System

Abstract

To provide a system for efficiently preparing a menu based on the request of a user, and for ordering necessary materials through a network.SOLUTION: A system includes a voice input part, an analysis part, a menu generation part, and a shopping support part. The voice input unit receives a voice of a user. The analysis unit analyzes the voice received by the voice input unit. The menu generation part generates a menu on the basis of the result analyzed by the analysis part. The shopping support unit orders necessary materials through the Internet based on the menu generated by the menu generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the process of creating a menu based on the user's requests and ordering the necessary ingredients online is complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently create a menu based on the user's requests and order the necessary ingredients online. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, an analysis unit, a menu generation unit, and a shopping support unit. The voice input unit receives a user's voice. The analysis unit analyzes the voice received by the voice input unit. The menu generation unit generates a menu based on the results of the analysis by the analysis unit. The shopping support unit orders the necessary ingredients online based on the menu generated by the menu generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create a menu based on the user's requests and order the necessary ingredients online. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​menu planning system according to an embodiment of the present invention is a system that automatically creates menus based on the user's voice input and also fully supports online shopping within a single app. This allows the AI ​​menu planning system to automatically create menus that reflect the user's requests and order the necessary ingredients online.

[0029] An AI menu creation system according to an embodiment includes a voice input unit, an analysis unit, a menu generation unit, and a shopping support unit. The voice input unit accepts user voice input. For example, a user can input a request such as, "I want to eat fish today, but please avoid shrimp." The analysis unit analyzes the voice received by the voice input unit. For example, it converts the voice into text using voice recognition technology to understand the user's request. The menu generation unit generates a menu based on the results of the analysis by the analysis unit. For example, the generation AI creates a menu taking into account the user's likes and dislikes, allergy information, and nutritional balance. The shopping support unit orders the necessary ingredients online based on the menu generated by the menu generation unit. For example, the generation AI lists the necessary ingredients and supports the online ordering process. As a result, the AI ​​menu creation system according to an embodiment can automatically generate a menu based on the user's voice input and order the necessary ingredients online.

[0030] The analysis unit can refer to the user's past request history and make suggestions taking into account changes and trends in preferences. The analysis unit, for example, stores the user's past request history in a database and refers to that history during voice input. For example, it prioritizes suggestions of dishes that have been frequently requested in the past. The analysis unit also analyzes the user's past request history during voice input and makes suggestions taking into account changes and trends in preferences. For example, it suggests new dishes based on recent request trends. The analysis unit also develops an algorithm that predicts changes and trends in preferences based on the user's past request history. For example, it analyzes seasonal request trends and suggests menus accordingly. This makes it possible to make suggestions that take into account changes and trends in the user's preferences.

[0031] The voice input unit can simultaneously collect the requests of all family members when voice input is made, and propose a menu that will satisfy everyone. For example, a system is developed for the voice input unit that simultaneously collects the requests of all family members when voice input is made. For example, multiple voices are analyzed and a menu that reflects everyone's requests is proposed. The voice input unit also develops an algorithm that simultaneously collects the requests of all family members and proposes a menu that will satisfy everyone. For example, each member's preferences and allergy information is taken into consideration. The voice input unit also analyzes the requests of all family members in real time when voice input is made, and a system is constructed that proposes a menu that will satisfy everyone. For example, everyone's requests are reflected in a balanced manner. This makes it possible to propose a menu that takes the requests of all family members into consideration.

[0032] The analysis unit can analyze the user's dietary history and suggest menus to correct nutritional imbalances. The analysis unit, for example, stores the user's dietary history in a database and analyzes nutritional imbalances. For example, it suggests menus that supplement missing nutrients based on past dietary content. The analysis unit also analyzes dietary history and develops an algorithm to suggest menus to correct nutritional imbalances. For example, it suggests dishes that supplement vitamins or minerals if they are lacking. The analysis unit also builds a system to suggest menus to correct nutritional imbalances based on the user's dietary history. For example, it analyzes past dietary content and suggests balanced menus. This makes it possible to suggest menus to correct nutritional imbalances.

[0033] The analysis unit can suggest alternative ingredients based on the user's allergy information and create a menu that avoids allergies while maintaining nutritional balance. The analysis unit, for example, saves the user's allergy information in a database and develops a system that suggests alternative ingredients. For example, chicken is suggested instead of shrimp for a user who is allergic to shrimp. The analysis unit also develops an algorithm that suggests alternative ingredients based on the allergy information and creates a menu that maintains nutritional balance. For example, soy milk is suggested for a user who is allergic to dairy products. The analysis unit also builds a system that suggests alternative ingredients based on the user's allergy information and creates a menu that maintains nutritional balance. For example, rice flour is suggested for a user who is allergic to wheat. This makes it possible to suggest a menu that avoids allergies while maintaining nutritional balance.

[0034] The analysis unit collects information on the likes, dislikes, and allergies of all family members and can propose menus that will satisfy everyone. For example, the analysis unit stores information on the likes, dislikes, and allergies of all family members in a database and develops a system that proposes menus that will satisfy everyone. For example, it takes into account the preferences and allergies of each member. The analysis unit also collects information on the likes, dislikes, and allergies of all family members and develops an algorithm that proposes menus that will satisfy everyone. For example, it proposes a balanced menu based on the information of each member. The analysis unit also collects information on the likes, dislikes, and allergies of all family members in real time and builds a system that proposes menus that will satisfy everyone. For example, it reflects everyone's information in a balanced manner. This makes it possible to propose menus that take into account the likes, dislikes, and allergy information of all family members.

[0035] The analysis unit can customize a menu that takes nutritional balance into consideration to suit a specific health goal. The analysis unit, for example, develops a system that customizes a menu that takes nutritional balance into consideration to suit a user's health goal. For example, it suggests low-calorie dishes to a user who is on a diet. The analysis unit also develops an algorithm that suggests a menu that takes nutritional balance into consideration to suit a specific health goal. For example, it suggests high-protein dishes to a user who is aiming to increase muscle strength. The analysis unit also builds a system that customizes a menu that takes nutritional balance into consideration in accordance with a user's health goal. For example, it suggests low-carbohydrate dishes to prevent diabetes. This makes it possible to suggest a nutritionally balanced menu that suits a specific health goal.

[0036] The analysis unit can consider the expiration dates of ingredients in the refrigerator and suggest menus that reduce waste. For example, the analysis unit stores the expiration dates of ingredients in the refrigerator in a database and suggests menus that prioritize ingredients with upcoming expiration dates. For example, it suggests dishes that use vegetables that are approaching their expiration dates. The analysis unit also develops an algorithm that considers the expiration dates of ingredients and suggests menus that reduce waste. For example, it suggests dishes that combine ingredients with upcoming expiration dates. The analysis unit also builds a system that manages the expiration dates of ingredients in the refrigerator in real time and suggests menus that reduce waste. For example, it automatically lists ingredients with upcoming expiration dates. This makes it possible to suggest menus that do not waste, taking the expiration dates of ingredients in the refrigerator into consideration.

