system

Abstract

The system according to the embodiment aims to propose recipes that match the mood and preferences of the user. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a linking unit. The input unit inputs information about ingredients or seasonings. The analysis unit analyzes the information input by the input unit and suggests recipes that match the user's mood and preferences. The suggestion unit suggests recipes based on the results obtained by the analysis unit. The linking unit links the recipes suggested by the suggestion unit with a social networking site or an e-commerce site.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose recipes that are tailored to the user's mood and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to propose recipes that match the mood and preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, and a linking unit. The input unit inputs information about ingredients or seasonings. The analysis unit analyzes the information input by the input unit and suggests recipes that match the user's mood and preferences. The suggestion unit suggests recipes based on the results obtained by the analysis unit. The linking unit links the recipes suggested by the suggestion unit with social networking services or e-commerce sites. [Effects of the Invention]

[0007] The system according to the embodiment can suggest recipes that match the mood and preferences of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A recipe suggestion system according to an embodiment of the present invention proposes recipes tailored to a user's mood and preferences based on the ingredients and seasonings they own. The recipe suggestion system improves user convenience by allowing the user to input, analyze, and suggest recipes based on the ingredients and seasonings they own. For example, a user inputs the ingredients and seasonings they own. For example, a list of vegetables, meat, and seasonings in their refrigerator is input. This information is then entered into the system. The recipe suggestion system then analyzes the input information and suggests recipes tailored to the user's mood and preferences. For example, if a user inputs, "I want something light today," the system suggests light recipes. Furthermore, the recipe suggestion system also takes into account environmental factors such as region and temperature when making suggestions. For example, a user living in a cold region may be suggested hot recipes. In addition, in hot seasons, cold recipes may be suggested. By suggesting optimal recipes to users, the recipe suggestion system can reduce cooking effort and increase meal satisfaction. Furthermore, by linking with social networking sites and e-commerce sites, the recipe suggestion system allows users to easily share recipes and purchase the necessary ingredients. This will increase the number of system users and expand market share. As a result, the recipe suggestion system allows users to enjoy the best dishes that suit their mood and environment without wasting the ingredients they have. In addition, by linking with social networking sites and e-commerce sites, users can easily share recipes and purchase the ingredients they need, which will increase the number of system users and expand market share.

[0029] A recipe suggestion system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a linking unit. The input unit inputs information about ingredients and seasonings owned by a user. The information about ingredients and seasonings owned by a user includes, but is not limited to, the type and amount of ingredients, the type of seasoning, and how to use them. The input unit, for example, inputs a list of vegetables, meat, and seasonings the user has in the refrigerator. The input unit can also input information about ingredients and seasonings using voice input or image input. For example, the user can simply input "chicken, cabbage, carrots" by voice, and the ingredients are automatically added to the list. The analysis unit analyzes the information input by the input unit and suggests recipes tailored to the user's mood and preferences. The analysis unit can analyze the input information using, for example, an algorithm and suggest recipes tailored to the user's mood and preferences. The analysis unit can also suggest recipes taking into account environmental factors such as region and temperature. For example, a user living in a cold region can be suggested recipes for hot dishes. The suggestion unit suggests recipes based on the results obtained by the analysis unit. For example, if a user inputs "I want to eat something light today," the suggestion unit suggests a recipe for a light dish. The suggestion unit can also suggest recipes that match the user's mood or preferences. The linking unit links the recipes suggested by the suggestion unit with social networking services (SNS) or e-commerce sites. The linking unit enables, for example, the user to share the suggested recipe on the SNS or purchase the necessary ingredients on the e-commerce site. In this way, the recipe suggestion system according to the embodiment suggests recipes that match the user's mood or preferences based on the ingredients and seasonings the user has, and improves user convenience by linking with social networking services (SNS) or e-commerce sites.

[0030] The analysis unit can suggest recipes based on environmental factors such as region or temperature. The analysis unit suggests recipes taking into account environmental factors such as region characteristics, season, and temperature range. For example, the analysis unit suggests hot dish recipes to a user living in a cold region. The analysis unit can also suggest cold dish recipes in hot seasons. This allows more appropriate recipes to be suggested by taking into account environmental factors such as region and temperature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input region and temperature data into the generation AI and have the generation AI suggest optimal recipes.

[0031] The suggestion unit can suggest recipes that match the user's mood and preferences. For example, if the user inputs, "I want to eat something light today," the suggestion unit will suggest recipes for light dishes. The suggestion unit can also suggest recipes that match the user's mood and preferences. For example, if the user inputs, "I want to eat something rich today," the suggestion unit will suggest recipes for rich dishes. This improves user satisfaction by suggesting recipes that match the user's mood and preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's mood and preferences into the generation AI and have the generation AI suggest optimal recipes.

[0032] The linking unit can link with social networking sites and e-commerce sites to enable users to share recipes and purchase necessary ingredients. The linking unit, for example, enables users to share suggested recipes on social networking sites. For example, a user can share a suggested recipe on social networking sites and share it with friends and family. The linking unit also enables users to purchase ingredients needed for the suggested recipe on e-commerce sites. For example, a user can order ingredients needed for the suggested recipe on an e-commerce site and have them delivered. This allows users to share recipes and purchase necessary ingredients by linking with social networking sites and e-commerce sites. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input data of the suggested recipe into the generation AI and cause the generation AI to link with the social networking site and e-commerce site.

