System

Abstract

An object of a system according to an embodiment is to provide a recipe on the basis of specific conditions and preferences of a user and to provide support during cooking.SOLUTION: A system includes a generation unit, an interaction unit, and a proposal unit. The generation unit generates a recipe based on specific conditions and preferences of the user. The interaction unit provides support through interaction with the user based on the recipe generated by the generation unit. The proposal unit proposes an alternative material on the basis of the information provided by the interaction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide recipes based on a user's specific requirements and preferences, nor do they adequately support the user while cooking, leaving room for improvement.

[0005] The system according to the embodiment aims to provide recipes based on the user's specific conditions and preferences and to provide support during cooking. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a dialogue unit, and a suggestion unit. The generation unit generates a recipe based on a user's specific conditions and preferences. The dialogue unit provides support through dialogue with the user based on the recipe generated by the generation unit. The suggestion unit suggests alternative ingredients based on information provided by the dialogue unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide recipes based on the user's specific requirements and preferences and provide support during cooking. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A recipe provision system according to an embodiment of the present invention uses a generation AI to provide individually optimized recipes based on a user's specific conditions and preferences. The recipe provision system allows users to input their own conditions and preferences, and the generation AI analyzes the information to generate an optimal recipe. The generated recipe is provided through dialogue with the user and also provides answers to questions the user may have while cooking and suggests alternative ingredients. For example, the recipe provision system allows users to input allergy information, likes and dislikes, and ingredients the user has on hand. This information is then input into the generation AI. The recipe provision system then analyzes the input information and generates an optimal recipe using the generation AI. The generation AI individually optimizes the recipe based on the user's conditions and preferences. For example, if the user has an allergy, the system generates a recipe that does not include that ingredient. The generated recipe is provided through dialogue with the user. When the user asks a question while cooking, the generation AI provides an appropriate answer to the question. For example, in response to a question such as, "Can I substitute this ingredient with something else?", the generation AI suggests an alternative ingredient. This allows the recipe provision system to easily obtain recipes based on the user's conditions and preferences and provide support during cooking. This improves cooking efficiency and increases user satisfaction.

[0029] A recipe providing system according to an embodiment includes a generation unit, a dialogue unit, and a suggestion unit. The generation unit generates recipes based on a user's specific conditions and preferences. For example, the generation unit analyzes information entered by the user, such as allergy information, likes and dislikes, and available ingredients, to generate an optimal recipe. The generation unit can also use a generation AI to individually optimize recipes based on the user's conditions and preferences. For example, if a user has an allergy, it can generate a recipe that does not include that ingredient. The dialogue unit provides support through dialogue with the user based on the recipe generated by the generation unit. For example, when a user asks a question while cooking, the dialogue unit provides an appropriate answer to the question. The dialogue unit can also use a generation AI to provide an appropriate answer to the user's question. For example, in response to a question such as, "Can I substitute this ingredient with something else?", the generation AI can suggest an alternative ingredient. The suggestion unit suggests an alternative ingredient based on the information provided by the dialogue unit. For example, if a user is running low on an ingredient to use while cooking, the suggestion unit suggests an alternative ingredient to replace that ingredient. The suggestion unit can also use a generation AI to suggest an alternative ingredient based on the user's conditions and preferences. For example, if a user has an allergy, it can suggest alternative ingredients that do not contain that ingredient. As a result, the recipe providing system according to the embodiment can provide recipes based on the user's conditions and preferences, provide support through dialogue, and suggest alternative ingredients.

[0030] The generation unit can analyze the user's past cooking history and select an appropriate recipe. For example, the generation unit uses data on dishes the user has made in the past to have the generation AI suggest similar recipes. The generation unit can also analyze trends in the dishes the user has liked to make in the past, and have the generation AI generate recipes that match those trends. The generation unit can also use data on dishes the user has avoided in the past to generate recipes that avoid those dishes. This makes it possible to provide optimal recipes based on the user's past cooking history.

[0031] When generating a recipe, the generation unit can adjust the recipe based on the user's current health condition and nutritional balance. For example, if the user is on a diet, the generation AI can generate a low-calorie recipe. Also, if the user is aiming to build muscle, the generation unit can generate a high-protein recipe. Also, if the user needs to consume a specific nutrient, the generation AI can generate a recipe that includes that nutrient. This makes it possible to optimize recipes according to the user's health condition and nutritional balance.

[0032] When generating a recipe, the generation unit can adjust the recipe based on the availability of ingredients in the user's area. For example, the generation unit generates a recipe using the generation AI based on ingredients that are seasonally available in the user's area. The generation unit can also generate a recipe using the generation AI based on ingredients that are generally easily available in the user's area. Furthermore, if a specific ingredient is in short supply in the user's area, the generation unit can generate a recipe that does not use that ingredient. This makes it possible to adjust the recipe according to the availability of ingredients in the user's area.

