System

Abstract

An object of a system according to an embodiment is to provide appropriate meal advice in consideration of individual needs and restrictions of a user.SOLUTION: A system includes a question analysis part, an information provision part, a history analysis part, and a plan suggestion part. The question analysis unit analyzes a question of a user. The information providing unit provides appropriate information based on the question analyzed by the question analysis unit. The history analysis unit analyzes a past meal history of the user. The plan suggestion unit suggests a healthy meal plan based on the meal history analyzed by the history analysis unit and the set goal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide appropriate dietary advice that takes into account the individual needs and restrictions of users.

[0005] The system according to the embodiment aims to provide appropriate dietary advice that takes into account the individual needs and restrictions of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit, an information provision unit, a history analysis unit, and a plan proposal unit. The question analysis unit analyzes a user's question. The information provision unit provides appropriate information based on the question analyzed by the question analysis unit. The history analysis unit analyzes the user's past eating history. The plan proposal unit proposes a healthy eating plan based on the eating history analyzed by the history analysis unit and the set goals. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate dietary advice that takes into account the individual needs and restrictions of the user. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A food advice system according to an embodiment of the present invention allows users to ask an AI chatbot questions about recipes, ingredient purchasing information, nutritional information, etc., and the AI ​​generator provides appropriate information and advice taking into account the user's individual needs and restrictions, and suggests healthy meal plans based on the user's past eating history and set goals. This allows the food advice system to provide food advice tailored to the user's needs and help them achieve a healthy diet.

[0029] A food advice system according to an embodiment includes a question analysis unit, an information provision unit, a history analysis unit, and a plan proposal unit. The question analysis unit analyzes a user's question. For example, if a user asks, "Please tell me an easy dinner recipe," the question analysis unit analyzes the question and prepares to provide appropriate information. The question analysis unit can accept the user's question in text or audio format. The information provision unit provides appropriate information based on the question analyzed by the question analysis unit. For example, the generation AI generates an answer to the user's question using a text generation AI (e.g., LLM). The generation AI can also provide recipes, ingredient purchasing information, and nutritional advice based on the user's needs. The history analysis unit analyzes the user's past eating history. For example, the history analysis unit analyzes the user's past meal history, such as the types of meals the user has eaten, the calorie intake, and meal times, to understand the user's eating patterns. The history analysis unit can also analyze the user's past purchase history and propose recipes based on the user's preferences and budget. The plan proposal unit proposes a healthy eating plan based on the eating history analyzed by the history analysis unit and the user's set goals. For example, if a user wants to lose weight, the AI ​​generator can suggest a specific meal plan. The AI ​​generator can also provide individually customized nutritional advice taking into account the user's health condition and medical history. This allows the food advice system according to the embodiment to provide appropriate information in response to the user's questions and suggest a healthy meal plan based on the user's past eating history and goals. For example, the user can receive food advice tailored to their needs and limitations, helping them achieve a healthy diet.

[0030] The history analysis unit can analyze the user's past purchase history and suggest recipes based on the user's preferences and budget. For example, the history analysis unit analyzes the user's past purchase history to identify frequently purchased ingredients and budget ranges. For example, based on the chicken and vegetables the user frequently purchases, recipes such as stir-fried chicken and vegetables or salads can be suggested. The history analysis unit can also suggest ingredients that fit the user's budget based on the user's purchase history. For example, economical recipes can be suggested based on ingredients that the user can purchase on a low budget. In this way, the user's past purchase history can be analyzed and recipes can be suggested based on the user's preferences and budget.

[0031] The information providing unit can provide recipes using seasonal ingredients based on the season and local specialties. For example, the information providing unit refers to seasonal specialty product data and suggests recipes using seasonal ingredients. For example, it provides recipes using asparagus in spring and recipes using pumpkin in autumn. The information providing unit can also consider local specialties and suggest recipes using local ingredients. For example, it provides recipes using fresh vegetables and fruits produced in a specific region. This makes it possible to provide recipes using seasonal ingredients taking into consideration the season and local specialties.

