugar in the past, the analysis unit analyzes the effects of this. The provision unit then provides these analysis results to the user, and the generation unit can generate an image simulating changes in the user's health status if the user consumes the suggested foods. This allows the user to receive specific advice based on their own dietary history, which can be useful for health management.

[0052] The generation unit can generate images that reflect the unique food culture of a region based on the user's geographical location. For example, if the user is in a specific region, it can generate images that reflect the traditional dishes of that region. Next, the analysis unit can analyze the nutritional components of these foods in detail, and the provision unit can provide this information to the user. The generation unit can also generate images that simulate changes in the user's health if they consume the suggested foods. This allows the user to receive specific advice based on their geographical location, which can be used to help manage their health.

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

[0054] Step 1: The reception unit scans the barcode of the food item. For example, a user can use their smartphone camera to read the barcode of food purchased at a supermarket. The reception unit then sends the barcode information to the AI. Step 2: The analysis unit analyzes the barcode information to identify the calories and ingredients of the food. For example, based on the information obtained from the barcode, it identifies the calories, protein, fat, carbohydrates, vitamins, minerals, and other nutritional components of the food. The analysis unit can use AI to analyze the nutritional components of the food in detail. Step 3: The providing unit provides the user with the nutritional information identified by the analysis unit. For example, the nutritional information can be displayed on the user's smartphone. The providing unit can use AI to provide the user with appropriate nutritional information. Step 4: The generation unit generates a model of the user after consuming the food for three months, based on the nutritional information provided by the supply unit. For example, if the user continues to eat high-calorie foods, the generation unit simulates how their weight will increase and their body shape will change. The generation unit can realistically simulate the user's appearance using generation AI.

[0055] (Example 2) A food analysis system according to an embodiment of the present invention scans food barcodes and analyzes the food's calories and ingredients using AI. This system allows users to instantly obtain nutritional information for a food simply by scanning the barcode. Based on the analysis results, the AI ​​then uses image generation AI to display what the user would look like if they continued to eat that food for three months. This helps prevent overeating and improve the effectiveness of their diet. For example, a user scans a food barcode using an app. For example, the user may use a smartphone camera to read the barcode of a food item purchased at a supermarket. This barcode information is then sent to AI. The AI ​​then analyzes the barcode information and analyzes the food's calories and ingredients. For example, based on the information obtained from the barcode, the food's nutritional components, such as calories, protein, fat, carbohydrates, vitamins, and minerals, are identified. The analysis results are then provided to the user. Based on the analysis results, the image generation AI then generates what the user would look like if they continued to eat that food for three months. For example, the system simulates how the user would gain weight and change their body shape if they continued to eat high-calorie foods. The simulation results are then displayed to the user as an image. This system allows users to review their eating habits and prevent overeating. It also improves the effectiveness of dieting. For example, by selecting healthy foods, users can help manage their weight and maintain their health. The food analysis system allows users to instantly obtain nutritional information about a food simply by scanning its barcode, and uses image generation AI to display what they would look like if they continued to eat that food for three months.

[0056] A food analysis system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a generation unit. The reception unit scans the barcode of a food product. For example, a user can use a smartphone camera to read the barcode of a food product purchased at a supermarket. The reception unit transmits the barcode information to an AI. The analysis unit analyzes the barcode information to identify the food's calories and ingredients. For example, the analysis unit identifies the food's nutritional components, such as calories, protein, fat, carbohydrates, vitamins, and minerals, based on the information obtained from the barcode. The analysis unit can use AI to analyze the food's nutritional components in detail. The provision unit provides the user with the nutritional component information identified by the analysis unit. For example, the nutritional component information can be displayed on the user's smartphone. The provision unit can use AI to provide the user with appropriate nutritional component information. Based on the nutritional component information provided by the provision unit, the generation unit generates a user's appearance if the user continues to eat that food for three months. For example, the system simulates how the user's weight gain and body shape change would occur if the user continued to eat high-calorie foods. The generation unit can use a generation AI to realistically simulate the user's appearance. As a result, the food analysis system of the embodiment allows a user to instantly obtain nutritional information about a food simply by scanning the food's barcode, and uses image generation AI to display what the user would look like if they continued to eat that food for three months.