[0037] The analysis unit can analyze the frequency of use of cooking utensils, select the most suitable cooking utensils, and create a menu. The analysis unit, for example, stores the frequency of use of cooking utensils in a database and develops a system that selects the most suitable cooking utensils. For example, it prioritizes suggestions for frequently used frying pans. The analysis unit also develops an algorithm that analyzes the frequency of use of cooking utensils, selects the most suitable cooking utensils, and creates a menu. For example, it suggests dishes based on frequently used cooking utensils. The analysis unit also builds a system that analyzes the frequency of use of cooking utensils in real time, selects the most suitable cooking utensils, and creates a menu. For example, it suggests dishes that make use of less frequently used cooking utensils. This makes it possible to suggest the most suitable menu taking into account the frequency of use of cooking utensils.

[0038] The analysis unit can add a function to automatically scan ingredients in the refrigerator and manage inventory in real time. The analysis unit, for example, develops a system to automatically scan ingredients in the refrigerator and manage inventory in real time. For example, it automatically obtains ingredient information using a barcode scanner. The analysis unit also builds a system for managing ingredient inventory in real time. For example, it automatically updates ingredient information using a camera in the refrigerator. The analysis unit also develops an algorithm to automatically scan ingredients in the refrigerator and manage inventory. For example, it obtains ingredient information in real time using RFID tags. This makes it possible to automatically scan ingredients in the refrigerator and manage inventory in real time.

[0039] The analysis unit can suggest new recipes that make use of cooking utensils that the user does not normally use, based on the cooking utensil usage history. The analysis unit, for example, develops a system that stores the cooking utensil usage history in a database and suggests recipes that make use of cooking utensils that the user does not normally use. For example, it suggests dishes that use an oven that is used infrequently. The analysis unit also analyzes the cooking utensil usage history and develops an algorithm that suggests new recipes that make use of cooking utensils that the user does not normally use. For example, it suggests new dishes based on cooking utensils that are used infrequently. The analysis unit also builds a system that suggests new recipes that make use of cooking utensils that the user does not normally use, based on the cooking utensil usage history. For example, it suggests dishes that use cooking utensils that the user does not normally use. This makes it possible to suggest new recipes that make use of cooking utensils that the user does not normally use.

[0040] The analysis unit can analyze ratings and comments on recipe sites and suggest highly rated recipes that suit the user's preferences. The analysis unit, for example, stores ratings and comments on recipe sites in a database and develops a system that suggests highly rated recipes that suit the user's preferences. For example, it prioritizes suggesting highly rated recipes. The analysis unit also analyzes ratings and comments on recipe sites and develops an algorithm that suggests highly rated recipes that suit the user's preferences. For example, it estimates the user's preferences based on the content of the comments. The analysis unit also builds a system that suggests highly rated recipes that suit the user's preferences based on ratings and comments on recipe sites. For example, it automatically lists highly rated recipes. This makes it possible to suggest highly rated recipes that suit the user's preferences.

[0041] The analysis unit can collect trend information from recipe sites in real time and suggest the latest popular recipes. The analysis unit, for example, collects trend information from recipe sites in real time and develops a system that suggests the latest popular recipes. For example, it suggests new dishes based on the trend information. The analysis unit also analyzes trend information from recipe sites and develops an algorithm that suggests the latest popular recipes. For example, it suggests dishes that suit the user's preferences based on the trend information. The analysis unit also collects trend information from recipe sites in real time and builds a system that suggests the latest popular recipes. For example, it automatically updates trend information. This makes it possible to suggest the latest popular recipes in real time.

[0042] The analysis unit can automatically generate recipes that match the ingredients and cooking utensils that a user has, based on information from a recipe site. The analysis unit, for example, develops a system that automatically generates recipes that match the ingredients and cooking utensils that a user has, based on information from a recipe site. For example, it suggests recipes based on the contents of a refrigerator. The analysis unit also analyzes information from recipe sites and develops an algorithm that automatically generates recipes that match the ingredients and cooking utensils that a user has. For example, it suggests dishes based on available cooking utensils. The analysis unit also builds a system that automatically generates recipes that match the ingredients and cooking utensils that a user has, based on information from a recipe site. For example, it analyzes the contents of a refrigerator in real time. This makes it possible to automatically generate recipes that match the ingredients and cooking utensils that a user has.

[0043] The analysis unit suggests dishes from different cultures and regions based on information from recipe sites, allowing users to enjoy a diversity of food. The analysis unit, for example, develops a system that suggests dishes from different cultures and regions based on information from recipe sites. For example, it suggests Italian or Chinese cuisine. The analysis unit also analyzes information from recipe sites and develops an algorithm that suggests dishes from different cultures and regions. For example, it suggests dishes from different cultures based on the user's preferences. The analysis unit also builds a system that suggests dishes from different cultures and regions based on information from recipe sites. For example, it suggests dishes from different cultures based on the user's requests. This allows users to suggest dishes from different cultures and regions, allowing them to enjoy a diversity of food.

[0044] The analysis unit takes into account the shelf life of the bento and can suggest a menu that will remain delicious even when stored for a long time. The analysis unit, for example, develops a system that takes into account the shelf life of the bento and suggests a menu that will remain delicious even when stored for a long time. For example, it suggests dishes that use ingredients that have a long shelf life. The analysis unit also develops an algorithm that analyzes the shelf life of the bento and suggests a menu that will remain delicious even when stored for a long time. For example, it suggests dishes based on cooking methods that have a long shelf life. The analysis unit also builds a system that takes into account the shelf life of the bento and suggests a menu that will remain delicious even when stored for a long time. For example, it automatically lists ingredients and cooking methods that have a long shelf life. This makes it possible to suggest bento menus that will remain delicious even when stored for a long time.

[0045] The analysis unit is able to emphasize the appearance of the bento and suggest menus that take color and balance into consideration. The analysis unit, for example, develops a system that emphasizes the appearance of the bento and suggests menus that take color and balance into consideration. For example, it suggests dishes using colorful ingredients. The analysis unit also develops an algorithm that analyzes the appearance of the bento and suggests menus that take color and balance into consideration. For example, it suggests dishes based on how beautiful the food looks. The analysis unit also builds a system that emphasizes the appearance of the bento and suggests menus that take color and balance into consideration. For example, it automatically lists colorful ingredients and cooking methods. This makes it possible to suggest menus that emphasize the appearance of the bento and consider color and balance into consideration.