[0032] The information providing unit can provide recipes for beginners or time-saving recipes based on the difficulty level and cooking time of the dish. For example, the information providing unit considers the user's cooking skill level and suggests easy recipes for beginners. For example, it provides easy recipes such as sandwiches and salads to a user who is cooking for the first time. The information providing unit can also consider cooking time and suggest time-saving recipes. For example, it provides a busy user with recipes for pasta or stir-fry that can be made in 10 minutes or less. In this way, it is possible to provide recipes for beginners or time-saving recipes taking into consideration the difficulty level and cooking time of the dish.

[0033] The information providing unit can prioritize suggesting eco-friendly options when providing food purchasing information. For example, the information providing unit provides information on locally produced food ingredients in order to prioritize suggesting eco-friendly options. For example, fresh vegetables and fruits produced in the user's area are suggested. The information providing unit can also prioritize suggesting organic food ingredients. For example, organically certified food ingredients are suggested. This allows eco-friendly options to be prioritized.

[0034] The information providing unit can provide individually customized nutritional advice based on the user's health condition and medical history. The information providing unit provides individually customized nutritional advice based on, for example, the user's health condition and medical history. For example, the information providing unit can suggest low-carbohydrate ingredients and recipes to a user with diabetes. The information providing unit can also provide advice on supplementing specific nutrients depending on the user's health condition. For example, the information providing unit can suggest ingredients and recipes that are high in iron to a user with an iron deficiency. This makes it possible to provide individually customized nutritional advice that takes into account the user's health condition and medical history.

[0035] The information providing unit can provide scientifically based nutrition advice based on the latest nutrition research. For example, the information providing unit refers to the latest nutrition research and provides nutrition advice based on scientific evidence. For example, the information providing unit suggests a method for taking specific nutrients based on new research results. The information providing unit can also provide reliable nutrition advice to the user based on expert opinions. For example, the information providing unit provides advice that incorporates the opinions of nutritionists and doctors. This makes it possible to refer to the latest nutrition research and provide advice based on scientific evidence.

[0036] The information providing unit can use visuals and infographics to provide easy-to-understand explanations when solving nutrition-related questions. For example, the information providing unit can use visuals and infographics to provide easy-to-understand explanations when solving nutrition-related questions. For example, the information providing unit can show vitamin and mineral intake amounts in graphs. The information providing unit can also use illustrations to explain the roles and effects of nutrients. For example, the effects of vitamin C can be shown in illustrations to provide an easy-to-understand explanation to the user. This makes it possible to use visuals and infographics to provide easy-to-understand explanations for nutrition-related questions.

[0037] The information providing unit can provide nutritional advice tailored to the user's lifestyle and eating habits. For example, the information providing unit provides individually customized nutritional advice taking into account the user's lifestyle and eating habits. For example, it can provide nutritional information for vegetarians and vegans. The information providing unit can also provide appropriate nutritional advice based on the user's meal frequency and time. For example, for a user who works the night shift, it can propose a meal plan suitable for nighttime. This makes it possible to provide nutritional advice tailored to the user's lifestyle and eating habits.

[0038] The information providing unit records the user's allergy information and dietary restrictions in detail and is able to suggest safe ingredients and recipes based on that information. The information providing unit, for example, records the user's allergy information in detail and is able to suggest safe ingredients and recipes based on that information. For example, a user with a nut allergy is provided with recipes that do not contain nuts. The information providing unit can also take the user's dietary restrictions into consideration and suggest recipes that exclude specific ingredients. For example, a gluten-free user is provided with recipes that do not contain wheat. This makes it possible to record the user's allergy information and dietary restrictions in detail and suggest safe ingredients and recipes based on that information.

[0039] The information providing unit can suggest alternative ingredients and cooking methods to accommodate the user's dietary restrictions. The information providing unit, for example, suggests alternative ingredients to accommodate the user's dietary restrictions. For example, a vegetarian user is provided with recipes that use tofu or tempeh instead of meat. The information providing unit can also suggest cooking methods that do not use specific ingredients. For example, a user who is allergic to dairy products is provided with a recipe for a dessert that does not use dairy products. In this way, alternative ingredients and cooking methods can be suggested to accommodate the user's dietary restrictions.

[0040] The information providing unit can share success stories and experiences of other users to accommodate the user's dietary restrictions. The information providing unit, for example, shares success stories of other users to accommodate the user's dietary restrictions. For example, it can introduce the experiences of users who have successfully lived a gluten-free lifestyle. The information providing unit can also provide a platform for users to share their experiences. For example, it can provide a place where users can exchange information with each other through online forums or social networking groups. This allows the user's dietary restrictions to be accommodated by sharing success stories and experiences of other users.