[0033] When generating a recipe, the generation unit can customize the recipe based on the user's family structure and meal sharing. For example, if the user lives alone, the generation AI generates a recipe for one person. Also, if the user lives with their family, the generation unit can generate recipes for the whole family. Also, if the user plans to share a meal with a friend, the generation AI can generate a recipe that is easy to share. This makes it possible to customize recipes according to the user's family structure and meal sharing.

[0034] When generating a recipe, the generation unit can suggest recipes taking into consideration the storage status of the user's ingredients. For example, when the user inputs ingredients that are in the refrigerator, the generation AI generates a recipe using those ingredients. Also, when the user inputs ingredients that are close to their expiration date, the generation unit can generate a recipe that prioritizes using those ingredients. Also, when the user wants to use up a specific ingredient, the generation unit can have the generation AI generate a recipe that uses that ingredient. This makes it possible to suggest recipes that suit the user's storage status of ingredients.

[0035] When generating a recipe, the generation unit can adjust the level of detail of the recipe according to the user's cooking skill level. For example, if the user is a beginner, the generation AI can generate a recipe that includes detailed steps. Also, if the user is an intermediate cook, the generation unit can generate a recipe that omits basic steps. Also, if the user is an advanced cook, the generation unit can generate a recipe that includes advanced techniques. This makes it possible to adjust the level of detail of the recipe according to the user's cooking skill level.

[0036] During a dialogue, the dialogue unit can provide optimal support by referring to the user's past dialogue history. For example, the dialogue unit allows the generation AI to provide relevant information based on questions the user has asked in the past. The dialogue unit can also conduct a dialogue in a style that the generation AI has preferred in the past based on that style. The dialogue unit can also conduct a dialogue in a way that the generation AI has avoided topics that the user has avoided in the past based on that topic. This makes it possible to provide optimal support based on the user's past dialogue history.

[0037] During the dialogue, the dialogue unit can grasp the user's current cooking progress in real time and provide appropriate advice. For example, if the user asks a question while cooking, the dialogue unit allows the generation AI to provide advice according to the progress. Furthermore, if the user makes a mistake in a cooking step, the dialogue unit can allow the generation AI to provide advice on how to correct the mistake in real time. Furthermore, when the user reports the cooking progress, the dialogue unit can allow the generation AI to guide the user on the next step. This makes it possible to provide appropriate advice according to the user's cooking progress.

[0038] During the dialogue, the dialogue unit can adjust the tempo of the dialogue based on the frequency and content of the user's questions. For example, if the user asks questions frequently, the dialogue unit can have the generation AI speed up the dialogue. Also, if the user asks questions slowly, the dialogue unit can have the generation AI slow down the dialogue. Also, if the user focuses on a specific topic and asks questions, the dialogue unit can have the generation AI focus on that topic and conduct the dialogue. This makes it possible to adjust the tempo of the dialogue according to the frequency and content of the user's questions.

[0039] The dialogue unit can select the optimal dialogue method during dialogue, taking into account the user's device information. For example, if the user is using a smartphone, the generation AI can provide a dialogue method that matches the screen size. Furthermore, if the user is using a tablet, the generation AI can provide a dialogue method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can provide a simple and highly visible dialogue method. This makes it possible to select the optimal dialogue method according to the user's device information.

[0040] The dialogue unit can provide multilingual support according to the user's language settings during dialogue. For example, the dialogue unit has the generation AI automatically set the dialogue language based on the language settings of the user's device. In addition, the dialogue unit can also have the generation AI provide a language switching function when the user uses multiple languages. In addition, in the dialogue unit, when the user selects a specific language, the generation AI can provide dialogue in that language. This makes it possible to provide multilingual support according to the user's language settings.

[0041] During the dialogue, the dialogue unit can provide advice based on the user's cooking environment. For example, the dialogue unit can have the generation AI provide advice that is suited to the kitchen equipment the user owns. The dialogue unit can also have the generation AI provide advice that is suited to the cooking utensils the user owns. The dialogue unit can also have the generation AI suggest alternative methods if the user does not have specific equipment or utensils. This makes it possible to provide advice that is suited to the user's cooking environment.

[0042] When proposing an alternative material, the suggestion unit can make the optimal suggestion by referring to the user's past use history of the alternative material. For example, the suggestion unit may have the generation AI re-suggest an alternative material based on an alternative material that the user has used in the past. The suggestion unit may also have the generation AI suggest an alternative material based on an alternative material that the user has preferred in the past. The suggestion unit may also have the generation AI make a suggestion that avoids an alternative material that the user has avoided in the past. This makes it possible to make optimal suggestions based on the user's past use history of the alternative material.