[0033] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit automatically displays ingredients and seasonings that the user has frequently input in the past as candidates. The input unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest ingredients and seasonings to be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and have the generation AI select the optimal input method.

[0034] When inputting ingredients and seasonings, the input unit can filter them based on the user's current meal plan and nutritional balance. For example, if the user is on a diet, the input unit can prioritize displaying low-calorie ingredients and seasonings. Furthermore, if the user is aiming to build muscle, the input unit can also prioritize displaying high-protein ingredients and seasonings. Furthermore, if the user has a specific allergy, the input unit can also prioritize displaying ingredients and seasonings that do not contain allergens. By filtering based on the user's meal plan and nutritional balance, more appropriate ingredients and seasonings can be suggested. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's meal plan and nutritional balance data into the generation AI and have the generation AI perform the filtering.

[0035] The input unit can select the optimal input means depending on the user's input method when inputting ingredients and seasonings. For example, the input unit can automatically add ingredients to a list by simply inputting "chicken, cabbage, carrots" by voice. The input unit can also automatically add ingredients to a list by having the user take a picture of the inside of the refrigerator with their smartphone camera through image analysis. The input unit can also simplify input when the user inputs ingredients using text by using a predictive conversion function. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0036] When inputting ingredients and seasonings, the input unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the input unit can prioritize displaying ingredients and seasonings commonly used in that area. Furthermore, if the user is traveling, the input unit can prioritize displaying local ingredients and seasonings. Furthermore, if the user wants to cook a dish from a specific area, the input unit can prioritize displaying ingredients and seasonings from that area. This makes it possible to provide more relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0037] The input unit can analyze the user's social media activity and input related information when inputting ingredients and seasonings. For example, the input unit can automatically add the ingredients and seasonings used to a list based on photos of dishes shared by the user on social media. The input unit can also analyze the user's social media posts to suggest related ingredients and seasonings. The input unit can also suggest related ingredients and seasonings based on the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0038] The input unit can customize the input method by reflecting the user's past feedback when inputting ingredients and seasonings. For example, the input unit suggests the optimal input method based on the user's past feedback on ingredients and seasonings. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest ingredients and seasonings to be used in a specific time period based on the user's past feedback. This makes it possible to provide a more appropriate input method by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of ingredients and seasonings. The analysis unit, for example, displays detailed analysis results for main ingredients and seasonings. The analysis unit can also display simple analysis results for supplementary ingredients and seasonings. The analysis unit can also display detailed analysis results for ingredients and seasonings in which the user is particularly interested. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of ingredients and seasonings. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data for ingredients and seasonings to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of ingredients and seasonings. For example, the analysis unit applies an analysis algorithm based on nutritional value to vegetables and fruits. The analysis unit can also apply an analysis algorithm based on cooking method to meat and fish. The analysis unit can also apply an analysis algorithm based on flavor and aroma to seasonings. In this way, by applying different analysis algorithms depending on the category of ingredients and seasonings, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of ingredients and seasonings to the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on the analysis results of recipes that the user previously preferred. The analysis unit can also improve the accuracy of the analysis based on the analysis results of ingredients and seasonings that the user previously avoided. The analysis unit can also improve the accuracy of the analysis based on the user's past feedback. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the analysis priority based on the submission date of the ingredients and seasonings. For example, the analysis unit prioritizes analysis of fresh ingredients and seasonings. The analysis unit can also prioritize analysis of ingredients and seasonings that are close to their expiration date. The analysis unit can also prioritize analysis of ingredients and seasonings that the user uses during a specific time period. This allows for more appropriate analysis results to be provided by determining the analysis priority based on the submission date of the ingredients and seasonings. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of ingredients and seasonings to the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the associations between ingredients and seasonings. For example, the analysis unit may prioritize analysis of main ingredients and seasonings, followed by analysis of supplementary ingredients and seasonings. The analysis unit can also prioritize analysis of ingredients and seasonings in which the user is particularly interested. The analysis unit can also adjust the order of analysis based on the combination of ingredients and seasonings. This allows for adjusting the order of analysis based on the associations between ingredients and seasonings, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input association data between ingredients and seasonings into the generation AI and have the generation AI adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can display the analysis results in simple language. Furthermore, if the user is an intermediate user, the analysis unit can display the analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can display the analysis results using detailed technical terms. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0045] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the recipe. For example, the suggestion unit makes detailed suggestions for main recipes. The suggestion unit can also make simple suggestions for auxiliary recipes. The suggestion unit can also make detailed suggestions for recipes in which the user is particularly interested. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the recipe. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input recipe importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0046] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the recipe. For example, the suggestion unit can apply a suggestion algorithm based on Japanese cooking methods to Japanese cuisine. The suggestion unit can also apply a suggestion algorithm based on Western cooking methods to Western cuisine. The suggestion unit can also apply a suggestion algorithm based on Chinese cooking methods to Chinese cuisine. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the recipe category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input recipe category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0047] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can improve the accuracy of the suggestion based on, for example, the suggestion results of recipes that the user liked in the past. The suggestion unit can also improve the accuracy of the suggestion based on the suggestion results of recipes that the user avoided in the past. The suggestion unit can also improve the accuracy of the suggestion based on the user's past feedback. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0048] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of recipe submission. For example, the suggestion unit can prioritize recipes that use fresh ingredients. The suggestion unit can also prioritize recipes that use ingredients with an approaching expiration date. The suggestion unit can also suggest optimal recipes based on ingredients that the user will use during a specific time period. This enables more appropriate suggestions by determining the priority of suggestions based on the time of recipe submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input recipe submission time data into the generation AI and have the generation AI determine the priority of the suggestions.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of recipes when making suggestions. For example, the suggestion unit can prioritize suggesting main recipes, followed by supporting recipes. The suggestion unit can also prioritize suggesting recipes in which the user is particularly interested. The suggestion unit can also adjust the order of suggestions based on combinations of recipes. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of recipes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input recipe relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0050] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can make the proposal using simple language. If the user is an intermediate user, the suggestion unit can also make the proposal using appropriate technical terminology. If the user is an advanced user, the suggestion unit can also make the proposal using detailed technical terminology. This enables more appropriate proposals by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the proposal.