[0046] The analysis unit can propose special menus that match the season or event based on the request for the bento. The analysis unit, for example, develops a system that proposes special menus that match the season or event based on the request for the bento. For example, it proposes dishes using seasonal ingredients. The analysis unit also develops an algorithm that analyzes the request for the bento and proposes special menus that match the season or event. For example, it proposes special dishes that match the event. The analysis unit also builds a system that proposes special menus that match the season or event based on the request for the bento. For example, it automatically lists dishes that match seasonal ingredients or events. This makes it possible to propose special bento menus that match the season or event.

[0047] The analysis unit can add a function to simultaneously create different bento lunches for each family member based on the lunch request. The analysis unit, for example, develops a system that simultaneously creates different bento lunches for each family member based on the lunch request. For example, it takes into account each member's preferences and allergy information. The analysis unit also analyzes the lunch request and develops an algorithm that simultaneously creates different bento lunches for each family member. For example, it suggests a balanced bento lunch based on each member's information. The analysis unit also builds a system that simultaneously creates different bento lunches for each family member based on the lunch request. For example, it reflects everyone's requests in a balanced manner. This makes it possible to simultaneously create different bento lunches for each family member.

[0048] The shopping support unit can refer to the user's past purchase history when shopping online and make suggestions to encourage repeat purchases. The shopping support unit, for example, develops a system that stores the user's past purchase history in a database and makes suggestions to encourage repeat purchases. For example, it prioritizes suggestions for products that have been purchased in the past. The shopping support unit also develops an algorithm that analyzes the user's past purchase history when shopping online and makes suggestions to encourage repeat purchases. For example, it suggests products based on repeat purchase trends. The shopping support unit also builds a system that makes suggestions to encourage repeat purchases based on the user's past purchase history. For example, it automatically lists products that have been purchased in the past. This makes it possible to make suggestions to encourage repeat purchases based on the user's past purchase history.

[0049] The shopping support unit can automatically apply sale information and coupons when shopping online and make suggestions to reduce costs. The shopping support unit, for example, develops a system that automatically applies sale information and coupons when shopping online. For example, it suggests products based on sale information. The shopping support unit also develops an algorithm that automatically applies sale information and coupons and makes suggestions to reduce costs. For example, it analyzes coupon application conditions and suggests optimal products. The shopping support unit also builds a system that automatically applies sale information and coupons when shopping online and makes suggestions to reduce costs. For example, it automatically lists sale information and coupons. This makes it possible to automatically apply sale information and coupons and make suggestions to reduce costs.

[0050] The shopping support department can provide options to purchase directly from local farmers and producers when shopping online. For example, the shopping support department develops a system that provides options to purchase directly from local farmers and producers when shopping online. For example, it prioritizes the suggestions of products from local farmers. The shopping support department also develops an algorithm that provides options to purchase directly from local farmers and producers. For example, it suggests products based on information about local producers. The shopping support department also builds a system that provides options to purchase directly from local farmers and producers when shopping online. For example, it automatically lists products from local farmers. This makes it possible to provide options to purchase directly from local farmers and producers.

[0051] The shopping support unit can prioritize suggesting eco-friendly products when shopping online, thereby promoting environmentally conscious shopping. For example, the shopping support unit develops a system that prioritizes suggesting eco-friendly products when shopping online. For example, it prioritizes suggesting environmentally conscious products. The shopping support unit also develops an algorithm that prioritizes suggesting eco-friendly products and promoting environmentally conscious shopping. For example, it suggests products based on information about environmentally friendly products. The shopping support unit also builds a system that prioritizes suggesting eco-friendly products when shopping online, thereby promoting environmentally conscious shopping. For example, it automatically lists eco-friendly products. This makes it possible to prioritize suggesting eco-friendly products and promote environmentally conscious shopping.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can suggest menus to enhance specific nutrients based on the user's dietary history. For example, if the user is deficient in vitamin D, it will suggest menus using ingredients that are rich in vitamin D. The analysis unit also analyzes the user's dietary history and develops algorithms to enhance specific nutrients. For example, if a user is deficient in calcium, it will suggest dishes that are rich in calcium. The analysis unit also builds a system to suggest menus to enhance specific nutrients based on the user's dietary history. For example, if a user is deficient in iron, it will suggest dishes that use ingredients that are rich in iron. This makes it possible to suggest menus to improve the user's nutritional balance.

[0054] The analysis unit can suggest menus that correspond to specific health conditions based on the user's dietary history. For example, if the user has high blood pressure, it will suggest menus that are low in salt. The analysis unit also analyzes the user's health condition and develops algorithms that correspond to specific health conditions. For example, it will suggest low-carbohydrate dishes to a user with diabetes. The analysis unit also builds a system that suggests menus that correspond to specific health conditions based on the user's health condition. For example, it will suggest low-cholesterol dishes to a user with high cholesterol levels. This makes it possible to suggest menus that correspond to the user's health condition.

[0055] The analysis unit can suggest menus that correspond to a specific diet plan based on the user's diet history. For example, if the user is on a ketogenic diet, it will suggest low-carbohydrate menus. The analysis unit also analyzes the user's diet plan and develops an algorithm that corresponds to the specific diet plan. For example, for a user on a vegan diet, it will suggest dishes that do not contain animal products. The analysis unit also builds a system that suggests menus that correspond to a specific diet plan based on the user's diet plan. For example, for a user on a paleo diet, it will suggest dishes that avoid processed foods. This makes it possible to suggest menus that correspond to the user's diet plan.

[0056] The analysis unit can suggest menus that accommodate specific allergies based on the user's dietary history. For example, if the user is allergic to nuts, it will suggest menus that do not include nuts. The analysis unit also analyzes the user's allergy information and develops algorithms that accommodate specific allergies. For example, it will suggest gluten-free dishes to a user who is allergic to gluten. The analysis unit also builds a system that suggests menus that accommodate specific allergies based on the user's allergy information. For example, it will suggest dishes that do not include dairy products to a user who is allergic to dairy products. This makes it possible to suggest menus that suit the user's allergies.

[0057] The analysis unit can suggest menus that correspond to specific cultures and religions based on the user's dietary history. For example, if the user requires halal food, it will suggest menus that use halal-certified ingredients. The analysis unit also analyzes the user's culture and religion and develops algorithms that correspond to specific cultures and religions. For example, if a user requires kosher food, it will suggest kosher-certified dishes. The analysis unit also builds a system that suggests menus that correspond to specific cultures and religions based on the user's culture and religion. For example, it will suggest dishes that do not include meat to a vegetarian user. This makes it possible to suggest menus that correspond to the user's culture and religion.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The voice input unit receives the user's voice. For example, the user can input a request by voice such as, "I want to eat fish today, but please avoid shrimp." Step 2: The analysis unit analyzes the voice received by the voice input unit. For example, it converts the voice into text using voice recognition technology to understand the user's request. Step 3: The menu generation unit generates a menu based on the results of the analysis by the analysis unit. For example, the generation AI creates a menu taking into account the user's likes and dislikes, allergy information, and nutritional balance. Step 4: The shopping support unit orders the necessary ingredients online based on the menu generated by the menu generation unit. For example, the generation AI lists the necessary ingredients and supports the online ordering process.