[0043] When proposing alternative ingredients, the suggestion unit can take into consideration the user's allergy information and health condition. For example, if the user has an allergy, the suggestion unit allows the generation AI to suggest alternative ingredients that do not contain the allergic ingredients. Furthermore, if the user has a specific health condition (e.g., high blood pressure or diabetes), the suggestion unit can also allow the generation AI to suggest alternative ingredients that are suitable for that condition. Furthermore, if the user needs to ingest a specific nutrient, the suggestion unit can also suggest alternative ingredients that contain that nutrient. This makes it possible to suggest alternative ingredients that take into account the user's allergy information and health condition.

[0044] When suggesting alternative ingredients, the suggestion unit can make suggestions taking into account the availability of ingredients in the user's area. For example, the suggestion unit allows the generation AI to suggest alternative ingredients based on ingredients that are seasonally available in the user's area. The suggestion unit can also allow the generation AI to suggest alternative ingredients based on ingredients that are generally easy to obtain in the user's area. Furthermore, if a specific ingredient is in short supply in the user's area, the suggestion unit can also suggest alternative ingredients that do not use that ingredient. This makes it possible to suggest alternative ingredients based on the availability of ingredients in the user's area.

[0045] When suggesting alternative ingredients, the suggestion unit can adjust the level of detail of the suggestion according to the user's cooking skill level. For example, if the user is a beginner, the suggestion unit causes the generation AI to suggest alternative ingredients that include detailed steps. Furthermore, if the user is an intermediate cook, the suggestion unit can also cause the generation AI to suggest alternative ingredients that omit basic steps. Furthermore, if the user is an advanced cook, the suggestion unit can also cause the generation AI to suggest alternative ingredients that include advanced techniques. This makes it possible to adjust the level of detail of the suggested alternative ingredients according to the user's cooking skill level.

[0046] When suggesting substitute ingredients, the suggestion unit can make suggestions taking into account the storage status of the user's ingredients. For example, when the user inputs ingredients they have in their refrigerator, the suggestion unit allows the generation AI to suggest substitute ingredients using those ingredients. In addition, when the user inputs ingredients that are close to their expiration date, the suggestion unit can also suggest substitute ingredients that prioritize using those ingredients. In addition, if the user wants to use up a specific ingredient, the suggestion unit can allow the generation AI to suggest substitute ingredients that use that ingredient. This makes it possible to suggest substitute ingredients that suit the storage status of the user's ingredients.

[0047] When suggesting alternative ingredients, the suggestion unit can make suggestions taking into consideration the user's cooking purpose. For example, if the user is on a diet, the suggestion unit's generation AI can suggest low-calorie alternative ingredients. Also, if the user is aiming to build muscle, the suggestion unit's generation AI can suggest high-protein alternative ingredients. Also, if the user needs to ingest a specific nutrient, the suggestion unit's generation AI can suggest alternative ingredients that contain that nutrient. This makes it possible to suggest alternative ingredients according to the user's cooking purpose.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The generator can also adjust recipes based on the user's mealtimes. For example, for breakfast, the generator AI generates a simple recipe that can be made in a short amount of time. For lunch, the generator AI can also generate a nutritionally balanced recipe. Furthermore, for dinner, the generator AI can generate a slightly more elaborate recipe. This makes it possible to optimize recipes according to the user's mealtimes.

[0050] The generation unit can also analyze the user's past cooking history and select recipes suited to specific events or seasons. For example, the generation AI can suggest Christmas recipes based on data on dishes the user has made for Christmas in the past. The generation AI can also generate summer recipes by analyzing trends in dishes the user has previously liked to make in the summer. Furthermore, the generation AI can generate recipes that avoid events the user has avoided in the past based on data on events the user has avoided in the past. This makes it possible to provide optimal recipes for events and seasons based on the user's past cooking history.

[0051] The generation unit can also take into account advice from the user's doctor or nutritionist when adjusting recipes based on the user's current health condition and nutritional balance. For example, if a user has been instructed by a doctor to consume specific nutrients, the generation AI can generate recipes that include those nutrients. Also, if the user has received advice from a nutritionist, the generation AI can generate recipes based on that advice. Furthermore, if the user has a specific health condition, the generation AI can generate recipes that are appropriate for that condition. This makes it possible to optimize recipes according to the user's health condition and nutritional balance.

[0052] The generation unit can also take into account traditional and local cuisine in the region when adjusting the recipe based on the availability of ingredients in the user's region. For example, based on a dish traditionally made in the user's region, the generation AI can generate a recipe that is an adaptation of that dish. Also, based on a local dish that is popular in the user's region, the generation AI can generate a recipe that incorporates that dish. Furthermore, if a specific ingredient is abundant in the user's region, the generation AI can generate a recipe that uses that ingredient. This makes it possible to adjust the recipe according to the availability of ingredients in the user's region.