[0051] At the time of collaboration, the collaboration unit can analyze the user's past collaboration history and select the optimal collaboration method. The collaboration unit, for example, suggests the optimal collaboration method based on collaboration methods used by the user in the past. The collaboration unit can also suggest the most efficient collaboration method based on the user's past collaboration history. The collaboration unit can also suggest the optimal collaboration method based on the user's past feedback. In this way, the optimal collaboration method can be provided by analyzing the user's past collaboration history. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past collaboration history data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0052] The collaboration unit can customize the collaboration method based on the user's current activity status during collaboration. For example, if the user is busy, the collaboration unit can suggest a simple collaboration method. Furthermore, if the user is relaxed, the collaboration unit can suggest a detailed collaboration method. Furthermore, if the user is performing a specific activity, the collaboration unit can suggest a collaboration method that is optimal for that activity. This enables more appropriate collaboration by customizing the collaboration method based on the user's current activity status. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's current activity status data into the generation AI and cause the generation AI to customize the collaboration method.

[0053] The collaboration unit can improve the collaboration method by reflecting user feedback during collaboration. The collaboration unit improves the collaboration method, for example, based on feedback provided by the user in the past. The collaboration unit can also reflect user feedback in real time and adjust the collaboration method. The collaboration unit can also analyze user feedback and propose an optimal collaboration method. In this way, the collaboration method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input user feedback data into the generation AI and cause the generation AI to improve the collaboration method.

[0054] At the time of collaboration, the collaboration unit can select the optimal collaboration method by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collaboration unit can suggest a collaboration method commonly used in that area. Furthermore, if the user is traveling, the collaboration unit can also suggest a local collaboration method. Furthermore, if the user is engaged in an activity in a specific area, the collaboration unit can suggest the optimal collaboration method for that area. In this way, the optimal collaboration method can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0055] At the time of linking, the linking unit can analyze the user's social media activity and suggest a means of linking. For example, the linking unit can suggest an optimal linking method based on information shared by the user on social media. The linking unit can also analyze the content of the user's social media posts and suggest a related linking method. The linking unit can also suggest an optimal linking method by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide an optimal linking method. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest a linking method.

[0056] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. The collaboration unit customizes the collaboration method based on, for example, feedback provided by the user in the past. The collaboration unit can also reflect the user's feedback in real time and adjust the collaboration method. The collaboration unit can also analyze the user's feedback and propose an optimal collaboration method. In this way, the collaboration method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collaboration method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The recipe suggestion system can further include a health management unit that monitors the user's health condition. The health management unit acquires the user's health data (e.g., blood pressure, blood sugar level, weight, etc.) and provides it to the analysis unit. Based on this health data, the analysis unit can suggest recipes that are suitable for the user's health condition. For example, if the user has high blood pressure, it can suggest low-salt recipes. Also, if the user has diabetes, it can suggest low-carbohydrate recipes. This makes it possible to support health management by suggesting recipes that suit the user's health condition.

[0059] The recipe suggestion system can further include a history management unit that records the user's meal history. The history management unit records recipes and meal history created by the user in the past and provides this to the analysis unit. The analysis unit analyzes the user's preferences and meal patterns based on this history data and can suggest more personalized recipes. For example, it can prioritize suggesting recipes that the user has frequently created in the past. It can also suggest recipes that do not include ingredients that the user has avoided in the past. This makes it possible to suggest recipes based on the user's meal history.

[0060] The recipe suggestion system can further include a storage management unit that manages the storage status of the user's ingredients. The storage management unit records the storage status (e.g., refrigerated, frozen, room temperature, etc.) of ingredients owned by the user and provides this to the analysis unit. Based on this storage status data, the analysis unit can suggest recipes suitable for the storage method. For example, it can suggest recipes that use frozen ingredients. It can also suggest recipes that prioritize the use of room temperature ingredients. This makes it possible to suggest recipes that suit the storage status of ingredients.