[0057] The reception unit can estimate the user's emotions and adjust the timing of barcode scanning based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the scan to help them relax. For example, if the user is in a hurry, the reception unit can speed up the scan to allow them to quickly obtain information. The reception unit can also adjust the scan timing if the user is excited to ensure accurate information is obtained. By adjusting the timing of barcode scanning according to the user's emotions, scanning can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0058] The reception unit can analyze the user's past scan history and select the optimal scan method. For example, the reception unit can prioritize and suggest scan methods that the user has used frequently in the past. For example, the reception unit can select the most efficient scan method from the user's past scan history. The reception unit can also analyze the user's past scan history and suggest a scan method with the least amount of errors. In this way, the optimal scan method can be selected by analyzing the user's past scan history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past scan history data into a generation AI and have the generation AI select the optimal scan method.

[0059] When scanning a barcode, the reception unit can perform filtering based on the user's current meal plan and health condition. For example, if the user is on a diet, the reception unit can filter out high-calorie foods. For example, if the user needs to consume a specific nutrient, the reception unit can preferentially display foods containing that nutrient. The reception unit can also filter out foods that should be avoided based on the user's health condition. This makes it possible to provide more appropriate food information by filtering based on the user's current meal plan and health condition. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's meal plan and health condition data into the generation AI and have the generation AI perform the filtering.

[0060] The reception unit can estimate the user's emotions and prioritize the foods to be scanned based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can prioritize scanning foods that have a relaxing effect. For example, if the user is in a hurry, the reception unit can prioritize scanning foods that are easy to scan. Furthermore, if the user is excited, the reception unit can prioritize scanning healthy foods. This allows for more appropriate food information to be provided by prioritizing foods to be scanned according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0061] When scanning a barcode, the reception unit can prioritize scanning highly relevant foods based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize scanning foods that are popular in that area. For example, if the user is traveling, the reception unit can prioritize scanning local specialties. Furthermore, if the user is in a specific store, the reception unit can prioritize scanning foods sold in that store. In this way, highly relevant foods can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant foods.

[0062] When scanning a barcode, the reception unit can analyze the user's social media activity and scan related foods. For example, the reception unit can prioritize scanning foods that the user is talking about on social media. For example, it can prioritize scanning foods recommended by the user's followers. The reception unit can also prioritize scanning foods that the user has shared on social media. In this way, related foods can be scanned by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI select related foods.

[0063] The analysis unit can estimate the user's emotions and adjust the nutritional analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed nutritional analysis. For example, if the user is in a hurry, the analysis unit can perform a concise nutritional analysis. Furthermore, if the user is excited, the analysis unit can perform a visually easy-to-understand nutritional analysis. This allows for more appropriate analysis by adjusting the nutritional analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the food. For example, in the case of a high-calorie food, the analysis unit performs a detailed nutritional analysis. For example, in the case of a low-calorie food, the analysis unit can perform a brief nutritional analysis. Furthermore, in the case of a food in which a specific nutrient is important, the analysis unit can also perform an analysis that focuses on that nutrient. In this way, by adjusting the level of detail of the analysis based on the importance of the food, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the food category. For example, in the case of processed foods, the analysis unit can apply an analysis algorithm for additives. For example, in the case of fresh foods, the analysis unit can apply an analysis algorithm for freshness. Furthermore, in the case of beverages, the analysis unit can also apply an analysis algorithm for sugar and caffeine. In this way, by applying different analysis algorithms depending on the food category, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0066] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] During analysis, the analysis unit can identify nutritional components based on the food's place of origin information. The analysis unit, for example, identifies nutritional components of food produced in a specific region. For example, the analysis unit can identify nutritional components taking into account the climatic conditions of the production area. The analysis unit can also identify nutritional components taking into account the farming method of the production area. This allows for more accurate identification of nutritional components by taking into account the food's place of origin information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food's place of origin information data into the generation AI and have the generation AI identify the nutritional components.