[0041] The information providing unit can provide a support community for accommodating dietary restrictions and promote interaction between users. The information providing unit can, for example, provide a support community for accommodating dietary restrictions and promote interaction between users. For example, an online forum can be set up for sharing allergy-friendly recipes. The information providing unit can also plan events where users can directly interact with each other. For example, workshops and seminars on dietary restrictions can be held. This can provide a support community for accommodating dietary restrictions and promote interaction between users.

[0042] The plan proposal unit can analyze the user's past meal history in detail and optimize the meal plan while evaluating its effectiveness. The plan proposal unit, for example, analyzes the user's past meal history in detail and evaluates its effectiveness. For example, the meal plan is optimized based on changes in weight or health status. The plan proposal unit can also adjust the meal plan according to the user's goals. For example, a meal plan high in protein is proposed to a user who wants to build muscle. This allows the user's past meal history to be analyzed in detail and the meal plan to be optimized while evaluating its effectiveness.

[0043] The plan suggestion unit can provide short-term and long-term meal plans according to the user's goals. The plan suggestion unit can provide, for example, a short-term meal plan according to the user's goals. For example, it can suggest a specific meal plan for losing weight in one month. The plan suggestion unit can also provide long-term meal plans. For example, it can suggest a meal plan for maintaining a healthy weight for one year. This makes it possible to provide short-term and long-term meal plans according to the user's goals.

[0044] When proposing a healthy meal plan, the plan proposal unit can also provide an exercise plan and advice on lifestyle improvement. For example, when proposing a healthy meal plan, the plan proposal unit also provides an exercise plan. For example, the plan proposal unit may propose an exercise menu that matches the meal plan. The plan proposal unit can also provide advice on lifestyle improvement. For example, the plan proposal unit may provide advice on improving sleep quality. In this way, when proposing a healthy meal plan, the plan proposal unit can also provide an exercise plan and advice on lifestyle improvement.

[0045] The plan proposal unit can link the user's meal plan with other health management apps to support comprehensive health management. The plan proposal unit, for example, links the user's meal plan with other health management apps to support comprehensive health management. For example, it shares data with a fitness app or a sleep management app. The plan proposal unit can also centrally manage the user's health data and perform comprehensive health management. For example, it can integrate data on diet, exercise, and sleep to comprehensively evaluate the user's health condition. This allows the user's meal plan to be linked with other health management apps to support comprehensive health management.

[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 food advice system can also include an allergy management unit that records the user's food allergy information and suggests safe recipes based on that information. For example, a user with a nut allergy can be suggested nut-free recipes. The allergy management unit can also periodically update the user's allergy information and respond to any new allergies that may arise. Furthermore, the allergy management unit can suggest safe menus when the user eats out. This allows the user to enjoy their meals with peace of mind.

[0048] The food advice system can also be equipped with a storage management unit that manages the storage status of the user's ingredients and suggests recipes that use them at the appropriate time. For example, it can track the expiration dates of ingredients in the refrigerator and suggest recipes that prioritize ingredients with an approaching expiration date. The storage management unit can also provide advice on how to store ingredients. For example, it can suggest the optimal way to store vegetables and fruits. This reduces food waste and allows for more efficient use of ingredients.

[0049] The food advice system may also include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal and reflects this in the next recipe suggestions. For example, the user can input their satisfaction level after a meal, and the next recipe suggestions will be optimized based on that data. The satisfaction evaluation unit can also collect user feedback and use it to improve the system. This allows the system to provide recipes that suit the user's preferences and increase satisfaction.

[0050] The food advice system may also include a nutritional balance assessment unit that evaluates the nutritional balance of the user's diet and suggests recipes to supplement necessary nutrients. For example, the system may analyze the user's diet history and, if there are vitamin or mineral deficiencies, suggest recipes using ingredients that contain these nutrients. The nutritional balance assessment unit may also provide advice on strengthening specific nutrients based on the user's health condition. This allows the user to achieve a balanced diet.