[0060] (Example 2) The AI ​​menu planning system according to an embodiment of the present invention is a system that automatically creates menus based on the user's voice input and also fully supports online shopping within a single app. This allows the AI ​​menu planning system to automatically create menus that reflect the user's requests and order the necessary ingredients online.

[0061] An AI menu creation system according to an embodiment includes a voice input unit, an analysis unit, a menu generation unit, and a shopping support unit. The voice input unit accepts user voice input. For example, a user can input a request such as, "I want to eat fish today, but please avoid shrimp." The analysis unit analyzes the voice received by the voice input unit. For example, it converts the voice into text using voice recognition technology to understand the user's request. The menu generation unit generates a menu based on the results of the analysis by the analysis unit. For example, the generation AI creates a menu taking into account the user's likes and dislikes, allergy information, and nutritional balance. The shopping support unit orders the necessary ingredients online based on the menu generated by the menu generation unit. For example, the generation AI lists the necessary ingredients and supports the online ordering process. As a result, the AI ​​menu creation system according to an embodiment can automatically generate a menu based on the user's voice input and order the necessary ingredients online.

[0062] The voice input unit analyzes the tone and speed of the user's voice to estimate stress and fatigue levels and reflect them in the menu. For example, when a user inputs a request by voice, the voice input unit analyzes the tone and speed of the voice to estimate stress and fatigue levels. For example, if a user says "I'd like a simple meal" in a tired voice, the generation AI will suggest a menu that doesn't require much effort. The voice input unit also analyzes the tone and speed of the user's voice in real time during voice input to quantify stress and fatigue levels. For example, if the stress level is high, it will suggest relaxing dishes. The voice input unit also analyzes the tone and speed of the user's voice to develop an algorithm that estimates stress and fatigue levels. For example, it evaluates stress levels based on changes in the pitch and speed of the voice and suggests menus accordingly. This makes it possible to suggest menus that take the user's stress and fatigue levels into account.

[0063] The analysis unit can refer to the user's past request history and make suggestions taking into account changes and trends in preferences. The analysis unit, for example, stores the user's past request history in a database and refers to that history during voice input. For example, it prioritizes suggestions of dishes that have been frequently requested in the past. The analysis unit also analyzes the user's past request history during voice input and makes suggestions taking into account changes and trends in preferences. For example, it suggests new dishes based on recent request trends. The analysis unit also develops an algorithm that predicts changes and trends in preferences based on the user's past request history. For example, it analyzes seasonal request trends and suggests menus accordingly. This makes it possible to make suggestions that take into account changes and trends in the user's preferences.

[0064] The analysis unit can analyze the user's emotional state in real time and suggest a menu that matches their mood. The analysis unit is equipped with an emotion estimation function that analyzes the user's emotional state in real time during voice input, for example. For example, if the user is feeling down, it will suggest dishes that will lift their spirits. The analysis unit also uses the emotion estimation function to analyze the user's emotional state and suggest a menu based on the results. For example, if the user is happy, it will suggest a special dish. The analysis unit also develops an algorithm that analyzes the user's emotional state in real time and suggests a menu that matches their mood. For example, it selects the optimal dish based on the emotion score. This makes it possible to suggest a menu that matches the user's emotional state.

[0065] The voice input unit can combine gesture and facial expression recognition in addition to voice input to collect requests more intuitively. For example, a system is developed in which the voice input unit combines gesture and facial expression recognition in addition to voice input. For example, if a user says with a smile, "I want to eat delicious food," a special dish is suggested. The voice input unit also uses gesture and facial expression recognition to collect user requests more intuitively. For example, if a user waves their hand and says, "I want something simple," a dish that doesn't require much effort is suggested. The voice input unit also combines voice input with gesture and facial expression recognition to build a system that understands user requests more accurately. For example, it analyzes changes in facial expression and suggests a menu that matches the emotion. This makes it possible to collect requests intuitively, taking into account the user's gestures and facial expressions.

[0066] The voice input unit can simultaneously collect the requests of all family members when voice input is made, and propose a menu that will satisfy everyone. For example, a system is developed for the voice input unit that simultaneously collects the requests of all family members when voice input is made. For example, multiple voices are analyzed and a menu that reflects everyone's requests is proposed. The voice input unit also develops an algorithm that simultaneously collects the requests of all family members and proposes a menu that will satisfy everyone. For example, each member's preferences and allergy information is taken into consideration. The voice input unit also analyzes the requests of all family members in real time when voice input is made, and a system is constructed that proposes a menu that will satisfy everyone. For example, everyone's requests are reflected in a balanced manner. This makes it possible to propose a menu that takes the requests of all family members into consideration.

[0067] The analysis unit can analyze the user's dietary history and suggest menus to correct nutritional imbalances. The analysis unit, for example, stores the user's dietary history in a database and analyzes nutritional imbalances. For example, it suggests menus that supplement missing nutrients based on past dietary content. The analysis unit also analyzes dietary history and develops an algorithm to suggest menus to correct nutritional imbalances. For example, it suggests dishes that supplement vitamins or minerals if they are lacking. The analysis unit also builds a system to suggest menus to correct nutritional imbalances based on the user's dietary history. For example, it analyzes past dietary content and suggests balanced menus. This makes it possible to suggest menus to correct nutritional imbalances.

[0068] The analysis unit can suggest alternative ingredients based on the user's allergy information and create a menu that avoids allergies while maintaining nutritional balance. The analysis unit, for example, saves the user's allergy information in a database and develops a system that suggests alternative ingredients. For example, chicken is suggested instead of shrimp for a user who is allergic to shrimp. The analysis unit also develops an algorithm that suggests alternative ingredients based on the allergy information and creates a menu that maintains nutritional balance. For example, soy milk is suggested for a user who is allergic to dairy products. The analysis unit also builds a system that suggests alternative ingredients based on the user's allergy information and creates a menu that maintains nutritional balance. For example, rice flour is suggested for a user who is allergic to wheat. This makes it possible to suggest a menu that avoids allergies while maintaining nutritional balance.