[0061] The recipe suggestion system can further include a purchase management unit that manages the user's ingredient purchase history. The purchase management unit records the history of ingredients purchased by the user in the past and provides this to the analysis unit. Based on this purchase history data, the analysis unit can suggest recipes that use ingredients that the user frequently purchases. For example, the analysis unit can suggest recipes that prioritize ingredients that the user frequently purchases. It can also suggest recipes that use ingredients that the user previously purchased but did not use. This makes it possible to suggest recipes based on the user's purchase history.

[0062] The recipe suggestion system can further include an expiration date management unit that manages the expiration dates of the user's ingredients. The expiration date management unit records the expiration dates of ingredients owned by the user and provides this information to the analysis unit. Based on this expiration date data, the analysis unit can suggest recipes that prioritize the use of ingredients with an approaching expiration date. For example, it can suggest recipes that use ingredients with an approaching expiration date. It can also warn users not to use ingredients that have passed their expiration date. This reduces food waste and enables efficient use of ingredients.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The input unit inputs information about ingredients and seasonings that the user has. For example, the user can input a list of vegetables, meat, and seasonings that are in the refrigerator. It is also possible to input information about ingredients and seasonings using voice input or image input. For example, the user can simply input "chicken, cabbage, carrots" by voice, and the ingredients will be automatically added to the list. Step 2: The analysis unit analyzes the information entered by the input unit and suggests recipes that match the user's mood and preferences. The analysis unit can also analyze the entered information using an algorithm and suggest recipes taking into account environmental factors such as region and temperature. For example, a user living in a cold region would be suggested recipes for hot dishes. Step 3: The suggestion unit suggests recipes based on the results obtained by the analysis unit. For example, if the user inputs "I want to eat something light today," the suggestion unit will suggest recipes for light dishes. The suggestion unit can also suggest recipes that match the user's mood and preferences. Step 4: The linking unit links the recipes suggested by the suggestion unit with social networking sites and e-commerce sites, allowing users to share the suggested recipes on social networking sites and purchase the necessary ingredients on e-commerce sites.

[0065] (Example 2) A recipe suggestion system according to an embodiment of the present invention proposes recipes tailored to a user's mood and preferences based on the ingredients and seasonings they own. The recipe suggestion system improves user convenience by allowing the user to input, analyze, and suggest recipes based on the ingredients and seasonings they own. For example, a user inputs the ingredients and seasonings they own. For example, a list of vegetables, meat, and seasonings in their refrigerator is input. This information is then entered into the system. The recipe suggestion system then analyzes the input information and suggests recipes tailored to the user's mood and preferences. For example, if a user inputs, "I want something light today," the system suggests light recipes. Furthermore, the recipe suggestion system also takes into account environmental factors such as region and temperature when making suggestions. For example, a user living in a cold region may be suggested hot recipes. In addition, in hot seasons, cold recipes may be suggested. By suggesting optimal recipes to users, the recipe suggestion system can reduce cooking effort and increase meal satisfaction. Furthermore, by linking with social networking sites and e-commerce sites, the recipe suggestion system allows users to easily share recipes and purchase the necessary ingredients. This will increase the number of system users and expand market share. As a result, the recipe suggestion system allows users to enjoy the best dishes that suit their mood and environment without wasting the ingredients they have. In addition, by linking with social networking sites and e-commerce sites, users can easily share recipes and purchase the ingredients they need, which will increase the number of system users and expand market share.

[0066] A recipe suggestion system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a linking unit. The input unit inputs information about ingredients and seasonings owned by a user. The information about ingredients and seasonings owned by a user includes, but is not limited to, the type and amount of ingredients, the type of seasoning, and how to use them. The input unit, for example, inputs a list of vegetables, meat, and seasonings the user has in the refrigerator. The input unit can also input information about ingredients and seasonings using voice input or image input. For example, the user can simply input "chicken, cabbage, carrots" by voice, and the ingredients are automatically added to the list. The analysis unit analyzes the information input by the input unit and suggests recipes tailored to the user's mood and preferences. The analysis unit can analyze the input information using, for example, an algorithm and suggest recipes tailored to the user's mood and preferences. The analysis unit can also suggest recipes taking into account environmental factors such as region and temperature. For example, a user living in a cold region can be suggested recipes for hot dishes. The suggestion unit suggests recipes based on the results obtained by the analysis unit. For example, if a user inputs "I want to eat something light today," the suggestion unit suggests a recipe for a light dish. The suggestion unit can also suggest recipes that match the user's mood or preferences. The linking unit links the recipes suggested by the suggestion unit with social networking services (SNS) or e-commerce sites. The linking unit enables, for example, the user to share the suggested recipe on the SNS or purchase the necessary ingredients on the e-commerce site. In this way, the recipe suggestion system according to the embodiment suggests recipes that match the user's mood or preferences based on the ingredients and seasonings the user has, and improves user convenience by linking with social networking services (SNS) or e-commerce sites.

[0067] The analysis unit can suggest recipes based on environmental factors such as region or temperature. The analysis unit suggests recipes taking into account environmental factors such as region characteristics, season, and temperature range. For example, the analysis unit suggests hot dish recipes to a user living in a cold region. The analysis unit can also suggest cold dish recipes in hot seasons. This allows more appropriate recipes to be suggested by taking into account environmental factors such as region and temperature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input region and temperature data into the generation AI and have the generation AI suggest optimal recipes.