[0051] The food advice system may further include an environmental assessment unit that evaluates the environmental impact of the user's meals and suggests eco-friendly options. For example, it may prioritize locally produced or organic ingredients. The environmental assessment unit may also consider the production process and transportation distance of ingredients to suggest eco-friendly options. This allows the user to choose environmentally friendly meals.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The question analysis unit analyzes the user's question. For example, if the user asks, "Please tell me an easy dinner recipe," the question analysis unit analyzes the question and prepares to provide appropriate information. The question analysis unit can also accept the user's question in text or voice format. Step 2: The information provider provides appropriate information based on the question analyzed by the question analyzer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to the user's question. The generation AI can also provide recipes, ingredient purchasing information, and nutritional advice according to the user's needs. Step 3: The history analysis unit analyzes the user's past eating history. For example, it analyzes the types of meals the user has eaten in the past, the calories they have consumed, and the times they eat, to understand the user's eating patterns. The history analysis unit can also analyze the user's past purchase history and suggest recipes based on the user's preferences and budget. Step 4: The plan suggestion unit proposes a healthy meal plan based on the diet history analyzed by the history analysis unit and the set goals. For example, if the user wants to lose weight, the generation AI will propose a specific meal plan. The generation AI can also provide individually customized nutritional advice taking into account the user's health condition and medical history.

[0054] (Example 2) A food advice system according to an embodiment of the present invention allows users to ask an AI chatbot questions about recipes, ingredient purchasing information, nutritional information, etc., and the AI ​​generator provides appropriate information and advice taking into account the user's individual needs and restrictions, and suggests healthy meal plans based on the user's past eating history and set goals. This allows the food advice system to provide food advice tailored to the user's needs and help them achieve a healthy diet.

[0055] A food advice system according to an embodiment includes a question analysis unit, an information provision unit, a history analysis unit, and a plan proposal unit. The question analysis unit analyzes a user's question. For example, if a user asks, "Please tell me an easy dinner recipe," the question analysis unit analyzes the question and prepares to provide appropriate information. The question analysis unit can accept the user's question in text or audio format. The information provision unit provides appropriate information based on the question analyzed by the question analysis unit. For example, the generation AI generates an answer to the user's question using a text generation AI (e.g., LLM). The generation AI can also provide recipes, ingredient purchasing information, and nutritional advice based on the user's needs. The history analysis unit analyzes the user's past eating history. For example, the history analysis unit analyzes the user's past meal history, such as the types of meals the user has eaten, the calorie intake, and meal times, to understand the user's eating patterns. The history analysis unit can also analyze the user's past purchase history and propose recipes based on the user's preferences and budget. The plan proposal unit proposes a healthy eating plan based on the eating history analyzed by the history analysis unit and the user's set goals. For example, if a user wants to lose weight, the AI ​​generator can suggest a specific meal plan. The AI ​​generator can also provide individually customized nutritional advice taking into account the user's health condition and medical history. This allows the food advice system according to the embodiment to provide appropriate information in response to the user's questions and suggest a healthy meal plan based on the user's past eating history and goals. For example, the user can receive food advice tailored to their needs and limitations, helping them achieve a healthy diet.

[0056] The history analysis unit can analyze the user's past purchase history and suggest recipes based on the user's preferences and budget. For example, the history analysis unit analyzes the user's past purchase history to identify frequently purchased ingredients and budget ranges. For example, based on the chicken and vegetables the user frequently purchases, recipes such as stir-fried chicken and vegetables or salads can be suggested. The history analysis unit can also suggest ingredients that fit the user's budget based on the user's purchase history. For example, economical recipes can be suggested based on ingredients that the user can purchase on a low budget. In this way, the user's past purchase history can be analyzed and recipes can be suggested based on the user's preferences and budget.

[0057] The information providing unit can provide recipes using seasonal ingredients based on the season and local specialties. For example, the information providing unit refers to seasonal specialty product data and suggests recipes using seasonal ingredients. For example, it provides recipes using asparagus in spring and recipes using pumpkin in autumn. The information providing unit can also consider local specialties and suggest recipes using local ingredients. For example, it provides recipes using fresh vegetables and fruits produced in a specific region. This makes it possible to provide recipes using seasonal ingredients taking into consideration the season and local specialties.