[0069] The analysis unit can use the emotion estimation function to suggest a menu that takes into account nutritional balance according to the user's emotional state. For example, the analysis unit uses the emotion estimation function to analyze the user's emotional state and suggest a menu that takes into account nutritional balance based on the results. For example, when stress is high, ingredients with a relaxing effect are used. The analysis unit also analyzes the user's emotional state in real time and develops an algorithm that suggests a menu that takes into account nutritional balance. For example, it provides optimal nutritional balance based on the emotion score. The analysis unit also uses the emotion estimation function to build a system that suggests a menu that takes into account nutritional balance according to the user's emotional state. For example, it suggests dishes that supplement vitamins and minerals according to the emotional state. This makes it possible to suggest a menu that takes into account nutritional balance according to the user's emotional state.

[0070] The analysis unit collects information on the likes, dislikes, and allergies of all family members and can propose menus that will satisfy everyone. For example, the analysis unit stores information on the likes, dislikes, and allergies of all family members in a database and develops a system that proposes menus that will satisfy everyone. For example, it takes into account the preferences and allergies of each member. The analysis unit also collects information on the likes, dislikes, and allergies of all family members and develops an algorithm that proposes menus that will satisfy everyone. For example, it proposes a balanced menu based on the information of each member. The analysis unit also collects information on the likes, dislikes, and allergies of all family members in real time and builds a system that proposes menus that will satisfy everyone. For example, it reflects everyone's information in a balanced manner. This makes it possible to propose menus that take into account the likes, dislikes, and allergy information of all family members.

[0071] The analysis unit can customize a menu that takes nutritional balance into consideration to suit a specific health goal. The analysis unit, for example, develops a system that customizes a menu that takes nutritional balance into consideration to suit a user's health goal. For example, it suggests low-calorie dishes to a user who is on a diet. The analysis unit also develops an algorithm that suggests a menu that takes nutritional balance into consideration to suit a specific health goal. For example, it suggests high-protein dishes to a user who is aiming to increase muscle strength. The analysis unit also builds a system that customizes a menu that takes nutritional balance into consideration in accordance with a user's health goal. For example, it suggests low-carbohydrate dishes to prevent diabetes. This makes it possible to suggest a nutritionally balanced menu that suits a specific health goal.

[0072] The analysis unit can use the emotion estimation function to analyze the emotional states of all family members and suggest nutritionally balanced menus that will make everyone feel positive. The analysis unit, for example, uses the emotion estimation function to analyze the emotional states of all family members and suggest nutritionally balanced menus based on the results. For example, it can suggest dishes that will make everyone feel positive. The analysis unit can also analyze the emotional states of all family members in real time and develop an algorithm that suggests nutritionally balanced menus. For example, it can provide optimal nutritional balance based on emotion scores. The analysis unit can also use the emotion estimation function to build a system that analyzes the emotional states of all family members and suggests nutritionally balanced menus based on the results. For example, it can suggest dishes that will please everyone. This makes it possible to suggest nutritionally balanced menus that suit the emotional states of all family members.

[0073] The analysis unit can consider the expiration dates of ingredients in the refrigerator and suggest menus that reduce waste. For example, the analysis unit stores the expiration dates of ingredients in the refrigerator in a database and suggests menus that prioritize ingredients with upcoming expiration dates. For example, it suggests dishes that use vegetables that are approaching their expiration dates. The analysis unit also develops an algorithm that considers the expiration dates of ingredients and suggests menus that reduce waste. For example, it suggests dishes that combine ingredients with upcoming expiration dates. The analysis unit also builds a system that manages the expiration dates of ingredients in the refrigerator in real time and suggests menus that reduce waste. For example, it automatically lists ingredients with upcoming expiration dates. This makes it possible to suggest menus that do not waste, taking the expiration dates of ingredients in the refrigerator into consideration.

[0074] The analysis unit can analyze the frequency of use of cooking utensils, select the most suitable cooking utensils, and create a menu. The analysis unit, for example, stores the frequency of use of cooking utensils in a database and develops a system that selects the most suitable cooking utensils. For example, it prioritizes suggestions for frequently used frying pans. The analysis unit also develops an algorithm that analyzes the frequency of use of cooking utensils, selects the most suitable cooking utensils, and creates a menu. For example, it suggests dishes based on frequently used cooking utensils. The analysis unit also builds a system that analyzes the frequency of use of cooking utensils in real time, selects the most suitable cooking utensils, and creates a menu. For example, it suggests dishes that make use of less frequently used cooking utensils. This makes it possible to suggest the most suitable menu taking into account the frequency of use of cooking utensils.

[0075] The analysis unit can use the emotion estimation function to select cooking utensils according to the user's emotional state and suggest a menu that reduces stress. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state and select cooking utensils based on the results. For example, if stress is high, easy-to-use cooking utensils are suggested. The analysis unit also analyzes the user's emotional state in real time and develops an algorithm to select cooking utensils that will reduce stress. For example, the analysis unit provides optimal cooking utensils based on the emotion score. The analysis unit also uses the emotion estimation function to build a system that selects cooking utensils according to the user's emotional state and suggests a menu that reduces stress. For example, easy-to-use cooking utensils are suggested according to the emotional state. This makes it possible to select cooking utensils according to the user's emotional state and suggest a menu that reduces stress.

[0076] The analysis unit can add a function to automatically scan ingredients in the refrigerator and manage inventory in real time. The analysis unit, for example, develops a system to automatically scan ingredients in the refrigerator and manage inventory in real time. For example, it automatically obtains ingredient information using a barcode scanner. The analysis unit also builds a system for managing ingredient inventory in real time. For example, it automatically updates ingredient information using a camera in the refrigerator. The analysis unit also develops an algorithm to automatically scan ingredients in the refrigerator and manage inventory. For example, it obtains ingredient information in real time using RFID tags. This makes it possible to automatically scan ingredients in the refrigerator and manage inventory in real time.

[0077] The analysis unit can suggest new recipes that make use of cooking utensils that the user does not normally use, based on the cooking utensil usage history. The analysis unit, for example, develops a system that stores the cooking utensil usage history in a database and suggests recipes that make use of cooking utensils that the user does not normally use. For example, it suggests dishes that use an oven that is used infrequently. The analysis unit also analyzes the cooking utensil usage history and develops an algorithm that suggests new recipes that make use of cooking utensils that the user does not normally use. For example, it suggests new dishes based on cooking utensils that are used infrequently. The analysis unit also builds a system that suggests new recipes that make use of cooking utensils that the user does not normally use, based on the cooking utensil usage history. For example, it suggests dishes that use cooking utensils that the user does not normally use. This makes it possible to suggest new recipes that make use of cooking utensils that the user does not normally use.