[0068] The suggestion unit can suggest recipes that match the user's mood and preferences. For example, if the user inputs, "I want to eat something light today," the suggestion unit will suggest recipes for light dishes. The suggestion unit can also suggest recipes that match the user's mood and preferences. For example, if the user inputs, "I want to eat something rich today," the suggestion unit will suggest recipes for rich dishes. This improves user satisfaction by suggesting recipes that match the user's mood and preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's mood and preferences into the generation AI and have the generation AI suggest optimal recipes.

[0069] The linking unit can link with social networking sites and e-commerce sites to enable users to share recipes and purchase necessary ingredients. The linking unit, for example, enables users to share suggested recipes on social networking sites. For example, a user can share a suggested recipe on social networking sites and share it with friends and family. The linking unit also enables users to purchase ingredients needed for the suggested recipe on e-commerce sites. For example, a user can order ingredients needed for the suggested recipe on an e-commerce site and have them delivered. This allows users to share recipes and purchase necessary ingredients by linking with social networking sites and e-commerce sites. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input data of the suggested recipe into the generation AI and cause the generation AI to link with the social networking site and e-commerce site.

[0070] The input unit can estimate the user's emotions and adjust the timing of inputting ingredients and seasonings based on the estimated user emotions. For example, when the user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable quick input of ingredients and seasonings. This allows for more appropriate input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, an AI, or without an AI. For example, the input unit can input the user's emotion data into the generation AI and have the generation AI adjust the input timing.

[0071] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit automatically displays ingredients and seasonings that the user has frequently input in the past as candidates. The input unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest ingredients and seasonings to be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI and have the generation AI select the optimal input method.

[0072] When inputting ingredients and seasonings, the input unit can filter them based on the user's current meal plan and nutritional balance. For example, if the user is on a diet, the input unit can prioritize displaying low-calorie ingredients and seasonings. Furthermore, if the user is aiming to build muscle, the input unit can also prioritize displaying high-protein ingredients and seasonings. Furthermore, if the user has a specific allergy, the input unit can also prioritize displaying ingredients and seasonings that do not contain allergens. By filtering based on the user's meal plan and nutritional balance, more appropriate ingredients and seasonings can be suggested. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's meal plan and nutritional balance data into the generation AI and have the generation AI perform the filtering.

[0073] The input unit can select the optimal input means depending on the user's input method when inputting ingredients and seasonings. For example, the input unit can automatically add ingredients to a list by simply inputting "chicken, cabbage, carrots" by voice. The input unit can also automatically add ingredients to a list by having the user take a picture of the inside of the refrigerator with their smartphone camera through image analysis. The input unit can also simplify input when the user inputs ingredients using text by using a predictive conversion function. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0074] The input unit can estimate the user's emotions and prioritize the ingredients and seasonings to be input based on the estimated user's emotions. For example, if the user is tired, the input unit can prioritize displaying ingredients and seasonings that are easy to cook. Furthermore, if the user is feeling energetic, the input unit can prioritize displaying ingredients and seasonings that are suitable for elaborate dishes. Furthermore, if the user is feeling stressed, the input unit can prioritize displaying ingredients and seasonings that have a relaxing effect. This enables more appropriate suggestions by prioritizing the ingredients and seasonings to be input based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, an AI. For example, the input unit can input the user's emotion data to the generation AI and have the generation AI determine the priorities of the ingredients and seasonings to be input.