[0058] The information providing unit can use the emotion estimation function to suggest recipes that match the user's mood. For example, the information providing unit analyzes the user's emotion at the time of input and suggests recipes that use ingredients that have a relaxing effect. For example, a user who is feeling stressed can be offered a recipe that uses chamomile tea or herbs. The information providing unit can also suggest recipes that use energizing ingredients depending on the user's mood. For example, a user who is tired can be offered a recipe that uses bananas or nuts that can replenish energy. In this way, the emotion estimation function can be used to suggest recipes that match the user's mood.

[0059] The information providing unit can provide recipes for beginners or time-saving recipes based on the difficulty level and cooking time of the dish. For example, the information providing unit considers the user's cooking skill level and suggests easy recipes for beginners. For example, it provides easy recipes such as sandwiches and salads to a user who is cooking for the first time. The information providing unit can also consider cooking time and suggest time-saving recipes. For example, it provides a busy user with recipes for pasta or stir-fry that can be made in 10 minutes or less. In this way, it is possible to provide recipes for beginners or time-saving recipes taking into consideration the difficulty level and cooking time of the dish.

[0060] The information providing unit can prioritize suggesting eco-friendly options when providing food purchasing information. For example, the information providing unit provides information on locally produced food ingredients in order to prioritize suggesting eco-friendly options. For example, fresh vegetables and fruits produced in the user's area are suggested. The information providing unit can also prioritize suggesting organic food ingredients. For example, organically certified food ingredients are suggested. This allows eco-friendly options to be prioritized.

[0061] The information providing unit can use the emotion estimation function to make suggestions to help the user enjoy cooking. The information providing unit, for example, uses the emotion estimation function to make suggestions to help the user enjoy cooking. For example, it can suggest recipes that are fun to make together with family and friends. The information providing unit can also provide advice to bring out the joy of cooking in accordance with the user's emotions. For example, it can suggest a way to enjoy cooking while listening to relaxing music to a user who wants to relax. In this way, it is possible to use the emotion estimation function to make suggestions to help the user enjoy cooking.

[0062] The information providing unit can provide individually customized nutritional advice based on the user's health condition and medical history. The information providing unit provides individually customized nutritional advice based on, for example, the user's health condition and medical history. For example, the information providing unit can suggest low-carbohydrate ingredients and recipes to a user with diabetes. The information providing unit can also provide advice on supplementing specific nutrients depending on the user's health condition. For example, the information providing unit can suggest ingredients and recipes that are high in iron to a user with an iron deficiency. This makes it possible to provide individually customized nutritional advice that takes into account the user's health condition and medical history.

[0063] The information providing unit can provide scientifically based nutrition advice based on the latest nutrition research. For example, the information providing unit refers to the latest nutrition research and provides nutrition advice based on scientific evidence. For example, the information providing unit suggests a method for taking specific nutrients based on new research results. The information providing unit can also provide reliable nutrition advice to the user based on expert opinions. For example, the information providing unit provides advice that incorporates the opinions of nutritionists and doctors. This makes it possible to refer to the latest nutrition research and provide advice based on scientific evidence.

[0064] The information providing unit can use the emotion estimation function to provide nutrition advice to alleviate the user's health anxieties and doubts. The information providing unit, for example, uses the emotion estimation function to provide advice to alleviate the user's health anxieties. For example, the information providing unit can provide advice to maintain motivation to a user who is on a diet. The information providing unit can also provide easy-to-understand explanations to the user's questions. For example, the information providing unit can use visuals and infographics to explain the roles of nutrients and how to consume them. In this way, the emotion estimation function can be used to provide advice to alleviate the user's health anxieties and doubts.

[0065] The information providing unit can use visuals and infographics to provide easy-to-understand explanations when solving nutrition-related questions. For example, the information providing unit can use visuals and infographics to provide easy-to-understand explanations when solving nutrition-related questions. For example, the information providing unit can show vitamin and mineral intake amounts in graphs. The information providing unit can also use illustrations to explain the roles and effects of nutrients. For example, the effects of vitamin C can be shown in illustrations to provide an easy-to-understand explanation to the user. This makes it possible to use visuals and infographics to provide easy-to-understand explanations for nutrition-related questions.

