system

Abstract

The system according to this embodiment aims to suggest alternative recipes that accommodate allergies and religious restrictions, and to accept customer reviews. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, and a review unit. The reception unit receives input of the dish name and allergy ingredients. The suggestion unit proposes an alternative recipe based on the information received by the reception unit. The review unit receives customer reviews based on the alternative recipe proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, alternative recipes corresponding to allergies and religious restrictions have not been sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose alternative recipes corresponding to allergies and religious restrictions and receive customer reviews.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a review unit. The reception unit receives an input of a dish name and allergic ingredients. The proposal unit proposes an alternative recipe based on the information received by the reception unit. The review unit receives a customer review based on the alternative recipe proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest alternative recipes that accommodate allergies and religious restrictions, and can accept customer reviews. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F​​​​​​​The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The alternative recipe suggestion system according to an embodiment of the present invention is a mechanism that enables the entire restaurant industry to cooperate in solving issues such as allergies and religious restrictions without burden, through a store-specific AI alternative recipe suggestion service using a generation AI and an improvement flow based on reviews. The alternative recipe suggestion system allows users to input the name of a dish and allergen ingredients, and the generation AI suggests an alternative recipe, which is then improved based on customer reviews. This mechanism reduces the burden on restaurants in thinking up alternative recipes, allowing them to concentrate on new menu development and other tasks. For example, if a user inputs "shrimp chawanmushi" and "shellfish allergy," the generation AI suggests changing the shrimp to scallops. This suggestion also includes a note that scallops are a seafood like shrimp, have a flavor suitable for chawanmushi, have a texture similar to shrimp, and are cost-effective. Furthermore, the generation AI improves the alternative recipe based on customer reviews. For example, a customer provides feedback on the taste and texture of "scallop chawanmushi." This feedback is reflected in the generation AI and used for the next alternative recipe suggestion. This will continuously improve the quality of alternative recipes. This system reduces the burden on restaurants in developing alternative recipes, allowing them to focus on new menu development and other tasks. Furthermore, customers can enjoy meals without worrying about allergies or religious restrictions. For example, pregnant women, infants, and those with religious restrictions can enjoy dining out with peace of mind. In addition, the generation AI can select alternative ingredients considering the season, region, and cost of ingredients. This allows restaurants to provide high-quality alternative dishes while keeping costs down. For example, suggesting alternative recipes using seasonal ingredients can offer customers a new dining experience. In this way, the alternative recipe suggestion service using generation AI will enable the entire restaurant industry to collaboratively solve challenges such as allergies and religious restrictions without burden, realizing a world where everyone can enjoy food to their heart's content, even to the point of wanting seconds. Thus, the alternative recipe suggestion system will solve challenges such as allergies and religious restrictions across the entire restaurant industry, enabling everyone to enjoy meals with peace of mind.

[0029] The alternative recipe suggestion system according to this embodiment comprises a reception unit, a suggestion unit, and a review unit. The reception unit receives input of the dish name and allergy ingredients. For example, the user can input "shrimp chawanmushi" and "shellfish allergy" into the reception unit. The suggestion unit suggests an alternative recipe based on the information received by the reception unit. The suggestion unit uses a generation AI to select appropriate alternative ingredients based on the dish name and allergy ingredients. For example, the suggestion unit suggests "changing shrimp to scallops" for "shrimp chawanmushi." This suggestion also includes a note that scallops are a seafood product like shrimp, have a flavor suitable for chawanmushi, have a texture similar to shrimp, and are cost-effective. The review unit receives customer reviews based on the alternative recipe suggested by the suggestion unit. For example, the review unit allows customers to provide feedback on the taste and texture of "scallop chawanmushi." The review unit reflects the received feedback in the suggestion unit. This allows the proposal department to utilize the feedback in future alternative recipe proposals and continuously improve the quality of those alternative recipes. As a result, the alternative recipe proposal system according to this embodiment can propose alternative recipes based on user input information and accept reviews, thereby solving issues such as allergies and religious restrictions across the entire restaurant industry.

[0030] The reception desk accepts input of dish names and allergens. Specifically, it provides an interface for users to access the system and input dish names and allergens. This interface is designed for ease of use and includes features such as dropdown menus and auto-completion. When a user enters "shrimp chawanmushi" and "shellfish allergy," the reception desk saves this information to the database and passes it on to the next processing step. Furthermore, the reception desk also has a validation function to verify the accuracy of the information entered by the user. For example, if the dish name does not exist or the allergen is not on the list, it displays an error message to the user and prompts them to re-enter the information. The reception desk can also allow users to enter multiple allergens, enabling it to collect more detailed information. This allows the reception desk to respond flexibly to user needs and improve the overall accuracy and reliability of the system.

[0031] The suggestion department proposes alternative recipes based on the information received by the reception department. The suggestion department uses a generation AI to select appropriate substitute ingredients based on the dish name and allergens. Specifically, the generation AI first analyzes the entered dish name and allergens and searches for relevant recipes in the recipe database. Next, it identifies recipes containing the allergens and generates candidates for substituting those ingredients. For example, for "Shrimp Chawanmushi," it might suggest "changing the shrimp to scallops." This suggestion would also include notes that scallops are seafood like shrimp, have a flavor suitable for chawanmushi, have a similar texture to shrimp, and are cost-effective. The generation AI has an algorithm that selects the optimal substitute ingredient by considering past feedback data and reviews from other users. Furthermore, the suggestion department can present multiple alternatives, taking into account the user's preferences and the availability of ingredients. This allows users to choose an alternative recipe that suits their taste. The suggestion department also provides comments to explain the suggestions in detail and advice on cooking methods, providing information that will be helpful when users actually try the alternative recipes. This allows the suggestion department to provide users with high-quality, reliable alternative recipes, expanding meal options that accommodate allergies and dietary restrictions.