[0068] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the food. The analysis unit can improve the accuracy of the analysis by, for example, referring to the latest nutritional research. For example, the analysis unit can improve the accuracy of the analysis by referring to academic papers on the ingredients of the food. The analysis unit can also improve the accuracy of the analysis by referring to literature on the health effects of the food. In this way, the accuracy of the analysis is improved by referring to literature related to the food. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0069] The service provider can estimate the user's emotions and adjust the method of providing nutritional information based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the method of providing nutritional information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The providing unit can provide appropriate information by referring to the user's past dietary history when providing the information. The providing unit can provide optimal information based on, for example, the nutritional components the user has previously consumed. For example, it can provide information that takes nutritional balance into consideration based on the user's past dietary history. The providing unit can also analyze the user's past dietary history and provide healthy information. This allows optimal information to be provided by referring to the user's past dietary history. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past dietary history data into the generation AI and cause the generation AI to provide appropriate information.

[0071] The information provider can customize the information based on the user's current health status at the time of delivery. For example, if the user is on a diet, the provider can provide information on low-calorie foods. For example, if the user needs to consume a specific nutrient, the provider can provide information on foods containing that nutrient. The provider can also provide information on foods to avoid based on the user's health status. This allows for more appropriate information to be provided by customizing the information based on the user's current health status. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's health status data into a generating AI and have the generating AI perform the information customization.

[0072] The information provider can estimate the user's emotions and prioritize information based on those emotions. For example, if the user is stressed, the information provider may prioritize providing information about foods with relaxing effects. For example, if the user is in a hurry, it may prioritize providing concise information. Also, if the user is excited, the information provider may prioritize providing information about healthy foods. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The information provider can provide appropriate information based on the user's geographical location at the time of delivery. For example, if the user is in a specific region, the information provider can provide information on food available in that region. For example, if the user is traveling, it can provide information on local specialties. The information provider can also provide information on food sold in a specific store if the user is in that store. This allows for optimal information provision by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing appropriate information.

[0074] At the time of providing, the providing unit can analyze the user's social media activity and provide the information. The providing unit can, for example, provide information on foods that the user is talking about on social media. For example, it can provide information on foods recommended by the user's followers. The providing unit can also provide information on foods that the user has shared on social media. This allows for more relevant information to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media activity data into the generation AI and have the generation AI provide the information.

[0075] The generation unit can estimate the user's emotions and adjust the representation of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate images with a calm expression. For example, if the user is in a hurry, it can generate concise and to-the-point images. The generation unit can also generate images with visually stimulating effects if the user is excited. In this way, by adjusting the representation of images according to the user's emotions, more appropriate images can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0076] At the time of generation, the generation unit can analyze the user's past dietary history to generate an appropriate image. The generation unit, for example, generates an image that reflects the effects of foods the user has consumed in the past. For example, it can generate an image of what would happen if the user continued to eat a healthy diet based on the user's past dietary history. The generation unit can also analyze the user's past dietary history to generate an image of what would happen if the user continued to binge eat. In this way, by analyzing the user's past dietary history, more appropriate images can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past dietary history data into the generation AI and cause the generation AI to generate an appropriate image.

[0077] The generation unit can customize the image based on the user's current health condition at the time of generation. For example, if the user is on a diet, the generation unit can generate an image that reflects weight loss. For example, if the user needs to ingest a specific nutrient, the generation unit can generate an image that reflects the effects of that nutrient. The generation unit can also generate an image that reflects a healthy body shape based on the user's health condition. This allows for more appropriate image generation by customizing the image based on the user's current health condition. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health condition data into the generation AI and have the generation AI customize the image.