[0053] The generator can also take into account the health status and allergy information of family members when customizing recipes based on the user's family composition and meal sharing. For example, if someone in the family has an allergy, the generator AI can generate a recipe that does not contain the allergic ingredient. Also, if someone in the family has a specific health condition, the generator AI can generate a recipe that is appropriate for that condition. Furthermore, the generator AI can generate a balanced recipe by taking into account the nutritional balance of each family member. This makes it possible to customize recipes according to family composition and meal sharing.

[0054] When proposing a recipe taking into account the storage conditions of ingredients of the user, the generation unit can also adjust the recipe based on the storage method and storage period of the ingredients. For example, when generating a recipe using ingredients that need to be stored in the refrigerator, the generation AI will propose the recipe taking into account the storage period of the ingredients. Also, when generating a recipe using ingredients that can be stored in the freezer, the generation AI can also propose a recipe that includes a method for thawing the ingredients. Furthermore, when generating a recipe using ingredients that can be stored in a dry state, the generation AI can also propose a recipe taking into account the storage method of the ingredients. This makes it possible to propose recipes that suit the storage conditions of the ingredients of the user.

[0055] The generator can also take the user's willingness to learn into account when adjusting the level of detail in the recipe according to the user's cooking skill level. For example, if the user is a beginner and highly motivated to learn, the generator AI can generate recipes that teach basic cooking techniques with detailed steps. Alternatively, if the user is an intermediate cook and wants to master a specific technique, the generator AI can generate recipes that focus on that technique. Furthermore, if the user is an advanced cook and is looking for a new challenge, the generator AI can generate recipes that include advanced techniques. This makes it possible to adjust the level of detail in the recipe according to the user's cooking skill level and willingness to learn.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The generator generates a recipe based on the user's specific conditions and preferences. For example, it analyzes information entered by the user, such as allergies, likes and dislikes, and ingredients available, to generate the optimal recipe. The generator can also use generative AI to individually optimize recipes based on the user's conditions and preferences. For example, if a user has an allergy, it can generate a recipe that does not include that ingredient. Step 2: The dialogue unit provides support through dialogue with the user based on the recipe generated by the generation unit. For example, if the user asks a question while cooking, the dialogue unit provides an appropriate answer to that question. The dialogue unit can also use the generation AI to provide appropriate answers to the user's questions. For example, in response to the question, "Can I substitute this ingredient with something else?", the generation AI can suggest alternative ingredients. Step 3: The suggestion unit suggests alternative ingredients based on the information provided by the dialogue unit. For example, if the user is running low on an ingredient they are using while cooking, the suggestion unit suggests an ingredient to replace that ingredient. The suggestion unit can also use generative AI to suggest alternative ingredients based on the user's conditions and preferences. For example, if the user has an allergy, it can suggest alternative ingredients that do not contain that ingredient.

[0058] (Example 2) A recipe provision system according to an embodiment of the present invention uses a generation AI to provide individually optimized recipes based on a user's specific conditions and preferences. The recipe provision system allows users to input their own conditions and preferences, and the generation AI analyzes the information to generate an optimal recipe. The generated recipe is provided through dialogue with the user and also provides answers to questions the user may have while cooking and suggests alternative ingredients. For example, the recipe provision system allows users to input allergy information, likes and dislikes, and ingredients the user has on hand. This information is then input into the generation AI. The recipe provision system then analyzes the input information and generates an optimal recipe using the generation AI. The generation AI individually optimizes the recipe based on the user's conditions and preferences. For example, if the user has an allergy, the system generates a recipe that does not include that ingredient. The generated recipe is provided through dialogue with the user. When the user asks a question while cooking, the generation AI provides an appropriate answer to the question. For example, in response to a question such as, "Can I substitute this ingredient with something else?", the generation AI suggests an alternative ingredient. This allows the recipe provision system to easily obtain recipes based on the user's conditions and preferences and provide support during cooking. This improves cooking efficiency and increases user satisfaction.

[0059] A recipe providing system according to an embodiment includes a generation unit, a dialogue unit, and a suggestion unit. The generation unit generates recipes based on a user's specific conditions and preferences. For example, the generation unit analyzes information entered by the user, such as allergy information, likes and dislikes, and available ingredients, to generate an optimal recipe. The generation unit can also use a generation AI to individually optimize recipes based on the user's conditions and preferences. For example, if a user has an allergy, it can generate a recipe that does not include that ingredient. The dialogue unit provides support through dialogue with the user based on the recipe generated by the generation unit. For example, when a user asks a question while cooking, the dialogue unit provides an appropriate answer to the question. The dialogue unit can also use a generation AI to provide an appropriate answer to the user's question. For example, in response to a question such as, "Can I substitute this ingredient with something else?", the generation AI can suggest an alternative ingredient. The suggestion unit suggests an alternative ingredient based on the information provided by the dialogue unit. For example, if a user is running low on an ingredient to use while cooking, the suggestion unit suggests an alternative ingredient to replace that ingredient. The suggestion unit can also use a generation AI to suggest an alternative ingredient based on the user's conditions and preferences. For example, if a user has an allergy, it can suggest alternative ingredients that do not contain that ingredient. As a result, the recipe providing system according to the embodiment can provide recipes based on the user's conditions and preferences, provide support through dialogue, and suggest alternative ingredients.