[0075] When inputting ingredients and seasonings, the input unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the input unit can prioritize displaying ingredients and seasonings commonly used in that area. Furthermore, if the user is traveling, the input unit can prioritize displaying local ingredients and seasonings. Furthermore, if the user wants to cook a dish from a specific area, the input unit can prioritize displaying ingredients and seasonings from that area. This makes it possible to provide more relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0076] The input unit can analyze the user's social media activity and input related information when inputting ingredients and seasonings. For example, the input unit can automatically add the ingredients and seasonings used to a list based on photos of dishes shared by the user on social media. The input unit can also analyze the user's social media posts to suggest related ingredients and seasonings. The input unit can also suggest related ingredients and seasonings based on the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0077] The input unit can customize the input method by reflecting the user's past feedback when inputting ingredients and seasonings. For example, the input unit suggests the optimal input method based on the user's past feedback on ingredients and seasonings. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest ingredients and seasonings to be used in a specific time period based on the user's past feedback. This makes it possible to provide a more appropriate input method by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display analysis results that proceed at a leisurely pace. If the user is in a hurry, the analysis unit can also display analysis results that emphasize the shortest route. If the user is excited, the analysis unit can also display analysis results with visually stimulating effects. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of ingredients and seasonings. The analysis unit, for example, displays detailed analysis results for main ingredients and seasonings. The analysis unit can also display simple analysis results for supplementary ingredients and seasonings. The analysis unit can also display detailed analysis results for ingredients and seasonings in which the user is particularly interested. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of ingredients and seasonings. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data for ingredients and seasonings to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of ingredients and seasonings. For example, the analysis unit applies an analysis algorithm based on nutritional value to vegetables and fruits. The analysis unit can also apply an analysis algorithm based on cooking method to meat and fish. The analysis unit can also apply an analysis algorithm based on flavor and aroma to seasonings. In this way, by applying different analysis algorithms depending on the category of ingredients and seasonings, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of ingredients and seasonings to the generation AI and cause the generation AI to apply different analysis algorithms.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on the analysis results of recipes that the user previously preferred. The analysis unit can also improve the accuracy of the analysis based on the analysis results of ingredients and seasonings that the user previously avoided. The analysis unit can also improve the accuracy of the analysis based on the user's past feedback. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can display a short, to-the-point analysis result. Alternatively, if the user is relaxed, the analysis unit can display a longer analysis result with detailed explanations. Alternatively, if the user is excited, the analysis unit can display an analysis result with visually stimulating effects. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the analysis priority based on the submission date of the ingredients and seasonings. For example, the analysis unit prioritizes analysis of fresh ingredients and seasonings. The analysis unit can also prioritize analysis of ingredients and seasonings that are close to their expiration date. The analysis unit can also prioritize analysis of ingredients and seasonings that the user uses during a specific time period. This allows for more appropriate analysis results to be provided by determining the analysis priority based on the submission date of the ingredients and seasonings. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of ingredients and seasonings to the generation AI and have the generation AI determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the associations between ingredients and seasonings. For example, the analysis unit may prioritize analysis of main ingredients and seasonings, followed by analysis of supplementary ingredients and seasonings. The analysis unit can also prioritize analysis of ingredients and seasonings in which the user is particularly interested. The analysis unit can also adjust the order of analysis based on the combination of ingredients and seasonings. This allows for adjusting the order of analysis based on the associations between ingredients and seasonings, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input association data between ingredients and seasonings into the generation AI and have the generation AI adjust the order of analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can display the analysis results in simple language. Furthermore, if the user is an intermediate user, the analysis unit can display the analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can display the analysis results using detailed technical terms. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0086] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions to proceed at a leisurely pace. If the user is in a hurry, the suggestion unit can also make suggestions that emphasize the shortest route. If the user is excited, the suggestion unit can also make suggestions that add visually stimulating effects. This enables more appropriate suggestions by adjusting the way the suggestions are expressed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0087] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the recipe. For example, the suggestion unit makes detailed suggestions for main recipes. The suggestion unit can also make simple suggestions for auxiliary recipes. The suggestion unit can also make detailed suggestions for recipes in which the user is particularly interested. This allows for more appropriate suggestions by adjusting the level of detail of the suggestion based on the importance of the recipe. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input recipe importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0088] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the recipe. For example, the suggestion unit can apply a suggestion algorithm based on Japanese cooking methods to Japanese cuisine. The suggestion unit can also apply a suggestion algorithm based on Western cooking methods to Western cuisine. The suggestion unit can also apply a suggestion algorithm based on Chinese cooking methods to Chinese cuisine. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the recipe category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input recipe category data into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0089] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can improve the accuracy of the suggestion based on, for example, the suggestion results of recipes that the user liked in the past. The suggestion unit can also improve the accuracy of the suggestion based on the suggestion results of recipes that the user avoided in the past. The suggestion unit can also improve the accuracy of the suggestion based on the user's past feedback. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0091] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of recipe submission. For example, the suggestion unit can prioritize recipes that use fresh ingredients. The suggestion unit can also prioritize recipes that use ingredients with an approaching expiration date. The suggestion unit can also suggest optimal recipes based on ingredients that the user will use during a specific time period. This enables more appropriate suggestions by determining the priority of suggestions based on the time of recipe submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input recipe submission time data into the generation AI and have the generation AI determine the priority of the suggestions.

[0092] The suggestion unit can adjust the order of suggestions based on the relevance of recipes when making suggestions. For example, the suggestion unit can prioritize suggesting main recipes, followed by supporting recipes. The suggestion unit can also prioritize suggesting recipes in which the user is particularly interested. The suggestion unit can also adjust the order of suggestions based on combinations of recipes. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of recipes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input recipe relevance data into a generation AI and cause the generation AI to adjust the order of suggestions.

[0093] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a beginner, the suggestion unit can make the proposal using simple language. If the user is an intermediate user, the suggestion unit can also make the proposal using appropriate technical terminology. If the user is an advanced user, the suggestion unit can also make the proposal using detailed technical terminology. This enables more appropriate proposals by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the proposal.

[0094] The collaboration unit can estimate the user's emotions and adjust the collaboration method based on the estimated user's emotions. For example, if the user is relaxed, the collaboration unit can perform collaboration at a leisurely pace. Furthermore, if the user is in a hurry, the collaboration unit can also perform collaboration quickly. Furthermore, if the user is excited, the collaboration unit can perform collaboration with a visually stimulating effect. This allows for more appropriate collaboration by adjusting the collaboration method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collaboration unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the collaboration method.