[0032] The review department receives customer reviews based on alternative recipes proposed by the suggestion department. Specifically, an interface is provided for users to provide feedback on the taste, texture, ease of preparation, etc., after trying an alternative recipe. This interface is designed to allow users to easily post reviews and includes features such as star ratings, comment sections, and photo upload functions. When a user provides feedback on the taste and texture of "Scallop Chawanmushi," the review department saves this information in a database and relays it to the suggestion department. The review department analyzes the feedback received so that the suggestion department can use it to propose alternative recipes in the future. For example, if the scallop alternative is well-received, similar alternatives can be proposed for other recipes. Also, if there is a lot of negative feedback from users, the suggestion department will review the alternative and use it as a reference to select more appropriate alternative ingredients. Furthermore, based on user feedback, the review department can identify areas for improvement in the overall system and improve the user experience. In this way, the review department can realize alternative recipe proposals that reflect user voices and improve the reliability and satisfaction of the system.

[0033] The suggestion function can provide reasons for selecting alternative ingredients. For example, the suggestion function might explain that scallops are a seafood product like shrimp, have a flavor suitable for chawanmushi (steamed egg custard), have a texture similar to shrimp, and offer good cost performance. By providing reasons for selecting alternative ingredients, the suggestion function can deepen the user's understanding of the suggested alternative recipe. Some or all of the above processing in the suggestion function may be performed using a generation AI, or not. For example, the suggestion function can input the reasons for selecting alternative ingredients into a generation AI, and the generation AI can output the reasons for selection.

[0034] The suggestion department can select alternative ingredients considering the season, region, and cost of the ingredients. For example, the suggestion department can propose alternative recipes using seasonal ingredients. By considering the season, region, and cost of the ingredients, the suggestion department can provide high-quality alternative dishes while keeping costs down. Some or all of the above processing in the suggestion department may be performed using a generation AI, or not. For example, the suggestion department can input information about the season, region, and cost of the ingredients into a generation AI, which can then select alternative ingredients.

[0035] The review department can receive feedback from customers and reflect it in the proposal department. For example, the review department might receive feedback from customers about the taste and texture of "scallop chawanmushi" (steamed egg custard with scallops). By reflecting the feedback received in the proposal department, the review department can continuously improve the quality of alternative recipes. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input customer feedback into an AI, which can then analyze the feedback and reflect it in the proposal department.

[0036] The suggestion department can make improvements to enhance the quality of alternative recipes. For example, the suggestion department can improve alternative recipes based on customer feedback. By improving the quality of alternative recipes, the suggestion department can increase customer satisfaction. Some or all of the above processes in the suggestion department may be performed using or without a generative AI. For example, the suggestion department can input customer feedback into a generative AI, which can then improve the alternative recipes.

[0037] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display as suggestions the dish names and allergens that the user has frequently entered in the past. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest dish names and allergens that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI, and the AI ​​can select the optimal input method.

[0038] The reception unit can filter the input of dish names and allergens based on the user's current dietary restrictions and health status. For example, if the user has diabetes, the reception unit can automatically exclude ingredients containing sugar. If the user is pregnant, the reception unit can automatically exclude ingredients unsuitable for pregnant women. If the user is on a specific diet, the reception unit can automatically exclude ingredients unsuitable for that diet. This allows for more appropriate alternative recipes to be provided by filtering according to the user's health status. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input information about the user's health status into the AI, which can then perform the filtering.

[0039] The reception desk can prioritize inputting highly relevant information when users enter dish names and allergens, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize suggesting ingredients that are readily available in that region. If a user is traveling, the reception desk can prioritize suggesting dish names that use local ingredients. If a user is in a specific country, the reception desk can prioritize suggesting dish names based on the food culture of that country. By considering geographical location, the reception desk can provide users with highly relevant information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, which can then select highly relevant information.

[0040] The reception desk can analyze the user's social media activity and input relevant information when the user enters the dish name and allergen information. For example, the reception desk can prioritize suggesting dish names that the user has shared on social media. The reception desk can prioritize suggesting recipes from cooking accounts that the user follows on social media. The reception desk can prioritize suggesting dish names that the user has "liked" on social media. In this way, by analyzing social media activity, the system can provide information that is highly relevant to the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can select relevant information.