[0066] The information providing unit can provide nutritional advice tailored to the user's lifestyle and eating habits. For example, the information providing unit provides individually customized nutritional advice taking into account the user's lifestyle and eating habits. For example, it can provide nutritional information for vegetarians and vegans. The information providing unit can also provide appropriate nutritional advice based on the user's meal frequency and time. For example, for a user who works the night shift, it can propose a meal plan suitable for nighttime. This makes it possible to provide nutritional advice tailored to the user's lifestyle and eating habits.

[0067] The information providing unit can use the emotion estimation function to take an approach to make it easier for the user to accept nutritional information. The information providing unit can, for example, use the emotion estimation function to take an approach to make it easier for the user to accept nutritional information. For example, it can make extensive use of positive feedback. The information providing unit can also provide easy-to-understand explanations and interesting presentations according to the user's emotions. For example, it can select a topic that is likely to interest the user and explain it using visually appealing infographics. In this way, the emotion estimation function can be used to take an approach to make it easier for the user to accept nutritional information.

[0068] The information providing unit records the user's allergy information and dietary restrictions in detail and is able to suggest safe ingredients and recipes based on that information. The information providing unit, for example, records the user's allergy information in detail and is able to suggest safe ingredients and recipes based on that information. For example, a user with a nut allergy is provided with recipes that do not contain nuts. The information providing unit can also take the user's dietary restrictions into consideration and suggest recipes that exclude specific ingredients. For example, a gluten-free user is provided with recipes that do not contain wheat. This makes it possible to record the user's allergy information and dietary restrictions in detail and suggest safe ingredients and recipes based on that information.

[0069] The information providing unit can suggest alternative ingredients and cooking methods to accommodate the user's dietary restrictions. The information providing unit, for example, suggests alternative ingredients to accommodate the user's dietary restrictions. For example, a vegetarian user is provided with recipes that use tofu or tempeh instead of meat. The information providing unit can also suggest cooking methods that do not use specific ingredients. For example, a user who is allergic to dairy products is provided with a recipe for a dessert that does not use dairy products. In this way, alternative ingredients and cooking methods can be suggested to accommodate the user's dietary restrictions.

[0070] The information providing unit can use the emotion estimation function to provide advice to reduce stress caused by dietary restrictions to the user. The information providing unit can, for example, use the emotion estimation function to provide advice to reduce stress caused by dietary restrictions to the user. For example, delicious recipes that do not use restricted ingredients can be suggested. The information providing unit can also suggest ideas for enjoying dietary restrictions in accordance with the user's emotions. For example, ideas for meals that can be enjoyed while adhering to dietary restrictions can be suggested. In this way, the emotion estimation function can be used to provide advice to reduce stress caused by dietary restrictions to the user.

[0071] The information providing unit can share success stories and experiences of other users to accommodate the user's dietary restrictions. The information providing unit, for example, shares success stories of other users to accommodate the user's dietary restrictions. For example, it can introduce the experiences of users who have successfully lived a gluten-free lifestyle. The information providing unit can also provide a platform for users to share their experiences. For example, it can provide a place where users can exchange information with each other through online forums or social networking groups. This allows the user's dietary restrictions to be accommodated by sharing success stories and experiences of other users.

[0072] The information providing unit can provide a support community for accommodating dietary restrictions and promote interaction between users. The information providing unit can, for example, provide a support community for accommodating dietary restrictions and promote interaction between users. For example, an online forum can be set up for sharing allergy-friendly recipes. The information providing unit can also plan events where users can directly interact with each other. For example, workshops and seminars on dietary restrictions can be held. This can provide a support community for accommodating dietary restrictions and promote interaction between users.

[0073] The information providing unit can use the emotion estimation function to make suggestions to help the user enjoy their dietary restrictions. The information providing unit, for example, uses the emotion estimation function to make suggestions to help the user enjoy their dietary restrictions. For example, it can suggest party recipes that do not use restricted ingredients. The information providing unit can also suggest ideas for enjoying their dietary restrictions in accordance with the user's emotions. For example, it can provide ideas for meals that can be enjoyed while adhering to the dietary restrictions. In this way, the emotion estimation function can be used to make suggestions to help the user enjoy their dietary restrictions.