[0095] At the time of collaboration, the collaboration unit can analyze the user's past collaboration history and select the optimal collaboration method. The collaboration unit, for example, suggests the optimal collaboration method based on collaboration methods used by the user in the past. The collaboration unit can also suggest the most efficient collaboration method based on the user's past collaboration history. The collaboration unit can also suggest the optimal collaboration method based on the user's past feedback. In this way, the optimal collaboration method can be provided by analyzing the user's past collaboration history. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's past collaboration history data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0096] The collaboration unit can customize the collaboration method based on the user's current activity status during collaboration. For example, if the user is busy, the collaboration unit can suggest a simple collaboration method. Furthermore, if the user is relaxed, the collaboration unit can suggest a detailed collaboration method. Furthermore, if the user is performing a specific activity, the collaboration unit can suggest a collaboration method that is optimal for that activity. This enables more appropriate collaboration by customizing the collaboration method based on the user's current activity status. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's current activity status data into the generation AI and cause the generation AI to customize the collaboration method.

[0097] The collaboration unit can improve the collaboration method by reflecting user feedback during collaboration. The collaboration unit improves the collaboration method, for example, based on feedback provided by the user in the past. The collaboration unit can also reflect user feedback in real time and adjust the collaboration method. The collaboration unit can also analyze user feedback and propose an optimal collaboration method. In this way, the collaboration method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input user feedback data into the generation AI and cause the generation AI to improve the collaboration method.

[0098] The collaboration unit can estimate the user's emotions and determine collaboration priorities based on the estimated user emotions. For example, if the user is in a hurry, the collaboration unit can perform collaboration quickly. Furthermore, if the user is relaxed, the collaboration unit can perform collaboration at a leisurely pace. Furthermore, if the user is excited, the collaboration unit can perform collaboration with visually stimulating effects. This enables more appropriate collaboration by determining collaboration priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collaboration unit can input the user's emotion data into the generation AI and have the generation AI determine the collaboration priorities.

[0099] At the time of collaboration, the collaboration unit can select the optimal collaboration method by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collaboration unit can suggest a collaboration method commonly used in that area. Furthermore, if the user is traveling, the collaboration unit can also suggest a local collaboration method. Furthermore, if the user is engaged in an activity in a specific area, the collaboration unit can suggest the optimal collaboration method for that area. In this way, the optimal collaboration method can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0100] At the time of linking, the linking unit can analyze the user's social media activity and suggest a means of linking. For example, the linking unit can suggest an optimal linking method based on information shared by the user on social media. The linking unit can also analyze the content of the user's social media posts and suggest a related linking method. The linking unit can also suggest an optimal linking method by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide an optimal linking method. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest a linking method.

[0101] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. The collaboration unit customizes the collaboration method based on, for example, feedback provided by the user in the past. The collaboration unit can also reflect the user's feedback in real time and adjust the collaboration method. The collaboration unit can also analyze the user's feedback and propose an optimal collaboration method. In this way, the collaboration method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collaboration method. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, suggestion unit, and linking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input information about ingredients and seasonings owned by the user using the reception device 38 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests recipes based on the analysis results. The linking unit links, for example, with an SNS or e-commerce site using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described input unit, analysis unit, suggestion unit, and linking unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can use the microphone 238 of the smart glasses 214 to input information about ingredients and seasonings that the user has by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests recipes based on the analysis results. The linking unit links with SNSs and e-commerce sites using, for example, the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, suggestion unit, and linking unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can use the microphone 238 of the headset terminal 314 to input information about ingredients and seasonings that the user has by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes based on the analysis results. The linking unit links with SNSs and e-commerce sites, for example, by using the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, suggestion unit, and linking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can use the microphone 238 of the robot 414 to input information about ingredients and seasonings that the user has by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes based on the analysis results. The linking unit links, for example, with SNS or e-commerce sites using the communication I / F 44 of the robot 414.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The recipe suggestion system can further include a health management unit that monitors the user's health condition. The health management unit acquires the user's health data (e.g., blood pressure, blood sugar level, weight, etc.) and provides it to the analysis unit. Based on this health data, the analysis unit can suggest recipes that are suitable for the user's health condition. For example, if the user has high blood pressure, it can suggest low-salt recipes. Also, if the user has diabetes, it can suggest low-carbohydrate recipes. This makes it possible to support health management by suggesting recipes that suit the user's health condition.

[0104] The recipe suggestion system can further include a history management unit that records the user's meal history. The history management unit records recipes and meal history created by the user in the past and provides this to the analysis unit. The analysis unit analyzes the user's preferences and meal patterns based on this history data and can suggest more personalized recipes. For example, it can prioritize suggesting recipes that the user has frequently created in the past. It can also suggest recipes that do not include ingredients that the user has avoided in the past. This makes it possible to suggest recipes based on the user's meal history.

[0105] The recipe suggestion system can further include a storage management unit that manages the storage status of the user's ingredients. The storage management unit records the storage status (e.g., refrigerated, frozen, room temperature, etc.) of ingredients owned by the user and provides this to the analysis unit. Based on this storage status data, the analysis unit can suggest recipes suitable for the storage method. For example, it can suggest recipes that use frozen ingredients. It can also suggest recipes that prioritize the use of room temperature ingredients. This makes it possible to suggest recipes that suit the storage status of ingredients.