[0041] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the ingredients when proposing alternative recipes. For example, it can provide detailed explanations for key ingredients and concise explanations for secondary ingredients. It can also provide detailed explanations for the selection of alternative ingredients and concise explanations for other ingredients. Furthermore, it can provide detailed explanations for important cooking steps and concise explanations for other steps. By adjusting the level of detail in suggestions based on the importance of the ingredients, it can provide users with important information. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input information about the importance of ingredients into a generative AI, which can then adjust the level of detail in the suggestions.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of cuisine when suggesting alternative recipes. For example, in the case of Japanese cuisine, the suggestion unit can apply a suggestion algorithm that emphasizes traditional cooking methods. In the case of Western cuisine, the suggestion unit can apply a suggestion algorithm that emphasizes modern cooking methods. In the case of ethnic cuisine, the suggestion unit can apply a suggestion algorithm that emphasizes the food culture of a specific region. By applying a suggestion algorithm according to the category of cuisine, it is possible to provide more appropriate alternative recipes. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input information about the category of cuisine into a generative AI, and the generative AI can apply a suggestion algorithm.

[0043] The suggestion department can prioritize suggestions based on the timing of ingredient submission when proposing alternative recipes. For example, the suggestion department may prioritize suggesting seasonal ingredients. The suggestion department may also prioritize suggesting ingredients with approaching expiration dates. The suggestion department may also prioritize suggesting seasonal ingredients. By prioritizing suggestions based on the timing of ingredient submission, the suggestion department can prioritize suggesting seasonal ingredients and ingredients with approaching expiration dates. Some or all of the above processing in the suggestion department may be performed using a generation AI, or not. For example, the suggestion department can input information about the timing of ingredient submission into a generation AI, which can then determine the priority of suggestions.

[0044] The suggestion unit can adjust the order of suggestions based on the relationships between ingredients when proposing alternative recipes. For example, the suggestion unit may suggest the main ingredients first and secondary ingredients later. The suggestion unit may explain the reasons for selecting alternative ingredients first and explain other ingredients later. The suggestion unit may explain important cooking steps first and explain other steps later. By adjusting the order of suggestions based on the relationships between ingredients, it becomes possible to provide suggestions that are easy for the user to understand. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input information about the relationships between ingredients into the generative AI, and the generative AI can adjust the order of suggestions.

[0045] The review department can select the optimal review method by referring to past review history when a review is received. For example, the review department can suggest the optimal review method based on the style of reviews the user has made in the past. The review department can prioritize displaying specific evaluation items from the user's past review history. The review department can analyze the user's past review history and suggest the most efficient review method. In this way, by referring to past review history, the review department can provide the user with the optimal review method. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input the user's past review history into AI, and the AI ​​can select the optimal review method.

[0046] The review department can conduct reviews while considering the user's attribute information when a review is received. For example, the review department can suggest the most suitable review method based on the user's age and gender. The review department can prioritize displaying specific evaluation items based on the user's occupation and lifestyle. The review department can customize the content of the review based on the user's hobbies and interests. This allows for the provision of a more appropriate review method by considering the user's attribute information. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input the user's attribute information into the AI, which can then select the most suitable review method.

[0047] The review department can select the most suitable review method when a review is received, taking into account the user's geographical location. For example, if the user is in a specific region, the review department can suggest a review method based on the local food culture. If the user is traveling, the review department can suggest a review method based on local ingredients. If the user is in a specific country, the review department can suggest a review method based on the food culture of that country. In this way, by considering geographical location, the review department can provide the user with the most suitable review method. Some or all of the above processing in the review department may be performed using AI or not. For example, the review department can input the user's geographical location information into the AI, which can then select the most suitable review method.

[0048] The review department can analyze the user's social media activity when a review is received and suggest a review method. For example, the review department can suggest the optimal review method based on reviews the user has shared on social media. The review department can suggest a review method based on the style of review accounts the user follows on social media. The review department can suggest a review method based on reviews the user has "liked" on social media. In this way, by analyzing social media activity, the review department can provide the user with the most suitable review method. Some or all of the above processing in the review department may be performed using AI or not. For example, the review department can input the user's social media activity into AI, and the AI ​​can select the most suitable review method.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The suggestion unit can analyze the user's past selection history and propose the most suitable alternative recipe. For example, it can suggest similar ingredients based on alternative ingredients the user has previously selected. It can suggest alternative recipes of a similar style based on cooking styles the user has previously preferred. It can suggest alternative recipes that exclude ingredients the user has previously avoided. In this way, by analyzing past selection history, the system can provide the most suitable alternative recipe for the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past selection history into AI, which can then select the most suitable alternative recipe.

[0051] The suggestion unit can propose alternative recipes considering the user's current health condition. For example, if the user has diabetes, it can suggest a sugar-free alternative recipe. If the user has high blood pressure, it can suggest a low-sodium alternative recipe. If the user is on a specific diet, it can suggest an alternative recipe suitable for that diet. This allows for more appropriate meal suggestions by providing alternative recipes tailored to the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input information about the user's health condition into the AI, which can then select the most suitable alternative recipe.

[0052] The suggestion unit can propose alternative recipes while considering the user's geographical location. For example, if the user is in a specific region, it can suggest alternative recipes using ingredients readily available in that region. If the user is traveling, it can suggest alternative recipes using local ingredients. If the user is in a specific country, it can suggest alternative recipes based on the food culture of that country. In this way, by considering geographical location, it is possible to provide alternative recipes that are highly relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location into the AI, which can then select the most suitable alternative recipe.

[0053] The suggestion function can analyze a user's social media activity and suggest relevant alternative recipes. For example, it can suggest alternative recipes based on the names of dishes the user has shared on social media. It can also suggest alternative recipes based on recipes from cooking accounts the user follows on social media. It can also suggest alternative recipes based on the names of dishes the user has "liked" on social media. In this way, by analyzing social media activity, it can provide alternative recipes that are highly relevant to the user. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the user's social media activity into AI, which can then select the most suitable alternative recipe.