[0078] The generation unit can estimate the user's emotions and determine the priority of images to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating images with a relaxing effect. For example, if the user is in a hurry, the generation unit can prioritize generating images that are concise and to the point. Furthermore, if the user is excited, the generation unit can prioritize generating images with visually stimulating effects. This allows for more appropriate image generation by determining the priority of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0079] At the time of generation, the generation unit can generate an appropriate image based on the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate an image that reflects the local specialty. For example, if the user is traveling, the generation unit can generate an image with the scenery of the travel destination as the background. Furthermore, if the user is in a specific store, the generation unit can also generate an image that reflects the products of that store. This allows for more appropriate image generation by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information data into the generation AI and cause the generation AI to generate an appropriate image.

[0080] At the time of generation, the generation unit can generate an image by analyzing the user's social media activity. The generation unit, for example, generates an image that reflects foods that the user is talking about on social media. For example, it can generate an image that reflects foods recommended by the user's followers. The generation unit can also generate an image that reflects foods that the user has shared on social media. This allows for more relevant image generation by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to generate an image. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit scans the barcode of the food using the camera 42 of the smart device 14 and transmits the barcode information to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the barcode information to identify the calories and ingredients of the food. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the analysis results to the user. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and simulates the user's appearance using image generation AI and displays it on the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit scans the barcode of the food using the camera 42 of the smart glasses 214 and transmits the barcode information to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the barcode information to identify the calories and ingredients of the food. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the analysis results to the user. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and simulates the user's appearance using image generation AI and displays it on the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and generation 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 scans the barcode of a food item using the camera 42 of the headset-type terminal 314 and transmits the barcode information to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the barcode information to identify the calories and ingredients of the food item. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the analysis results to the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates the user's appearance using image generation AI and displays it on the display of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit scans the barcode of a food item using the camera 42 of the robot 414 and transmits the barcode information to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the barcode information to identify the calories and ingredients of the food item. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the analysis results to the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates the user's appearance using image generation AI and displays it on the display of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the user's past dietary history and detect deficiencies of specific nutrients. For example, it can identify vitamin or mineral deficiencies based on data on foods the user has previously consumed. The analysis unit then identifies foods to compensate for this deficiency, and the provision unit can suggest those foods to the user. This allows the user to receive specific advice on improving their nutritional balance. The generation unit can also generate images that simulate changes in the user's health condition if the user ingests the suggested foods.

[0083] The analysis unit can estimate the user's emotions and customize food options based on the estimated emotions. For example, if the user is feeling stressed, foods with stress-reducing effects are preferentially suggested. Next, the provision unit provides the user with nutritional information on these foods, and the generation unit can generate an image simulating changes in health status when these foods are consumed. This allows the user to make food choices according to their emotional state and lead a healthier life.

[0084] The providing unit can detect specific eating patterns based on the user's past eating history and provide nutritional advice based on this. For example, if the user has frequently consumed high-calorie foods in the past, the providing unit can suggest low-calorie substitute foods. The analysis unit then performs a detailed analysis of the nutritional components of these substitute foods, and the providing unit can provide this information to the user. The generating unit can also generate an image simulating changes in the user's health condition if the user consumes the suggested substitute foods.

[0085] The reception unit can suggest foods that address local health issues based on the user's geographic location information. For example, if vitamin D deficiency is a problem in a particular region, the reception unit can suggest foods that are high in vitamin D to users in that region. The analysis unit then performs a detailed analysis of the nutritional components of these foods, and the provision unit can provide that information to the user. The generation unit can also generate an image that simulates changes in the user's health status if the user ingests the suggested foods.

[0086] The analysis unit can estimate the user's emotions and customize the nutritional analysis results based on the estimated emotions. For example, if the user is relaxed, a detailed nutritional analysis can be performed, and if the user is in a hurry, a brief nutritional analysis can be performed. The providing unit then provides these analysis results to the user, and the generating unit can generate an image simulating changes in the user's health status if the user consumes the suggested foods. This allows the user to receive information tailored to their emotional state and make more appropriate dietary choices.