[0060] The generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can generate a simple and hassle-free recipe. Also, if the user is relaxed, the generation unit can generate a slightly more elaborate recipe. Also, if the user is feeling challenging, the generation unit can generate a more difficult recipe. This makes it possible to adjust the difficulty of the recipe according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0061] The generation unit can analyze the user's past cooking history and select an appropriate recipe. For example, the generation unit uses data on dishes the user has made in the past to have the generation AI suggest similar recipes. The generation unit can also analyze trends in the dishes the user has liked to make in the past, and have the generation AI generate recipes that match those trends. The generation unit can also use data on dishes the user has avoided in the past to generate recipes that avoid those dishes. This makes it possible to provide optimal recipes based on the user's past cooking history.

[0062] When generating a recipe, the generation unit can adjust the recipe based on the user's current health condition and nutritional balance. For example, if the user is on a diet, the generation AI can generate a low-calorie recipe. Also, if the user is aiming to build muscle, the generation unit can generate a high-protein recipe. Also, if the user needs to consume a specific nutrient, the generation AI can generate a recipe that includes that nutrient. This makes it possible to optimize recipes according to the user's health condition and nutritional balance.

[0063] When generating a recipe, the generation unit can adjust the recipe based on the availability of ingredients in the user's area. For example, the generation unit generates a recipe using the generation AI based on ingredients that are seasonally available in the user's area. The generation unit can also generate a recipe using the generation AI based on ingredients that are generally easily available in the user's area. Furthermore, if a specific ingredient is in short supply in the user's area, the generation unit can generate a recipe that does not use that ingredient. This makes it possible to adjust the recipe according to the availability of ingredients in the user's area.

[0064] The generation unit can estimate the user's emotions and adjust the presentation method of the recipe based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple and easy-to-read recipe. If the user is feeling relaxed, the generation AI can also provide a recipe with detailed instructions. If the user is feeling challenging, the generation AI can also provide a visually appealing recipe. This makes it possible to adjust the presentation method of the recipe according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0065] When generating a recipe, the generation unit can customize the recipe based on the user's family structure and meal sharing. For example, if the user lives alone, the generation AI generates a recipe for one person. Also, if the user lives with their family, the generation unit can generate recipes for the whole family. Also, if the user plans to share a meal with a friend, the generation AI can generate a recipe that is easy to share. This makes it possible to customize recipes according to the user's family structure and meal sharing.

[0066] When generating a recipe, the generation unit can suggest recipes taking into consideration the storage status of the user's ingredients. For example, when the user inputs ingredients that are in the refrigerator, the generation AI generates a recipe using those ingredients. Also, when the user inputs ingredients that are close to their expiration date, the generation unit can generate a recipe that prioritizes using those ingredients. Also, when the user wants to use up a specific ingredient, the generation unit can have the generation AI generate a recipe that uses that ingredient. This makes it possible to suggest recipes that suit the user's storage status of ingredients.

[0067] When generating a recipe, the generation unit can adjust the level of detail of the recipe according to the user's cooking skill level. For example, if the user is a beginner, the generation AI can generate a recipe that includes detailed steps. Also, if the user is an intermediate cook, the generation unit can generate a recipe that omits basic steps. Also, if the user is an advanced cook, the generation unit can generate a recipe that includes advanced techniques. This makes it possible to adjust the level of detail of the recipe according to the user's cooking skill level.

[0068] The dialogue unit can estimate the user's emotions and adjust the tone and style of the dialogue based on the estimated user emotions. For example, if the user is feeling stressed, the dialogue unit can have the generation AI speak in a calm tone. If the user is relaxed, the dialogue unit can also have the generation AI speak in a friendly tone. If the user is feeling challenging, the dialogue unit can also have the generation AI speak in an encouraging tone. This makes it possible to adjust the tone and style of the dialogue according to the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] During a dialogue, the dialogue unit can provide optimal support by referring to the user's past dialogue history. For example, the dialogue unit allows the generation AI to provide relevant information based on questions the user has asked in the past. The dialogue unit can also conduct a dialogue in a style that the generation AI has preferred in the past based on that style. The dialogue unit can also conduct a dialogue in a way that the generation AI has avoided topics that the user has avoided in the past based on that topic. This makes it possible to provide optimal support based on the user's past dialogue history.

[0070] During the dialogue, the dialogue unit can grasp the user's current cooking progress in real time and provide appropriate advice. For example, if the user asks a question while cooking, the dialogue unit allows the generation AI to provide advice according to the progress. Furthermore, if the user makes a mistake in a cooking step, the dialogue unit can allow the generation AI to provide advice on how to correct the mistake in real time. Furthermore, when the user reports the cooking progress, the dialogue unit can allow the generation AI to guide the user on the next step. This makes it possible to provide appropriate advice according to the user's cooking progress.