[0054] The suggestion unit can propose alternative recipes while considering the user's attribute information. For example, it can suggest the most suitable alternative recipe based on the user's age and gender. It can suggest alternative recipes using specific ingredients based on the user's occupation and lifestyle. It can suggest relevant alternative recipes based on the user's hobbies and interests. In this way, by considering the user's attribute information, more appropriate alternative recipes can be provided. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's attribute information into the AI, and the AI ​​can select the most suitable alternative recipe.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk accepts input of the dish name and allergy ingredients. For example, a user can enter "Shrimp Chawanmushi" and "Shellfish Allergy". Step 2: The suggestion department proposes alternative recipes based on the information received by the reception department. The suggestion department uses a generation AI to select appropriate alternative ingredients based on the dish name and allergens. For example, for "Shrimp Chawanmushi," it might suggest "changing the shrimp to scallops." This suggestion would also include notes that scallops are seafood like shrimp, have a flavor suitable for chawanmushi, have a similar texture to shrimp, and are cost-effective. Step 3: The review department receives customer reviews based on the alternative recipes proposed by the suggestion department. For example, customers can provide feedback on the taste and texture of "Scallop Chawanmushi." The review department then incorporates the feedback received into the suggestion department. This allows the suggestion department to utilize the feedback in future alternative recipe suggestions and continuously improve the quality of the alternative recipes.

[0057] (Example of form 2) The alternative recipe suggestion system according to an embodiment of the present invention is a mechanism that enables the entire restaurant industry to cooperate in solving issues such as allergies and religious restrictions without burden, through a store-specific AI alternative recipe suggestion service using a generation AI and an improvement flow based on reviews. The alternative recipe suggestion system allows users to input the name of a dish and allergen ingredients, and the generation AI suggests an alternative recipe, which is then improved based on customer reviews. This mechanism reduces the burden on restaurants in thinking up alternative recipes, allowing them to concentrate on new menu development and other tasks. For example, if a user inputs "shrimp chawanmushi" and "shellfish allergy," the generation AI suggests changing the shrimp to scallops. This suggestion also includes a note that scallops are a seafood like shrimp, have a flavor suitable for chawanmushi, have a texture similar to shrimp, and are cost-effective. Furthermore, the generation AI improves the alternative recipe based on customer reviews. For example, a customer provides feedback on the taste and texture of "scallop chawanmushi." This feedback is reflected in the generation AI and used for the next alternative recipe suggestion. This will continuously improve the quality of alternative recipes. This system reduces the burden on restaurants in developing alternative recipes, allowing them to focus on new menu development and other tasks. Furthermore, customers can enjoy meals without worrying about allergies or religious restrictions. For example, pregnant women, infants, and those with religious restrictions can enjoy dining out with peace of mind. In addition, the generation AI can select alternative ingredients considering the season, region, and cost of ingredients. This allows restaurants to provide high-quality alternative dishes while keeping costs down. For example, suggesting alternative recipes using seasonal ingredients can offer customers a new dining experience. In this way, the alternative recipe suggestion service using generation AI will enable the entire restaurant industry to collaboratively solve challenges such as allergies and religious restrictions without burden, realizing a world where everyone can enjoy food to their heart's content, even to the point of wanting seconds. Thus, the alternative recipe suggestion system will solve challenges such as allergies and religious restrictions across the entire restaurant industry, enabling everyone to enjoy meals with peace of mind.

[0058] The alternative recipe suggestion system according to this embodiment comprises a reception unit, a suggestion unit, and a review unit. The reception unit receives input of the dish name and allergy ingredients. For example, the user can input "shrimp chawanmushi" and "shellfish allergy" into the reception unit. The suggestion unit suggests an alternative recipe based on the information received by the reception unit. The suggestion unit uses a generation AI to select appropriate alternative ingredients based on the dish name and allergy ingredients. For example, the suggestion unit suggests "changing shrimp to scallops" for "shrimp chawanmushi." This suggestion also includes a note that scallops are a seafood product like shrimp, have a flavor suitable for chawanmushi, have a texture similar to shrimp, and are cost-effective. The review unit receives customer reviews based on the alternative recipe suggested by the suggestion unit. For example, the review unit allows customers to provide feedback on the taste and texture of "scallop chawanmushi." The review unit reflects the received feedback in the suggestion unit. This allows the proposal department to utilize the feedback in future alternative recipe proposals and continuously improve the quality of those alternative recipes. As a result, the alternative recipe proposal system according to this embodiment can propose alternative recipes based on user input information and accept reviews, thereby solving issues such as allergies and religious restrictions across the entire restaurant industry.

[0059] The reception desk accepts input of dish names and allergens. Specifically, it provides an interface for users to access the system and input dish names and allergens. This interface is designed for ease of use and includes features such as dropdown menus and auto-completion. When a user enters "shrimp chawanmushi" and "shellfish allergy," the reception desk saves this information to the database and passes it on to the next processing step. Furthermore, the reception desk also has a validation function to verify the accuracy of the information entered by the user. For example, if the dish name does not exist or the allergen is not on the list, it displays an error message to the user and prompts them to re-enter the information. The reception desk can also allow users to enter multiple allergens, enabling it to collect more detailed information. This allows the reception desk to respond flexibly to user needs and improve the overall accuracy and reliability of the system.