[0074] The plan proposal unit can analyze the user's past meal history in detail and optimize the meal plan while evaluating its effectiveness. The plan proposal unit, for example, analyzes the user's past meal history in detail and evaluates its effectiveness. For example, the meal plan is optimized based on changes in weight or health status. The plan proposal unit can also adjust the meal plan according to the user's goals. For example, a meal plan high in protein is proposed to a user who wants to build muscle. This allows the user's past meal history to be analyzed in detail and the meal plan to be optimized while evaluating its effectiveness.

[0075] The plan suggestion unit can provide short-term and long-term meal plans according to the user's goals. The plan suggestion unit can provide, for example, a short-term meal plan according to the user's goals. For example, it can suggest a specific meal plan for losing weight in one month. The plan suggestion unit can also provide long-term meal plans. For example, it can suggest a meal plan for maintaining a healthy weight for one year. This makes it possible to provide short-term and long-term meal plans according to the user's goals.

[0076] The plan proposal unit can use the emotion estimation function to propose a meal plan to maintain the user's motivation. The plan proposal unit, for example, uses the emotion estimation function to propose a meal plan to maintain the user's motivation. For example, it can propose a meal that will be a reward when a goal is achieved. The plan proposal unit can also provide an encouraging message depending on the user's emotion. For example, it can provide positive feedback to a user who is on a diet. In this way, it is possible to use the emotion estimation function to propose a meal plan to maintain the user's motivation.

[0077] When proposing a healthy meal plan, the plan proposal unit can also provide an exercise plan and advice on lifestyle improvement. For example, when proposing a healthy meal plan, the plan proposal unit also provides an exercise plan. For example, the plan proposal unit may propose an exercise menu that matches the meal plan. The plan proposal unit can also provide advice on lifestyle improvement. For example, the plan proposal unit may provide advice on improving sleep quality. In this way, when proposing a healthy meal plan, the plan proposal unit can also provide an exercise plan and advice on lifestyle improvement.

[0078] The plan proposal unit can link the user's meal plan with other health management apps to support comprehensive health management. The plan proposal unit, for example, links the user's meal plan with other health management apps to support comprehensive health management. For example, it shares data with a fitness app or a sleep management app. The plan proposal unit can also centrally manage the user's health data and perform comprehensive health management. For example, it can integrate data on diet, exercise, and sleep to comprehensively evaluate the user's health condition. This allows the user's meal plan to be linked with other health management apps to support comprehensive health management.

[0079] The plan proposal unit can use the emotion estimation function to make suggestions for the user to enjoy the meal plan. The plan proposal unit, for example, uses the emotion estimation function to make suggestions for the user to enjoy the meal plan. For example, it can propose a meal plan that can be enjoyed with family and friends. The plan proposal unit can also suggest ideas for enjoying the meal plan according to the user's emotions. For example, it can provide a meal plan tailored to a special event or anniversary. In this way, it is possible to use the emotion estimation function to make suggestions for the user to enjoy the meal plan.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The food advice system can also include an allergy management unit that records the user's food allergy information and suggests safe recipes based on that information. For example, a user with a nut allergy can be suggested nut-free recipes. The allergy management unit can also periodically update the user's allergy information and respond to any new allergies that may arise. Furthermore, the allergy management unit can suggest safe menus when the user eats out. This allows the user to enjoy their meals with peace of mind.

[0082] The food advice system can also be equipped with a storage management unit that manages the storage status of the user's ingredients and suggests recipes that use them at the appropriate time. For example, it can track the expiration dates of ingredients in the refrigerator and suggest recipes that prioritize ingredients with an approaching expiration date. The storage management unit can also provide advice on how to store ingredients. For example, it can suggest the optimal way to store vegetables and fruits. This reduces food waste and allows for more efficient use of ingredients.

[0083] The food advice system may also include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal and reflects this in the next recipe suggestions. For example, the user can input their satisfaction level after a meal, and the next recipe suggestions will be optimized based on that data. The satisfaction evaluation unit can also collect user feedback and use it to improve the system. This allows the system to provide recipes that suit the user's preferences and increase satisfaction.

[0084] The food advice system may also include a nutritional balance assessment unit that evaluates the nutritional balance of the user's diet and suggests recipes to supplement necessary nutrients. For example, the system may analyze the user's diet history and, if there are vitamin or mineral deficiencies, suggest recipes using ingredients that contain these nutrients. The nutritional balance assessment unit may also provide advice on strengthening specific nutrients based on the user's health condition. This allows the user to achieve a balanced diet.