[0071] During the dialogue, the dialogue unit can adjust the tempo of the dialogue based on the frequency and content of the user's questions. For example, if the user asks questions frequently, the dialogue unit can have the generation AI speed up the dialogue. Also, if the user asks questions slowly, the dialogue unit can have the generation AI slow down the dialogue. Also, if the user focuses on a specific topic and asks questions, the dialogue unit can have the generation AI focus on that topic and conduct the dialogue. This makes it possible to adjust the tempo of the dialogue according to the frequency and content of the user's questions.

[0072] The dialogue unit can estimate the user's emotions and customize the content of the dialogue based on the estimated user emotions. For example, if the user is feeling stressed, the dialogue unit can have the generation AI dialogue with content that will relax the user. Also, if the user is relaxed, the dialogue unit can have the generation AI dialogue with content that is fun. Also, if the user is feeling challenging, the dialogue unit can have the generation AI dialogue with content that will motivate the user. This makes it possible to customize the content of the dialogue according to the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] The dialogue unit can select the optimal dialogue method during dialogue, taking into account the user's device information. For example, if the user is using a smartphone, the generation AI can provide a dialogue method that matches the screen size. Furthermore, if the user is using a tablet, the generation AI can provide a dialogue method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the generation AI can provide a simple and highly visible dialogue method. This makes it possible to select the optimal dialogue method according to the user's device information.

[0074] The dialogue unit can provide multilingual support according to the user's language settings during dialogue. For example, the dialogue unit has the generation AI automatically set the dialogue language based on the language settings of the user's device. In addition, the dialogue unit can also have the generation AI provide a language switching function when the user uses multiple languages. In addition, in the dialogue unit, when the user selects a specific language, the generation AI can provide dialogue in that language. This makes it possible to provide multilingual support according to the user's language settings.

[0075] During the dialogue, the dialogue unit can provide advice based on the user's cooking environment. For example, the dialogue unit can have the generation AI provide advice that is suited to the kitchen equipment the user owns. The dialogue unit can also have the generation AI provide advice that is suited to the cooking utensils the user owns. The dialogue unit can also have the generation AI suggest alternative methods if the user does not have specific equipment or utensils. This makes it possible to provide advice that is suited to the user's cooking environment.

[0076] The suggestion unit can estimate the user's emotions and adjust the suggested alternative ingredients based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can cause the generation AI to suggest alternative ingredients that are easy to obtain. Also, if the user is relaxed, the suggestion unit can cause the generation AI to suggest alternative ingredients that require a little effort. Also, if the user is feeling challenging, the suggestion unit can cause the generation AI to suggest alternative ingredients that are more difficult. This makes it possible to adjust the suggested alternative ingredients according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] When proposing an alternative material, the suggestion unit can make the optimal suggestion by referring to the user's past use history of the alternative material. For example, the suggestion unit may have the generation AI re-suggest an alternative material based on an alternative material that the user has used in the past. The suggestion unit may also have the generation AI suggest an alternative material based on an alternative material that the user has preferred in the past. The suggestion unit may also have the generation AI make a suggestion that avoids an alternative material that the user has avoided in the past. This makes it possible to make optimal suggestions based on the user's past use history of the alternative material.

[0078] When proposing alternative ingredients, the suggestion unit can take into consideration the user's allergy information and health condition. For example, if the user has an allergy, the suggestion unit allows the generation AI to suggest alternative ingredients that do not contain the allergic ingredients. Furthermore, if the user has a specific health condition (e.g., high blood pressure or diabetes), the suggestion unit can also allow the generation AI to suggest alternative ingredients that are suitable for that condition. Furthermore, if the user needs to ingest a specific nutrient, the suggestion unit can also suggest alternative ingredients that contain that nutrient. This makes it possible to suggest alternative ingredients that take into account the user's allergy information and health condition.

[0079] When suggesting alternative ingredients, the suggestion unit can make suggestions taking into account the availability of ingredients in the user's area. For example, the suggestion unit allows the generation AI to suggest alternative ingredients based on ingredients that are seasonally available in the user's area. The suggestion unit can also allow the generation AI to suggest alternative ingredients based on ingredients that are generally easy to obtain in the user's area. Furthermore, if a specific ingredient is in short supply in the user's area, the suggestion unit can also suggest alternative ingredients that do not use that ingredient. This makes it possible to suggest alternative ingredients based on the availability of ingredients in the user's area.