[0078] The analysis unit can use the emotion estimation function to analyze the emotional state of all family members and suggest a menu using cooking utensils that all members have positive emotions about. For example, the analysis unit can use the emotion estimation function to analyze the emotional state of all family members and select cooking utensils based on the results. For example, it can suggest cooking utensils that all members have positive emotions about. The analysis unit can also develop an algorithm that analyzes the emotional state of all family members in real time and suggest a menu using cooking utensils that all members have positive emotions about. For example, it can provide optimal cooking utensils based on emotion scores. The analysis unit can also use the emotion estimation function to build a system that analyzes the emotional state of all family members and selects cooking utensils based on the results. For example, it can suggest cooking utensils that all members will enjoy. This makes it possible to suggest a menu using cooking utensils that match the emotional state of all family members.

[0079] The analysis unit can analyze ratings and comments on recipe sites and suggest highly rated recipes that suit the user's preferences. The analysis unit, for example, stores ratings and comments on recipe sites in a database and develops a system that suggests highly rated recipes that suit the user's preferences. For example, it prioritizes suggesting highly rated recipes. The analysis unit also analyzes ratings and comments on recipe sites and develops an algorithm that suggests highly rated recipes that suit the user's preferences. For example, it estimates the user's preferences based on the content of the comments. The analysis unit also builds a system that suggests highly rated recipes that suit the user's preferences based on ratings and comments on recipe sites. For example, it automatically lists highly rated recipes. This makes it possible to suggest highly rated recipes that suit the user's preferences.

[0080] The analysis unit can collect trend information from recipe sites in real time and suggest the latest popular recipes. The analysis unit, for example, collects trend information from recipe sites in real time and develops a system that suggests the latest popular recipes. For example, it suggests new dishes based on the trend information. The analysis unit also analyzes trend information from recipe sites and develops an algorithm that suggests the latest popular recipes. For example, it suggests dishes that suit the user's preferences based on the trend information. The analysis unit also collects trend information from recipe sites in real time and builds a system that suggests the latest popular recipes. For example, it automatically updates trend information. This makes it possible to suggest the latest popular recipes in real time.

[0081] The analysis unit can use the emotion estimation function to utilize information from recipe sites according to the user's emotional state and suggest recipes that suit the user's mood. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state and utilizes information from recipe sites based on the results. For example, if the user is feeling down, the analysis unit can suggest dishes that will lift the user's spirits. The analysis unit can also analyze the user's emotional state in real time and develop an algorithm that suggests recipes that suit the user's mood. For example, the analysis unit can provide optimal dishes based on the emotion score. The analysis unit can also use the emotion estimation function to utilize information from recipe sites according to the user's emotional state and build a system that suggests recipes that suit the user's mood. For example, the analysis unit can select recipes according to the user's emotional state. This makes it possible to suggest recipes that suit the user's emotional state.

[0082] The analysis unit can automatically generate recipes that match the ingredients and cooking utensils that a user has, based on information from a recipe site. The analysis unit, for example, develops a system that automatically generates recipes that match the ingredients and cooking utensils that a user has, based on information from a recipe site. For example, it suggests recipes based on the contents of a refrigerator. The analysis unit also analyzes information from recipe sites and develops an algorithm that automatically generates recipes that match the ingredients and cooking utensils that a user has. For example, it suggests dishes based on available cooking utensils. The analysis unit also builds a system that automatically generates recipes that match the ingredients and cooking utensils that a user has, based on information from a recipe site. For example, it analyzes the contents of a refrigerator in real time. This makes it possible to automatically generate recipes that match the ingredients and cooking utensils that a user has.

[0083] The analysis unit suggests dishes from different cultures and regions based on information from recipe sites, allowing users to enjoy a diversity of food. The analysis unit, for example, develops a system that suggests dishes from different cultures and regions based on information from recipe sites. For example, it suggests Italian or Chinese cuisine. The analysis unit also analyzes information from recipe sites and develops an algorithm that suggests dishes from different cultures and regions. For example, it suggests dishes from different cultures based on the user's preferences. The analysis unit also builds a system that suggests dishes from different cultures and regions based on information from recipe sites. For example, it suggests dishes from different cultures based on the user's requests. This allows users to suggest dishes from different cultures and regions, allowing them to enjoy a diversity of food.

[0084] The analysis unit uses the emotion estimation function to analyze the emotional state of all family members and can suggest recipes that will make everyone feel positive. For example, the analysis unit uses the emotion estimation function to analyze the emotional state of all family members and suggest recipes based on the results. For example, it suggests dishes that will make everyone feel positive. The analysis unit also develops an algorithm that analyzes the emotional state of all family members in real time and suggests recipes that will make everyone feel positive. For example, it provides optimal dishes based on the emotion score. The analysis unit also uses the emotion estimation function to build a system that analyzes the emotional state of all family members and suggests recipes based on the results. For example, it suggests dishes that will please everyone. This makes it possible to suggest recipes that suit the emotional state of all family members.

[0085] The analysis unit takes into account the shelf life of the bento and can suggest a menu that will remain delicious even when stored for a long time. The analysis unit, for example, develops a system that takes into account the shelf life of the bento and suggests a menu that will remain delicious even when stored for a long time. For example, it suggests dishes that use ingredients that have a long shelf life. The analysis unit also develops an algorithm that analyzes the shelf life of the bento and suggests a menu that will remain delicious even when stored for a long time. For example, it suggests dishes based on cooking methods that have a long shelf life. The analysis unit also builds a system that takes into account the shelf life of the bento and suggests a menu that will remain delicious even when stored for a long time. For example, it automatically lists ingredients and cooking methods that have a long shelf life. This makes it possible to suggest bento menus that will remain delicious even when stored for a long time.

[0086] The analysis unit is able to emphasize the appearance of the bento and suggest menus that take color and balance into consideration. The analysis unit, for example, develops a system that emphasizes the appearance of the bento and suggests menus that take color and balance into consideration. For example, it suggests dishes using colorful ingredients. The analysis unit also develops an algorithm that analyzes the appearance of the bento and suggests menus that take color and balance into consideration. For example, it suggests dishes based on how beautiful the food looks. The analysis unit also builds a system that emphasizes the appearance of the bento and suggests menus that take color and balance into consideration. For example, it automatically lists colorful ingredients and cooking methods. This makes it possible to suggest menus that emphasize the appearance of the bento and consider color and balance into consideration.

[0087] The analysis unit uses the emotion estimation function to suggest a lunch menu that matches the user's emotional state, thereby increasing the enjoyment of meals. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state and suggest a lunch menu based on the results. For example, if the user is feeling down, the analysis unit suggests dishes that will lift the user's spirits. The analysis unit also analyzes the user's emotional state in real time and develops an algorithm that suggests a lunch menu to increase the enjoyment of meals. For example, it provides optimal dishes based on the emotion score. The analysis unit also uses the emotion estimation function to suggest a lunch menu that matches the user's emotional state, thereby building a system that increases the enjoyment of meals. For example, it selects the contents of the lunch according to the emotional state. This makes it possible to suggest a lunch menu that matches the user's emotional state, thereby increasing the enjoyment of meals.