[0060] The suggestion department proposes alternative recipes based on the information received by the reception department. The suggestion department uses a generation AI to select appropriate substitute ingredients based on the dish name and allergens. Specifically, the generation AI first analyzes the entered dish name and allergens and searches for relevant recipes in the recipe database. Next, it identifies recipes containing the allergens and generates candidates for substituting those ingredients. For example, for "Shrimp Chawanmushi," it might suggest "changing the shrimp to scallops." This suggestion would also include notes that scallops are seafood like shrimp, have a flavor suitable for chawanmushi, have a similar texture to shrimp, and are cost-effective. The generation AI has an algorithm that selects the optimal substitute ingredient by considering past feedback data and reviews from other users. Furthermore, the suggestion department can present multiple alternatives, taking into account the user's preferences and the availability of ingredients. This allows users to choose an alternative recipe that suits their taste. The suggestion department also provides comments to explain the suggestions in detail and advice on cooking methods, providing information that will be helpful when users actually try the alternative recipes. This allows the suggestion department to provide users with high-quality, reliable alternative recipes, expanding meal options that accommodate allergies and dietary restrictions.

[0061] The review department receives customer reviews based on alternative recipes proposed by the suggestion department. Specifically, an interface is provided for users to provide feedback on the taste, texture, ease of preparation, etc., after trying an alternative recipe. This interface is designed to allow users to easily post reviews and includes features such as star ratings, comment sections, and photo upload functions. When a user provides feedback on the taste and texture of "Scallop Chawanmushi," the review department saves this information in a database and relays it to the suggestion department. The review department analyzes the feedback received so that the suggestion department can use it to propose alternative recipes in the future. For example, if the scallop alternative is well-received, similar alternatives can be proposed for other recipes. Also, if there is a lot of negative feedback from users, the suggestion department will review the alternative and use it as a reference to select more appropriate alternative ingredients. Furthermore, based on user feedback, the review department can identify areas for improvement in the overall system and improve the user experience. In this way, the review department can realize alternative recipe proposals that reflect user voices and improve the reliability and satisfaction of the system.

[0062] The suggestion function can provide reasons for selecting alternative ingredients. For example, the suggestion function might explain that scallops are a seafood product like shrimp, have a flavor suitable for chawanmushi (steamed egg custard), have a texture similar to shrimp, and offer good cost performance. By providing reasons for selecting alternative ingredients, the suggestion function can deepen the user's understanding of the suggested alternative recipe. Some or all of the above processing in the suggestion function may be performed using a generation AI, or not. For example, the suggestion function can input the reasons for selecting alternative ingredients into a generation AI, and the generation AI can output the reasons for selection.

[0063] The suggestion department can select alternative ingredients considering the season, region, and cost of the ingredients. For example, the suggestion department can propose alternative recipes using seasonal ingredients. By considering the season, region, and cost of the ingredients, the suggestion department can provide high-quality alternative dishes while keeping costs down. Some or all of the above processing in the suggestion department may be performed using a generation AI, or not. For example, the suggestion department can input information about the season, region, and cost of the ingredients into a generation AI, which can then select alternative ingredients.

[0064] The review department can receive feedback from customers and reflect it in the proposal department. For example, the review department might receive feedback from customers about the taste and texture of "scallop chawanmushi" (steamed egg custard with scallops). By reflecting the feedback received in the proposal department, the review department can continuously improve the quality of alternative recipes. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input customer feedback into an AI, which can then analyze the feedback and reflect it in the proposal department.

[0065] The suggestion department can make improvements to enhance the quality of alternative recipes. For example, the suggestion department can improve alternative recipes based on customer feedback. By improving the quality of alternative recipes, the suggestion department can increase customer satisfaction. Some or all of the above processes in the suggestion department may be performed using or without a generative AI. For example, the suggestion department can input customer feedback into a generative AI, which can then improve the alternative recipes.

[0066] The reception desk can estimate the user's emotions and adjust the timing of inputting the dish name and allergens based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the dish name and allergens. This reduces user stress and increases input efficiency by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which can then perform emotion estimation.

[0067] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display as suggestions the dish names and allergens that the user has frequently entered in the past. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest dish names and allergens that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI, and the AI ​​can select the optimal input method.

[0068] The reception unit can filter the input of dish names and allergens based on the user's current dietary restrictions and health status. For example, if the user has diabetes, the reception unit can automatically exclude ingredients containing sugar. If the user is pregnant, the reception unit can automatically exclude ingredients unsuitable for pregnant women. If the user is on a specific diet, the reception unit can automatically exclude ingredients unsuitable for that diet. This allows for more appropriate alternative recipes to be provided by filtering according to the user's health status. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input information about the user's health status into the AI, which can then perform the filtering.

[0069] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of the dish names and allergy ingredients to be entered. For example, if the user is stressed, the reception desk will prioritize suggesting simple and easy-to-prepare dishes. If the user is relaxed, the reception desk can prioritize suggesting dishes that can be prepared over a longer period of time. If the user is in a hurry, the reception desk can prioritize suggesting dishes that can be prepared quickly. By prioritizing input according to the user's emotions, it becomes possible to provide suggestions that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which can then estimate the emotion.