[0087] The providing unit can analyze the user's social media activity and identify foods that the user is interested in. For example, it can prioritize and suggest foods that the user frequently mentions on social media. Next, the analysis unit can perform a detailed analysis of the nutritional components of these foods, and the providing unit can provide that information to the user. The generating unit can also generate an image that simulates changes in the user's health status if the user consumes the suggested foods.

[0088] The reception unit can estimate the user's emotions and adjust the timing of barcode scanning based on those emotions. For example, if the user is feeling stressed, the scanning timing can be delayed to allow them to relax. Next, the analysis unit can analyze the nutritional components of the scanned food in detail, and the provision unit can provide that information to the user. The generation unit can also generate images that simulate changes in the user's health if they consume the suggested food. This allows users to scan at a timing that suits their emotional state and receive more appropriate information.

[0089] The analysis unit can detect excessive intake of specific nutrients based on the user's past dietary history. For example, if the user has consumed excessive amounts of sugar in the past, the analysis unit analyzes the effects of this. The provision unit then provides these analysis results to the user, and the generation unit can generate an image simulating changes in the user's health status if the user consumes the suggested foods. This allows the user to receive specific advice based on their own dietary history, which can be useful for health management.

[0090] The information delivery unit can estimate the user's emotions and adjust the way nutritional information is presented based on those emotions. For example, if the user is feeling anxious, it can provide a simple and highly visible display. Next, the analysis unit can analyze the nutritional components of the scanned food in detail, and the information delivery unit can then provide that information to the user. The generation unit can also generate images that simulate changes in the user's health if they were to consume the suggested food. This allows the user to receive information tailored to their emotional state and make more appropriate food choices.

[0091] The generation unit can generate images that reflect the unique food culture of a region based on the user's geographical location. For example, if the user is in a specific region, it can generate images that reflect the traditional dishes of that region. Next, the analysis unit can analyze the nutritional components of these foods in detail, and the provision unit can provide this information to the user. The generation unit can also generate images that simulate changes in the user's health if they consume the suggested foods. This allows the user to receive specific advice based on their geographical location, which can be used to help manage their health.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit scans the barcode of the food item. For example, a user can use their smartphone camera to read the barcode of food purchased at a supermarket. The reception unit then sends the barcode information to the AI. Step 2: The analysis unit analyzes the barcode information to identify the calories and ingredients of the food. For example, based on the information obtained from the barcode, it identifies the calories, protein, fat, carbohydrates, vitamins, minerals, and other nutritional components of the food. The analysis unit can use AI to analyze the nutritional components of the food in detail. Step 3: The providing unit provides the user with the nutritional information identified by the analysis unit. For example, the nutritional information can be displayed on the user's smartphone. The providing unit can use AI to provide the user with appropriate nutritional information. Step 4: The generation unit generates a model of the user after consuming the food for three months, based on the nutritional information provided by the supply unit. For example, if the user continues to eat high-calorie foods, the generation unit simulates how their weight will increase and their body shape will change. The generation unit can realistically simulate the user's appearance using generation AI.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0141] 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 desk that scans food barcodes; an analysis unit that analyzes the barcode information acquired by the reception unit and identifies nutritional components; a providing unit that provides nutritional component information identified by the analyzing unit; a generation unit that generates an appearance of the user if the user continues to eat the food for three months based on the nutritional component information provided by the provision unit. A system characterized by:

2. The reception unit Estimate the user's emotion and adjust the timing of barcode scanning based on the estimated user emotion.

2. The system of claim 1.

3. The reception unit Analyze users' past scanning history and select the appropriate scanning method 2. The system of claim 1.

4. The reception unit Filtering based on the user's current diet plan and health status when scanning a barcode 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the foods to scan based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Prioritize relevant food items based on the user's geographic location when scanning barcodes 2. The system of claim 1.

7. The reception unit When scanning a barcode, it analyzes the user's social media activity and scans related foods.

2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the analysis method of nutritional components based on the estimated user emotions.

2. The system of claim 1.

Images