[0080] The suggestion unit can estimate the user's emotions and prioritize the proposal of alternative ingredients based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can cause the generation AI to prioritize the proposal of alternative ingredients that are easy to obtain. Furthermore, if the user is relaxed, the suggestion unit can cause the generation AI to prioritize the proposal of alternative ingredients that require a little effort. Furthermore, if the user is feeling adventurous, the suggestion unit can cause the generation AI to prioritize the proposal of alternative ingredients that require a lot of effort. This makes it possible to prioritize the proposal of alternative ingredients according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When suggesting alternative ingredients, the suggestion unit can adjust the level of detail of the suggestion according to the user's cooking skill level. For example, if the user is a beginner, the suggestion unit causes the generation AI to suggest alternative ingredients that include detailed steps. Furthermore, if the user is an intermediate cook, the suggestion unit can also cause the generation AI to suggest alternative ingredients that omit basic steps. Furthermore, if the user is an advanced cook, the suggestion unit can also cause the generation AI to suggest alternative ingredients that include advanced techniques. This makes it possible to adjust the level of detail of the suggested alternative ingredients according to the user's cooking skill level.

[0082] When suggesting substitute ingredients, the suggestion unit can make suggestions taking into account the storage status of the user's ingredients. For example, when the user inputs ingredients they have in their refrigerator, the suggestion unit allows the generation AI to suggest substitute ingredients using those ingredients. In addition, when the user inputs ingredients that are close to their expiration date, the suggestion unit can also suggest substitute ingredients that prioritize using those ingredients. In addition, if the user wants to use up a specific ingredient, the suggestion unit can allow the generation AI to suggest substitute ingredients that use that ingredient. This makes it possible to suggest substitute ingredients that suit the storage status of the user's ingredients.

[0083] When suggesting alternative ingredients, the suggestion unit can make suggestions taking into consideration the user's cooking purpose. For example, if the user is on a diet, the suggestion unit's generation AI can suggest low-calorie alternative ingredients. Also, if the user is aiming to build muscle, the suggestion unit's generation AI can suggest high-protein alternative ingredients. Also, if the user needs to ingest a specific nutrient, the suggestion unit's generation AI can suggest alternative ingredients that contain that nutrient. This makes it possible to suggest alternative ingredients according to the user's cooking purpose. === Hard Collateral 1-1 === Each of the multiple elements, including the generation unit, dialogue unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and generates a recipe based on the user's conditions and preferences. The dialogue unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides support through dialogue with the user. The suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, dialogue unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and generates a recipe based on the user's conditions and preferences. The dialogue unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides support through dialogue with the user. The suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, dialogue unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and generates a recipe based on the user's conditions and preferences. The dialogue unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides support through dialogue with the user. The suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, dialogue unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and generates a recipe based on the user's conditions and preferences. The dialogue unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides support through dialogue with the user. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and suggests alternative ingredients.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The generator can also adjust recipes based on the user's mealtimes. For example, for breakfast, the generator AI generates a simple recipe that can be made in a short amount of time. For lunch, the generator AI can also generate a nutritionally balanced recipe. Furthermore, for dinner, the generator AI can generate a slightly more elaborate recipe. This makes it possible to optimize recipes according to the user's mealtimes.

[0086] The generation unit can also estimate the user's emotions and adjust the portion sizes of the recipe based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can generate a recipe with a small portion size. Alternatively, if the user is feeling relaxed, the generation AI can generate a recipe with a normal portion size. Furthermore, if the user is feeling adventurous, the generation AI can generate a recipe with a larger portion size. This makes it possible to adjust the portion sizes of the recipe according to the user's emotions.

[0087] The generation unit can also analyze the user's past cooking history and select recipes suited to specific events or seasons. For example, the generation AI can suggest Christmas recipes based on data on dishes the user has made for Christmas in the past. The generation AI can also generate summer recipes by analyzing trends in dishes the user has previously liked to make in the summer. Furthermore, the generation AI can generate recipes that avoid events the user has avoided in the past based on data on events the user has avoided in the past. This makes it possible to provide optimal recipes for events and seasons based on the user's past cooking history.

[0088] The generation unit can also take into account advice from the user's doctor or nutritionist when adjusting recipes based on the user's current health condition and nutritional balance. For example, if a user has been instructed by a doctor to consume specific nutrients, the generation AI can generate recipes that include those nutrients. Also, if the user has received advice from a nutritionist, the generation AI can generate recipes based on that advice. Furthermore, if the user has a specific health condition, the generation AI can generate recipes that are appropriate for that condition. This makes it possible to optimize recipes according to the user's health condition and nutritional balance.

[0089] The generation unit can also take into account traditional and local cuisine in the region when adjusting the recipe based on the availability of ingredients in the user's region. For example, based on a dish traditionally made in the user's region, the generation AI can generate a recipe that is an adaptation of that dish. Also, based on a local dish that is popular in the user's region, the generation AI can generate a recipe that incorporates that dish. Furthermore, if a specific ingredient is abundant in the user's region, the generation AI can generate a recipe that uses that ingredient. This makes it possible to adjust the recipe according to the availability of ingredients in the user's region.