[0088] The analysis unit can propose special menus that match the season or event based on the request for the bento. The analysis unit, for example, develops a system that proposes special menus that match the season or event based on the request for the bento. For example, it proposes dishes using seasonal ingredients. The analysis unit also develops an algorithm that analyzes the request for the bento and proposes special menus that match the season or event. For example, it proposes special dishes that match the event. The analysis unit also builds a system that proposes special menus that match the season or event based on the request for the bento. For example, it automatically lists dishes that match seasonal ingredients or events. This makes it possible to propose special bento menus that match the season or event.

[0089] The analysis unit can add a function to simultaneously create different bento lunches for each family member based on the lunch request. The analysis unit, for example, develops a system that simultaneously creates different bento lunches for each family member based on the lunch request. For example, it takes into account each member's preferences and allergy information. The analysis unit also analyzes the lunch request and develops an algorithm that simultaneously creates different bento lunches for each family member. For example, it suggests a balanced bento lunch based on each member's information. The analysis unit also builds a system that simultaneously creates different bento lunches for each family member based on the lunch request. For example, it reflects everyone's requests in a balanced manner. This makes it possible to simultaneously create different bento lunches for each family member.

[0090] The analysis unit can use the emotion estimation function to analyze the emotional states of all family members and suggest lunch box menus that will make everyone feel positive. The analysis unit, for example, uses the emotion estimation function to analyze the emotional states of all family members and suggest lunch box menus based on the results. For example, it suggests dishes that will make everyone feel positive. The analysis unit also develops an algorithm that analyzes the emotional states of all family members in real time and suggests lunch box menus that will make everyone feel positive. For example, it provides optimal dishes based on emotion scores. The analysis unit also uses the emotion estimation function to build a system that analyzes the emotional states of all family members and suggests lunch box menus based on the results. For example, it suggests dishes that will please everyone. This makes it possible to suggest lunch box menus that match the emotional states of all family members.

[0091] The shopping support unit can refer to the user's past purchase history when shopping online and make suggestions to encourage repeat purchases. The shopping support unit, for example, develops a system that stores the user's past purchase history in a database and makes suggestions to encourage repeat purchases. For example, it prioritizes suggestions for products that have been purchased in the past. The shopping support unit also develops an algorithm that analyzes the user's past purchase history when shopping online and makes suggestions to encourage repeat purchases. For example, it suggests products based on repeat purchase trends. The shopping support unit also builds a system that makes suggestions to encourage repeat purchases based on the user's past purchase history. For example, it automatically lists products that have been purchased in the past. This makes it possible to make suggestions to encourage repeat purchases based on the user's past purchase history.

[0092] The shopping support unit can automatically apply sale information and coupons when shopping online and make suggestions to reduce costs. The shopping support unit, for example, develops a system that automatically applies sale information and coupons when shopping online. For example, it suggests products based on sale information. The shopping support unit also develops an algorithm that automatically applies sale information and coupons and makes suggestions to reduce costs. For example, it analyzes coupon application conditions and suggests optimal products. The shopping support unit also builds a system that automatically applies sale information and coupons when shopping online and makes suggestions to reduce costs. For example, it automatically lists sale information and coupons. This makes it possible to automatically apply sale information and coupons and make suggestions to reduce costs.

[0093] The shopping support unit uses the emotion estimation function to create a shopping list according to the user's emotional state, thereby reducing stress. The shopping support unit, for example, uses the emotion estimation function to analyze the user's emotional state and create a shopping list based on the results. For example, if the user is feeling stressed, the shopping support unit suggests products that are easy to purchase. The shopping support unit also analyzes the user's emotional state in real time and develops an algorithm to create a shopping list to reduce stress. For example, it provides optimal products based on the emotion score. The shopping support unit also uses the emotion estimation function to create a shopping list according to the user's emotional state and build a system to reduce stress. For example, it automatically updates the shopping list according to the emotional state. This allows the shopping support unit to create a shopping list according to the user's emotional state and reduce stress.

[0094] The shopping support department can provide options to purchase directly from local farmers and producers when shopping online. For example, the shopping support department develops a system that provides options to purchase directly from local farmers and producers when shopping online. For example, it prioritizes the suggestions of products from local farmers. The shopping support department also develops an algorithm that provides options to purchase directly from local farmers and producers. For example, it suggests products based on information about local producers. The shopping support department also builds a system that provides options to purchase directly from local farmers and producers when shopping online. For example, it automatically lists products from local farmers. This makes it possible to provide options to purchase directly from local farmers and producers.

[0095] The shopping support unit can prioritize suggesting eco-friendly products when shopping online, thereby promoting environmentally conscious shopping. For example, the shopping support unit develops a system that prioritizes suggesting eco-friendly products when shopping online. For example, it prioritizes suggesting environmentally conscious products. The shopping support unit also develops an algorithm that prioritizes suggesting eco-friendly products and promoting environmentally conscious shopping. For example, it suggests products based on information about environmentally friendly products. The shopping support unit also builds a system that prioritizes suggesting eco-friendly products when shopping online, thereby promoting environmentally conscious shopping. For example, it automatically lists eco-friendly products. This makes it possible to prioritize suggesting eco-friendly products and promote environmentally conscious shopping.

[0096] The shopping support unit can use the emotion estimation function to analyze the emotional states of all family members and create a shopping list that will make everyone feel positive. For example, the shopping support unit uses the emotion estimation function to analyze the emotional states of all family members and create a shopping list based on the results. For example, it can suggest products that will make everyone feel positive. The shopping support unit also develops an algorithm that analyzes the emotional states of all family members in real time and creates a shopping list that will make everyone feel positive. For example, it can provide optimal products based on emotion scores. The shopping support unit also uses the emotion estimation function to build a system that analyzes the emotional states of all family members and creates a shopping list based on the results. For example, it can suggest products that will make everyone happy. This makes it possible to create a shopping list that suits the emotional states of all family members.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can suggest menus to enhance specific nutrients based on the user's dietary history. For example, if the user is deficient in vitamin D, it will suggest menus using ingredients that are rich in vitamin D. The analysis unit also analyzes the user's dietary history and develops algorithms to enhance specific nutrients. For example, if a user is deficient in calcium, it will suggest dishes that are rich in calcium. The analysis unit also builds a system to suggest menus to enhance specific nutrients based on the user's dietary history. For example, if a user is deficient in iron, it will suggest dishes that use ingredients that are rich in iron. This makes it possible to suggest menus to improve the user's nutritional balance.