[0085] The food advice system may further include an environmental assessment unit that evaluates the environmental impact of the user's meals and suggests eco-friendly options. For example, it may prioritize locally produced or organic ingredients. The environmental assessment unit may also consider the production process and transportation distance of ingredients to suggest eco-friendly options. This allows the user to choose environmentally friendly meals.

[0086] The information providing unit can use the emotion estimation function to suggest recipes with a relaxing effect according to the user's stress level. For example, a user who is feeling stressed can be offered a recipe with a relaxing effect using chamomile tea or herbs. The information providing unit can also suggest relaxing cooking methods and ways to enjoy meals according to the user's emotions. This allows the user to relax through meals.

[0087] The information providing unit can use the emotion estimation function to suggest how to select ingredients that match the user's mood. For example, if a user is feeling low, it can suggest recipes using bananas and nuts that will replenish energy. The information providing unit can also suggest ingredients and cooking methods that will lift the user's mood, depending on the user's emotions. This allows the user to improve their mood through meals.

[0088] The information providing unit can use the emotion estimation function to suggest meal timings that correspond to the user's emotions. For example, if a user is feeling stressed, the information providing unit can suggest a relaxing evening meal. The information providing unit can also provide advice on adjusting meal timings according to the user's emotions. This allows the user to enjoy meals at optimal times.

[0089] The information providing unit can use the emotion estimation function to suggest a meal atmosphere that corresponds to the user's emotion. For example, if a user wants to relax, the information providing unit can suggest a way to enjoy a meal while listening to relaxing music. The information providing unit can also provide advice on how to improve the meal atmosphere according to the user's emotion. This allows the user to enjoy their meal even more.

[0090] The information providing unit can use the emotion estimation function to suggest a meal presentation method that corresponds to the user's emotion. For example, it can suggest a meal presentation method that is suitable for a special event or anniversary. The information providing unit can also provide advice on how to improve the meal presentation according to the user's emotion. This allows the user to enjoy the meal visually as well.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The question analysis unit analyzes the user's question. For example, if the user asks, "Please tell me an easy dinner recipe," the question analysis unit analyzes the question and prepares to provide appropriate information. The question analysis unit can also accept the user's question in text or voice format. Step 2: The information provider provides appropriate information based on the question analyzed by the question analyzer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to the user's question. The generation AI can also provide recipes, ingredient purchasing information, and nutritional advice according to the user's needs. Step 3: The history analysis unit analyzes the user's past eating history. For example, it analyzes the types of meals the user has eaten in the past, the calories they have consumed, and the times they eat, to understand the user's eating patterns. The history analysis unit can also analyze the user's past purchase history and suggest recipes based on the user's preferences and budget. Step 4: The plan suggestion unit proposes a healthy meal plan based on the diet history analyzed by the history analysis unit and the set goals. For example, if the user wants to lose weight, the generation AI will propose a specific meal plan. The generation AI can also provide individually customized nutritional advice taking into account the user's health condition and medical history.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] 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).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] 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.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

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

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] 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. [Explanation of symbols]

[0160] 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 question analysis unit that analyzes a user's question; an information providing unit that provides appropriate information based on the question analyzed by the question analyzing unit; a history analysis unit that analyzes the user's past meal history; a plan suggestion unit that suggests a healthy meal plan based on the meal history analyzed by the history analysis unit and the set goal. A system characterized by:

2. The information providing unit Providing recipes using seasonal ingredients based on the season and local specialties 2. The system of claim 1.

3. The information providing unit Prioritize eco-friendly options when providing food purchasing information 2. The system of claim 1.

4. The information providing unit Providing personalized nutrition advice based on the user's health and medical history 2. The system of claim 1.

5. The information providing unit Record the user's allergy information and dietary restrictions in detail and suggest safe ingredients and recipes based on that information 2. The system of claim 1.

6. The plan proposal unit The user's past dietary history is analyzed in detail, and the effectiveness of the diet plan is evaluated and optimized.

2. The system of claim 1.

7. The information providing unit Suggest recipes that match the user's mood 2. The system of claim 1.

8. The plan proposal unit Proposing the meal plan to maintain the motivation of the user 2. The system of claim 1.

Images