[0070] The reception desk can prioritize inputting highly relevant information when users enter dish names and allergens, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can prioritize suggesting ingredients that are readily available in that region. If a user is traveling, the reception desk can prioritize suggesting dish names that use local ingredients. If a user is in a specific country, the reception desk can prioritize suggesting dish names based on the food culture of that country. By considering geographical location, the reception desk can provide users with highly relevant information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, which can then select highly relevant information.

[0071] The reception desk can analyze the user's social media activity and input relevant information when the user enters the dish name and allergen information. For example, the reception desk can prioritize suggesting dish names that the user has shared on social media. The reception desk can prioritize suggesting recipes from cooking accounts that the user follows on social media. The reception desk can prioritize suggesting dish names that the user has "liked" on social media. In this way, by analyzing social media activity, the system can provide information that is highly relevant to the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can select relevant information.

[0072] The suggestion unit can estimate the user's emotions and adjust the presentation of alternative recipes based on the estimated emotions. For example, if the user is relaxed, the suggestion unit's generating AI can suggest an alternative recipe with detailed explanations. If the user is in a hurry, the suggestion unit's generating AI can suggest a concise and to-the-point alternative recipe. If the user is excited, the suggestion unit's generating AI can suggest a visually appealing alternative recipe. By adjusting the presentation of alternative recipes according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generating AI or not. For example, the suggestion unit can input user emotion data into a generating AI, which can then estimate the emotion.

[0073] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the ingredients when proposing alternative recipes. For example, it can provide detailed explanations for key ingredients and concise explanations for secondary ingredients. It can also provide detailed explanations for the selection of alternative ingredients and concise explanations for other ingredients. Furthermore, it can provide detailed explanations for important cooking steps and concise explanations for other steps. By adjusting the level of detail in suggestions based on the importance of the ingredients, it can provide users with important information. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input information about the importance of ingredients into a generative AI, which can then adjust the level of detail in the suggestions.

[0074] The suggestion unit can apply different suggestion algorithms depending on the category of cuisine when suggesting alternative recipes. For example, in the case of Japanese cuisine, the suggestion unit can apply a suggestion algorithm that emphasizes traditional cooking methods. In the case of Western cuisine, the suggestion unit can apply a suggestion algorithm that emphasizes modern cooking methods. In the case of ethnic cuisine, the suggestion unit can apply a suggestion algorithm that emphasizes the food culture of a specific region. By applying a suggestion algorithm according to the category of cuisine, it is possible to provide more appropriate alternative recipes. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input information about the category of cuisine into a generative AI, and the generative AI can apply a suggestion algorithm.

[0075] The suggestion unit can estimate the user's emotions and adjust the length of alternative recipes based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit's generating AI can suggest a short, concise alternative recipe. If the user is relaxed, the suggestion unit's generating AI can suggest a longer alternative recipe with detailed explanations. If the user is excited, the suggestion unit's generating AI can suggest an alternative recipe with visually stimulating effects. By adjusting the length of alternative recipes according to the user's emotions, suggestions that are easy for the user to understand can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generating AI. For example, the suggestion unit can input user emotion data into a generating AI, which can then estimate the emotion.

[0076] The suggestion department can prioritize suggestions based on the timing of ingredient submission when proposing alternative recipes. For example, the suggestion department may prioritize suggesting seasonal ingredients. The suggestion department may also prioritize suggesting ingredients with approaching expiration dates. The suggestion department may also prioritize suggesting seasonal ingredients. By prioritizing suggestions based on the timing of ingredient submission, the suggestion department can prioritize suggesting seasonal ingredients and ingredients with approaching expiration dates. Some or all of the above processing in the suggestion department may be performed using a generation AI, or not. For example, the suggestion department can input information about the timing of ingredient submission into a generation AI, which can then determine the priority of suggestions.

[0077] The suggestion unit can adjust the order of suggestions based on the relationships between ingredients when proposing alternative recipes. For example, the suggestion unit may suggest the main ingredients first and secondary ingredients later. The suggestion unit may explain the reasons for selecting alternative ingredients first and explain other ingredients later. The suggestion unit may explain important cooking steps first and explain other steps later. By adjusting the order of suggestions based on the relationships between ingredients, it becomes possible to provide suggestions that are easy for the user to understand. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input information about the relationships between ingredients into the generative AI, and the generative AI can adjust the order of suggestions.

[0078] The review section can estimate the user's emotions and adjust how the review is displayed based on the estimated emotions. For example, if the user is nervous, the review section can provide a simple and highly visible display. If the user is relaxed, the review section can provide a display that includes detailed information. If the user is in a hurry, the review section can provide a display that gets straight to the point. By adjusting how the review is displayed according to the user's emotions, it becomes possible to display reviews in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the review section may be performed using AI or not using AI. For example, the review section can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0079] The review department can select the optimal review method by referring to past review history when a review is received. For example, the review department can suggest the optimal review method based on the style of reviews the user has made in the past. The review department can prioritize displaying specific evaluation items from the user's past review history. The review department can analyze the user's past review history and suggest the most efficient review method. In this way, by referring to past review history, the review department can provide the user with the optimal review method. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input the user's past review history into AI, and the AI ​​can select the optimal review method.