[0106] The recipe suggestion system can further include a purchase management unit that manages the user's ingredient purchase history. The purchase management unit records the history of ingredients purchased by the user in the past and provides this to the analysis unit. Based on this purchase history data, the analysis unit can suggest recipes that use ingredients that the user frequently purchases. For example, the analysis unit can suggest recipes that prioritize ingredients that the user frequently purchases. It can also suggest recipes that use ingredients that the user previously purchased but did not use. This makes it possible to suggest recipes based on the user's purchase history.

[0107] The recipe suggestion system can further include an expiration date management unit that manages the expiration dates of the user's ingredients. The expiration date management unit records the expiration dates of ingredients owned by the user and provides this information to the analysis unit. Based on this expiration date data, the analysis unit can suggest recipes that prioritize the use of ingredients with an approaching expiration date. For example, it can suggest recipes that use ingredients with an approaching expiration date. It can also warn users not to use ingredients that have passed their expiration date. This reduces food waste and enables efficient use of ingredients.

[0108] The recipe suggestion system can also estimate the user's emotions and adjust the difficulty of the recipes based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest simple and easy recipes. If the user is relaxed, the system can suggest more elaborate recipes. Furthermore, if the user is excited, the system can suggest more challenging recipes. This allows the system to suggest more appropriate recipes by adjusting the difficulty of the recipes according to the user's emotions.

[0109] The recipe recommendation system can also estimate the user's emotions and adjust the recipe presentation method based on the estimated emotions. For example, if the user is relaxed, the system can display recipes at a leisurely pace. If the user is in a hurry, the system can display recipes that are concise and to the point. Furthermore, if the user is excited, the system can display recipes with visually stimulating effects. This allows the system to adjust the recipe presentation method according to the user's emotions, thereby enabling more appropriate recipe recommendations.

[0110] The recipe suggestion system can also estimate the user's emotions and adjust the ingredients of the recipe based on the estimated emotions. For example, if the user is tired, it can suggest recipes using easily available ingredients. If the user is energetic, it can suggest recipes using special ingredients. Furthermore, if the user is stressed, it can suggest recipes using ingredients that have a relaxing effect. This allows for more appropriate recipe suggestions by adjusting the ingredients of the recipe according to the user's emotions.

[0111] The recipe recommendation system can also estimate the user's emotions and adjust the cooking time of the recipe based on the estimated emotions. For example, if the user is in a hurry, the system can suggest recipes that can be made in a short time. If the user is relaxed, the system can suggest recipes that take more time to make. Furthermore, if the user is excited, the system can suggest recipes with challenging cooking times. This allows for more appropriate recipe suggestions by adjusting the cooking time of the recipe according to the user's emotions.

[0112] The recipe suggestion system can also estimate the user's emotions and adjust recipe portion sizes based on the estimated emotions. For example, if the user is feeling stressed, it can suggest recipes that are satisfying with small portions. If the user is relaxed, it can also suggest recipes with normal portion sizes. Furthermore, if the user is excited, it can suggest recipes that require large quantities. This allows for more appropriate recipe suggestions by adjusting recipe portion sizes according to the user's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The input unit inputs information about ingredients and seasonings that the user has. For example, the user can input a list of vegetables, meat, and seasonings that are in the refrigerator. It is also possible to input information about ingredients and seasonings using voice input or image input. For example, the user can simply input "chicken, cabbage, carrots" by voice, and the ingredients will be automatically added to the list. Step 2: The analysis unit analyzes the information entered by the input unit and suggests recipes that match the user's mood and preferences. The analysis unit can also analyze the entered information using an algorithm and suggest recipes taking into account environmental factors such as region and temperature. For example, a user living in a cold region would be suggested recipes for hot dishes. Step 3: The suggestion unit suggests recipes based on the results obtained by the analysis unit. For example, if the user inputs "I want to eat something light today," the suggestion unit will suggest recipes for light dishes. The suggestion unit can also suggest recipes that match the user's mood and preferences. Step 4: The linking unit links the recipes suggested by the suggestion unit with social networking sites and e-commerce sites, allowing users to share the suggested recipes on social networking sites and purchase the necessary ingredients on e-commerce sites.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting information about ingredients or seasonings; an analysis unit that analyzes the information input by the input unit and suggests recipes that match the user's mood and preferences; a suggestion unit that proposes a recipe based on the results obtained by the analysis unit; a linking unit that links the recipes proposed by the suggestion unit with an SNS or an e-commerce site. A system characterized by:

2. The analysis unit Suggest recipes based on environmental factors like location or temperature 2. The system of claim 1.

3. The proposal unit Suggest recipes that match the user's mood and preferences 2. The system of claim 1.

4. The linking unit is It will link with social media and e-commerce sites, allowing users to share recipes and purchase the ingredients they need.

2. The system of claim 1.

5. The input unit Estimate the user's emotions and adjust the timing of adding ingredients and seasonings based on the estimated emotions.

2. The system of claim 1.

6. The input unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

7. The input unit When entering ingredients and seasonings, filtering is performed based on the user's current meal plan and nutritional balance.

2. The system of claim 1.

8. The input unit When inputting ingredients and seasonings, select the most appropriate input method according to the user's input method.

2. The system of claim 1.

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