[0090] The generation unit can also estimate the user's emotions and adjust the cooking time of the recipe based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can generate a recipe that can be made in a short time. Alternatively, if the user is relaxed, the generation AI can generate a recipe that takes a normal cooking time. Furthermore, if the user is feeling adventurous, the generation AI can generate a recipe that takes a long time. This makes it possible to adjust the cooking time of a recipe according to the user's emotions.

[0091] The generator can also take into account the health status and allergy information of family members when customizing recipes based on the user's family composition and meal sharing. For example, if someone in the family has an allergy, the generator AI can generate a recipe that does not contain the allergic ingredient. Also, if someone in the family has a specific health condition, the generator AI can generate a recipe that is appropriate for that condition. Furthermore, the generator AI can generate a balanced recipe by taking into account the nutritional balance of each family member. This makes it possible to customize recipes according to family composition and meal sharing.

[0092] When proposing a recipe taking into account the storage conditions of ingredients of the user, the generation unit can also adjust the recipe based on the storage method and storage period of the ingredients. For example, when generating a recipe using ingredients that need to be stored in the refrigerator, the generation AI will propose the recipe taking into account the storage period of the ingredients. Also, when generating a recipe using ingredients that can be stored in the freezer, the generation AI can also propose a recipe that includes a method for thawing the ingredients. Furthermore, when generating a recipe using ingredients that can be stored in a dry state, the generation AI can also propose a recipe taking into account the storage method of the ingredients. This makes it possible to propose recipes that suit the storage conditions of the ingredients of the user.

[0093] The generator can also take the user's willingness to learn into account when adjusting the level of detail in the recipe according to the user's cooking skill level. For example, if the user is a beginner and highly motivated to learn, the generator AI can generate recipes that teach basic cooking techniques with detailed steps. Alternatively, if the user is an intermediate cook and wants to master a specific technique, the generator AI can generate recipes that focus on that technique. Furthermore, if the user is an advanced cook and is looking for a new challenge, the generator AI can generate recipes that include advanced techniques. This makes it possible to adjust the level of detail in the recipe according to the user's cooking skill level and willingness to learn.

[0094] The dialogue unit can also take the user's hobbies and interests into consideration when estimating the user's emotions and customizing the content of the dialogue based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can engage in a dialogue on relaxing topics related to the user's hobbies. Alternatively, if the user is relaxed, the generation AI can engage in a dialogue on fun topics related to the user's interests. Furthermore, if the user is feeling challenged, the generation AI can engage in a dialogue on motivating topics related to the user's hobbies. This makes it possible to customize the content of the dialogue according to the user's emotions and hobbies and interests.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The generator generates a recipe based on the user's specific conditions and preferences. For example, it analyzes information entered by the user, such as allergies, likes and dislikes, and ingredients available, to generate the optimal recipe. The generator can also use generative AI to individually optimize recipes based on the user's conditions and preferences. For example, if a user has an allergy, it can generate a recipe that does not include that ingredient. Step 2: The dialogue unit provides support through dialogue with the user based on the recipe generated by the generation unit. For example, if the user asks a question while cooking, the dialogue unit provides an appropriate answer to that question. The dialogue unit can also use the generation AI to provide appropriate answers to the user's questions. For example, in response to the question, "Can I substitute this ingredient with something else?", the generation AI can suggest alternative ingredients. Step 3: The suggestion unit suggests alternative ingredients based on the information provided by the dialogue unit. For example, if the user is running low on an ingredient they are using while cooking, the suggestion unit suggests an ingredient to replace that ingredient. The suggestion unit can also use generative AI to suggest alternative ingredients based on the user's conditions and preferences. For example, if the user has an allergy, it can suggest alternative ingredients that do not contain that ingredient.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 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 recipe generator that generates recipes based on specific conditions and preferences of a user; a dialogue unit that provides support through dialogue with a user based on the recipe generated by the generation unit; a suggestion unit that suggests an alternative material based on the information provided by the dialogue unit. A system characterized by:

2. The generation unit Infer user emotions and adjust the difficulty of recipes based on the estimated user emotions The system of claim 1 .

3. The generation unit Analyze the user's cooking history and select appropriate recipes The system of claim 1 .

4. The generation unit When generating recipes, adjust them based on the user's current health status and nutritional balance. The system of claim 1 .

5. The generation unit At recipe generation time, adjust the recipe based on the availability of ingredients in the user's area The system of claim 1 .

6. The generation unit Infer user emotions and adjust recipe presentation based on the inferred user emotions The system of claim 1 .

7. The generation unit Customize recipes based on the user's family structure and meal sharing preferences when generating recipes The system of claim 1 .

8. The generation unit When generating recipes, the system considers the user's storage status of ingredients and suggests recipes. The system of claim 1 .

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