[0080] The review department can conduct reviews while considering the user's attribute information when a review is received. For example, the review department can suggest the most suitable review method based on the user's age and gender. The review department can prioritize displaying specific evaluation items based on the user's occupation and lifestyle. The review department can customize the content of the review based on the user's hobbies and interests. This allows for the provision of a more appropriate review method by considering the user's attribute information. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input the user's attribute information into the AI, which can then select the most suitable review method.

[0081] The review unit can estimate the user's emotions and determine the priority of reviews based on the estimated emotions. For example, if the user is stressed, the review unit can prioritize suggesting a simple and easy review method. If the user is relaxed, the review unit can prioritize suggesting a detailed review method. If the user is in a hurry, the review unit can prioritize suggesting a review method that can be completed quickly. In this way, by prioritizing reviews according to the user's emotions, the system can provide the user with the most suitable review method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the review unit may be performed using AI or not. For example, the review unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0082] The review department can select the most suitable review method when a review is received, taking into account the user's geographical location. For example, if the user is in a specific region, the review department can suggest a review method based on the local food culture. If the user is traveling, the review department can suggest a review method based on local ingredients. If the user is in a specific country, the review department can suggest a review method based on the food culture of that country. In this way, by considering geographical location, the review department can provide the user with the most suitable review method. Some or all of the above processing in the review department may be performed using AI or not. For example, the review department can input the user's geographical location information into the AI, which can then select the most suitable review method.

[0083] The review department can analyze the user's social media activity when a review is received and suggest a review method. For example, the review department can suggest the optimal review method based on reviews the user has shared on social media. The review department can suggest a review method based on the style of review accounts the user follows on social media. The review department can suggest a review method based on reviews the user has "liked" on social media. In this way, by analyzing social media activity, the review department can provide the user with the most suitable review method. Some or all of the above processing in the review department may be performed using AI or not. For example, the review department can input the user's social media activity into AI, and the AI ​​can select the most suitable review method.

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

[0085] The suggestion unit can estimate the user's emotions and adjust the suggested alternative recipes based on those emotions. For example, if the user is stressed, it can suggest a simple and easy alternative recipe. If the user is relaxed, it can suggest an alternative recipe with detailed cooking instructions. If the user is excited, it can suggest a visually appealing alternative recipe. By adjusting the suggested alternative recipes according to the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0086] The suggestion unit can analyze the user's past selection history and propose the most suitable alternative recipe. For example, it can suggest similar ingredients based on alternative ingredients the user has previously selected. It can suggest alternative recipes of a similar style based on cooking styles the user has previously preferred. It can suggest alternative recipes that exclude ingredients the user has previously avoided. In this way, by analyzing past selection history, the system can provide the most suitable alternative recipe for the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past selection history into AI, which can then select the most suitable alternative recipe.

[0087] The suggestion unit can estimate the user's emotions and adjust the difficulty of alternative recipes based on the estimated emotions. For example, if the user is stressed, it can suggest an easy and simple alternative recipe. If the user is relaxed, it can suggest a slightly more challenging but satisfying alternative recipe. If the user is excited, it can suggest a challenging alternative recipe. By adjusting the difficulty of alternative recipes according to the user's emotions, it becomes possible to provide the optimal suggestion for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0088] The suggestion unit can propose alternative recipes considering the user's current health condition. For example, if the user has diabetes, it can suggest a sugar-free alternative recipe. If the user has high blood pressure, it can suggest a low-sodium alternative recipe. If the user is on a specific diet, it can suggest an alternative recipe suitable for that diet. This allows for more appropriate meal suggestions by providing alternative recipes tailored to the user's health condition. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input information about the user's health condition into the AI, which can then select the most suitable alternative recipe.

[0089] The suggestion unit can estimate the user's emotions and adjust the presentation method of alternative recipes based on the estimated emotions. For example, if the user is relaxed, it can suggest an alternative recipe with a detailed explanation. If the user is in a hurry, it can suggest a concise and to-the-point alternative recipe. If the user is excited, it can suggest an alternative recipe with a visually appealing presentation. By adjusting the presentation method of alternative recipes according to the user's emotions, it becomes possible to make suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0090] The suggestion unit can propose alternative recipes while considering the user's geographical location. For example, if the user is in a specific region, it can suggest alternative recipes using ingredients readily available in that region. If the user is traveling, it can suggest alternative recipes using local ingredients. If the user is in a specific country, it can suggest alternative recipes based on the food culture of that country. In this way, by considering geographical location, it is possible to provide alternative recipes that are highly relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location into the AI, which can then select the most suitable alternative recipe.

[0091] The suggestion unit can estimate the user's emotions and adjust the order in which alternative recipes are suggested based on the estimated emotions. For example, if the user is stressed, a simple and easy alternative recipe may be suggested first. If the user is relaxed, an alternative recipe with detailed cooking instructions may be suggested first. If the user is excited, a visually appealing alternative recipe may be suggested first. By adjusting the order in which alternative recipes are suggested according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0092] The suggestion function can analyze a user's social media activity and suggest relevant alternative recipes. For example, it can suggest alternative recipes based on the names of dishes the user has shared on social media. It can also suggest alternative recipes based on recipes from cooking accounts the user follows on social media. It can also suggest alternative recipes based on the names of dishes the user has "liked" on social media. In this way, by analyzing social media activity, it can provide alternative recipes that are highly relevant to the user. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the user's social media activity into AI, which can then select the most suitable alternative recipe.