[0099] The analysis unit can suggest menus that correspond to specific health conditions based on the user's dietary history. For example, if the user has high blood pressure, it will suggest menus that are low in salt. The analysis unit also analyzes the user's health condition and develops algorithms that correspond to specific health conditions. For example, it will suggest low-carbohydrate dishes to a user with diabetes. The analysis unit also builds a system that suggests menus that correspond to specific health conditions based on the user's health condition. For example, it will suggest low-cholesterol dishes to a user with high cholesterol levels. This makes it possible to suggest menus that correspond to the user's health condition.

[0100] The analysis unit can suggest menus that correspond to a specific diet plan based on the user's diet history. For example, if the user is on a ketogenic diet, it will suggest low-carbohydrate menus. The analysis unit also analyzes the user's diet plan and develops an algorithm that corresponds to the specific diet plan. For example, for a user on a vegan diet, it will suggest dishes that do not contain animal products. The analysis unit also builds a system that suggests menus that correspond to a specific diet plan based on the user's diet plan. For example, for a user on a paleo diet, it will suggest dishes that avoid processed foods. This makes it possible to suggest menus that correspond to the user's diet plan.

[0101] The analysis unit can suggest menus that accommodate specific allergies based on the user's dietary history. For example, if the user is allergic to nuts, it will suggest menus that do not include nuts. The analysis unit also analyzes the user's allergy information and develops algorithms that accommodate specific allergies. For example, it will suggest gluten-free dishes to a user who is allergic to gluten. The analysis unit also builds a system that suggests menus that accommodate specific allergies based on the user's allergy information. For example, it will suggest dishes that do not include dairy products to a user who is allergic to dairy products. This makes it possible to suggest menus that suit the user's allergies.

[0102] The analysis unit can suggest menus that correspond to specific cultures and religions based on the user's dietary history. For example, if the user requires halal food, it will suggest menus that use halal-certified ingredients. The analysis unit also analyzes the user's culture and religion and develops algorithms that correspond to specific cultures and religions. For example, if a user requires kosher food, it will suggest kosher-certified dishes. The analysis unit also builds a system that suggests menus that correspond to specific cultures and religions based on the user's culture and religion. For example, it will suggest dishes that do not include meat to a vegetarian user. This makes it possible to suggest menus that correspond to the user's culture and religion.

[0103] The analysis unit uses the emotion estimation function to select ingredients according to the user's emotional state and suggest a menu that matches their mood. For example, if the user is tired, it suggests a menu using ingredients that will replenish energy. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and develop an algorithm to select ingredients based on the results. For example, if the user is feeling stressed, it suggests dishes using ingredients that have a relaxing effect. The analysis unit also uses the emotion estimation function to select ingredients according to the user's emotional state and build a system that suggests a menu that matches their mood. For example, if the user is happy, it suggests dishes using special ingredients. This makes it possible to select ingredients according to the user's emotional state and suggest a menu that matches their mood.

[0104] The analysis unit uses the emotion estimation function to select a cooking method according to the user's emotional state and suggest a menu that reduces stress. For example, if the user is tired, it suggests a menu using easy cooking methods. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and develops an algorithm that selects a cooking method based on the results. For example, if the user is feeling stressed, it suggests a dish that uses a cooking method that has a relaxing effect. The analysis unit also uses the emotion estimation function to build a system that selects a cooking method according to the user's emotional state and suggests a menu that reduces stress. For example, if the user is happy, it suggests a dish that uses a special cooking method. This makes it possible to select a cooking method according to the user's emotional state and suggest a menu that reduces stress.

[0105] The analysis unit can use the emotion estimation function to suggest meal timings based on the user's emotional state. For example, if the user is tired, it suggests having an early dinner. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and develop an algorithm that suggests meal timings based on the results. For example, if the user is feeling stressed, it suggests eating at a time when the user can relax. The analysis unit also uses the emotion estimation function to build a system that suggests meal timings based on the user's emotional state. For example, if the user is happy, it suggests eating at a special time. This makes it possible to suggest meal timings based on the user's emotional state.

[0106] The analysis unit can use the emotion estimation function to suggest places to eat that correspond to the user's emotional state. For example, if the user is tired, it suggests eating at home. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and develops an algorithm to suggest places to eat based on the results. For example, if the user is feeling stressed, it suggests eating at a place where the user can relax. The analysis unit also uses the emotion estimation function to build a system that suggests places to eat that correspond to the user's emotional state. For example, if the user is happy, it suggests eating at a special place. This makes it possible to suggest places to eat that correspond to the user's emotional state.

[0107] The analysis unit can use the emotion estimation function to suggest a meal atmosphere that matches the user's emotional state. For example, if the user is tired, a meal with a relaxing atmosphere is suggested. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and develop an algorithm that suggests a meal atmosphere based on the results. For example, if the user is feeling stressed, a meal with a calming atmosphere is suggested. The analysis unit also uses the emotion estimation function to build a system that suggests a meal atmosphere that matches the user's emotional state. For example, if the user is happy, a meal with a fun atmosphere is suggested. This makes it possible to suggest a meal atmosphere that matches the user's emotional state.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The voice input unit receives the user's voice. For example, the user can input a request by voice such as, "I want to eat fish today, but please avoid shrimp." Step 2: The analysis unit analyzes the voice received by the voice input unit. For example, it converts the voice into text using voice recognition technology to understand the user's request. Step 3: The menu generation unit generates a menu based on the results of the analysis by the analysis unit. For example, the generation AI creates a menu taking into account the user's likes and dislikes, allergy information, and nutritional balance. Step 4: The shopping support unit orders the necessary ingredients online based on the menu generated by the menu generation unit. For example, the generation AI lists the necessary ingredients and supports the online ordering process.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] 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]

[0177] 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 voice input unit that accepts a user's voice; an analysis unit that analyzes the voice received by the voice input unit; A menu creation unit that creates a menu based on the results of the analysis by the analysis unit; a shopping support unit that orders necessary ingredients online based on the menu created by the menu creation unit. A system characterized by:

2. The voice input unit Analyzes the tone and speed of the user's voice to estimate stress and fatigue levels and reflect this in menu planning.

2. The system of claim 1.

3. The analysis unit Refer to the user's past request history and make suggestions taking into account changes and trends in preferences 2. The system of claim 1.

4. The analysis unit Analyzes the user's emotional state in real time and suggests menus that match their mood 2. The system of claim 1.

5. The voice input unit In addition to voice input, gesture and facial expression recognition are combined to collect requests more intuitively.

2. The system of claim 1.

6. The voice input unit When you input your voice, the system simultaneously collects requests from all family members and suggests menus that will satisfy everyone.

2. The system of claim 1.

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