[0093] The suggestion unit can estimate the user's emotions and adjust the frequency of alternative recipe suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion frequency can be lowered, and if the user is relaxed, the suggestion frequency can be increased. If the user is excited, the suggestion frequency can be adjusted to suggest alternative recipes that will interest the user. By adjusting the frequency of alternative recipe suggestions according to the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0094] The suggestion unit can propose alternative recipes while considering the user's attribute information. For example, it can suggest the most suitable alternative recipe based on the user's age and gender. It can suggest alternative recipes using specific ingredients based on the user's occupation and lifestyle. It can suggest relevant alternative recipes based on the user's hobbies and interests. In this way, by considering the user's attribute information, more appropriate alternative recipes can be provided. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's attribute information into the AI, and the AI ​​can select the most suitable alternative recipe.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk accepts input of the dish name and allergy ingredients. For example, a user can enter "Shrimp Chawanmushi" and "Shellfish Allergy". Step 2: The suggestion department proposes alternative recipes based on the information received by the reception department. The suggestion department uses a generation AI to select appropriate alternative ingredients based on the dish name and allergens. For example, for "Shrimp Chawanmushi," it might suggest "changing the shrimp to scallops." This suggestion would also include notes that scallops are seafood like shrimp, have a flavor suitable for chawanmushi, have a similar texture to shrimp, and are cost-effective. Step 3: The review department receives customer reviews based on the alternative recipes proposed by the suggestion department. For example, customers can provide feedback on the taste and texture of "Scallop Chawanmushi." The review department then incorporates the feedback received into the suggestion department. This allows the suggestion department to utilize the feedback in future alternative recipe suggestions and continuously improve the quality of the alternative recipes.

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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] Each of the multiple elements described above, including the reception unit, proposal unit, and review unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user can input the name of the dish and allergens. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, where it proposes alternative recipes using a generation AI. The review unit is implemented by the control unit 46A of the smart device 14, where it receives customer reviews and reflects them in the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0102] As shown in Figure 3, the data processing system 210 includes a 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, proposal unit, and review unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to voice input the name of the dish and any allergens. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses a generation AI to suggest alternative recipes. The review unit is implemented, for example, by the control unit 46A of the smart glasses 214, which receives customer reviews and reflects them in the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, proposal unit, and review unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to voice input the name of the dish and any allergens. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses a generation AI to suggest alternative recipes. The review unit is implemented by, for example, the control unit 46A of the headset terminal 314, which receives customer reviews and reflects them in the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, proposal unit, and review unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to voice input the name of the dish and any allergens. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses a generation AI to suggest alternative recipes. The review unit is implemented by, for example, the control unit 46A of the robot 414, which receives customer reviews and reflects them in the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has 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 ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to 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] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A reception desk that accepts input of dish names and allergen information, A proposal unit proposes alternative recipes based on the information received by the aforementioned reception unit, The system includes a review unit that receives customer reviews based on alternative recipes proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Provide reasons for selecting alternative ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We select alternative ingredients considering the season, region, and cost of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned review section, We will receive feedback from customers and reflect it in the aforementioned proposal department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will make improvements to enhance the quality of the alternative recipes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting the dish name and allergens based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering dish names and allergen information, filtering is performed based on the user's current dietary restrictions and health status. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and, based on those emotions, determines the priority of the dish names and allergens to be entered. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering the dish name and allergen information, the system prioritizes inputting more relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter the dish name and allergen information, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, The system estimates the user's emotions and adjusts how alternative recipes are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When suggesting alternative recipes, adjust the level of detail based on the importance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When suggesting alternative recipes, different suggestion algorithms are applied depending on the category of the dish. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of alternative recipes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When suggesting alternative recipes, prioritize suggestions based on when the ingredients are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When suggesting alternative recipes, adjust the order of suggestions based on the relevance of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned review section, It estimates user sentiment and adjusts how reviews are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned review section, When receiving a review, the system will refer to past review history to select the most suitable review method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned review section, When receiving a review, we will conduct the review while taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned review section, It estimates user sentiment and prioritizes reviews based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned review section, When receiving reviews, the system selects the most suitable review method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned review section, When receiving reviews, we analyze the user's social media activity and suggest methods for submitting reviews. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of dish names and allergen information, A proposal unit proposes alternative recipes based on the information received by the aforementioned reception unit, The system includes a review unit that receives customer reviews based on alternative recipes proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Provide reasons for selecting alternative ingredients. The system according to feature 1.

3. The aforementioned proposal section is, We select alternative ingredients considering the season, region, and cost of the ingredients. The system according to feature 1.

4. The aforementioned review section, We will receive feedback from customers and reflect it in the aforementioned proposal department. The system according to feature 1.

5. The aforementioned proposal section is, We will make improvements to enhance the quality of the alternative recipes. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting the dish name and allergens based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering dish names and allergen information, filtering is performed based on the user's current dietary restrictions and health status. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and, based on those emotions, determines the priority of the dish names and allergens to be entered. The system according to feature 1.

10. The aforementioned reception unit is When entering the dish name and allergen information, the system prioritizes inputting more relevant information, taking into account the user's geographical location. The system according to feature 1.

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