system

Abstract

The system according to the embodiment aims to suggest ingredients and recipes by effectively utilizing the contents of the refrigerator while taking into consideration the preferences and allergy information of family members. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a refrigerator information input unit, a suggestion unit, and a shopping list suggestion unit. The input unit inputs family member preferences and allergy information. The analysis unit analyzes the information input by the input unit. The refrigerator information input unit inputs the contents of the refrigerator. The analysis unit analyzes the information input by the refrigerator information input unit. The suggestion unit suggests appropriate ingredients and recipes based on the information analyzed by the analysis unit. The shopping list suggestion unit suggests a list of ingredients to purchase based on the recipe suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately take into account family preferences and allergy information, while effectively utilizing the contents of the refrigerator to suggest ingredients and recipes, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest ingredients and recipes by effectively utilizing the contents of the refrigerator while taking into consideration the preferences and allergy information of family members. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a refrigerator information input unit, a suggestion unit, and a shopping list suggestion unit. The input unit inputs family member preferences and allergy information. The analysis unit analyzes the information input by the input unit. The refrigerator information input unit inputs the contents of the refrigerator. The analysis unit analyzes the information input by the refrigerator information input unit. The suggestion unit suggests appropriate ingredients and recipes based on the information analyzed by the analysis unit. The shopping list suggestion unit suggests a list of ingredients to purchase based on the recipe suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest ingredients and recipes by effectively utilizing the contents of the refrigerator while taking into consideration the preferences and allergy information of family members. [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 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 cooking assistant system according to an embodiment of the present invention utilizes the contents of a refrigerator and family preferences and allergy information to suggest ingredients and recipes to purchase. The cooking assistant system allows users to input their family's preferences and profiles through an app or platform, and analyzes this information to suggest optimal ingredients and recipe options. This not only effectively utilizes household ingredients, reduces food waste, but also enables meal planning tailored to individual preferences and allergies. For example, a user inputs family preferences and allergy information through an app or platform. For example, if one family member has a dairy allergy, the user can input this information. The family also inputs their preferences, such as a preference for spicy food or a preference for specific ingredients. Next, the user inputs the contents of the refrigerator into the app. For example, the user inputs information about the vegetables, meat, and seasonings in the refrigerator. This allows the app to grasp the current contents of the refrigerator. Based on this information, the app suggests optimal ingredients and recipe options. For example, the app suggests recipes using chicken and vegetables in the refrigerator. It also suggests dairy-free recipes, taking into account the family's preferences and allergy information. The app also suggests a list of ingredients to purchase. For example, it lists ingredients missing from the refrigerator and ingredients needed for recipes. This allows users to shop efficiently. This system makes it possible to effectively utilize ingredients available at home and reduce food waste. It also enables meal planning tailored to individual preferences and allergies. For example, a family member with a dairy allergy can enjoy meals without worry by suggesting dairy-free recipes. This cooking assistant can be a partner for busy households, supporting meal preparation and nutritional management. For example, it can suggest recipes that take into account the contents of the refrigerator and the preferences of the family, so that a parent who is busy at work can start cooking immediately after returning home. This makes meal preparation go smoothly and ensures meals that satisfy the whole family. The cooking assistant system analyzes family preferences, allergy information, and the contents of the refrigerator to suggest the optimal ingredients and recipes, reducing food waste and enabling meal planning tailored to individual preferences and allergies.

[0029] A cooking assistant system according to an embodiment includes an input unit, a refrigerator information input unit, an analysis unit, a suggestion unit, and a shopping list suggestion unit. The input unit inputs family preferences and allergy information. For example, the input unit allows a user to input family preferences and allergy information through an app or platform. The input unit can also input family preferences such as a preference for spicy food or a preference for specific ingredients. The refrigerator information input unit inputs information about ingredients in the refrigerator. For example, the refrigerator information input unit can input information about vegetables, meat, seasonings, etc. in the refrigerator. The refrigerator information input unit can also input information about ingredients in the refrigerator to an app. The analysis unit analyzes the information input by the input unit and the refrigerator information input unit. For example, the analysis unit can analyze the family preferences and allergy information and the contents of the refrigerator to suggest optimal ingredients and recipes. The analysis unit can also suggest recipes that do not use dairy products, taking into account the family preferences and allergy information. The suggestion unit suggests optimal ingredients and recipes based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest recipes using chicken and vegetables in the refrigerator. The suggestion unit can also suggest dairy-free recipes taking into account family preferences and allergy information. The shopping list suggestion unit suggests a list of ingredients to purchase based on the recipe suggested by the suggestion unit. For example, the shopping list suggestion unit can list ingredients that are missing from the refrigerator or ingredients needed for a recipe. The shopping list suggestion unit can also suggest a list to help the user shop efficiently. As a result, the cooking assistant system according to the embodiment analyzes family preferences, allergy information, and the contents of the refrigerator to suggest optimal ingredients and recipes, thereby reducing food waste and realizing meal plans tailored to individual preferences and allergies.

[0030] The input unit can analyze the family's past meal history and select an appropriate input method. The input unit can, for example, simplify the input of preferences based on the family's favorite dishes in the past. The input unit can also automatically complete the input of allergy information based on ingredients that the family has avoided in the past. The input unit can also prioritize displaying categories of frequently eaten dishes based on the family's meal history. This allows for more efficient input by selecting an input method based on the family's past meal history. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the family's past meal history data into the generation AI and have the generation AI select the optimal input method.

[0031] The input unit can filter the input of preference and allergy information based on the family's current health condition and nutritional balance. For example, the input unit prioritizes input of ingredients containing specific nutrients based on the family's health condition. The input unit can also filter ingredients to avoid overconsumption, taking into account the family's nutritional balance. The input unit can also input recommendations for or to avoid specific ingredients based on the results of the family's health check. This makes it possible to input information that takes into account the health condition and nutritional balance. Some or all of the above-mentioned processing in the input unit can be performed using, or without, AI. For example, the input unit can input the family's health condition data into the generation AI and have the generation AI perform filtering.

[0032] When inputting preference and allergy information, the input unit can prioritize inputting highly relevant information taking into account the geographical location information of family members. For example, if a family member lives in a specific area, the input unit can prioritize inputting ingredients that are easily available in that area. Furthermore, if a family member is traveling, the input unit can prioritize inputting ingredients and recipes for the travel destination. Furthermore, if a family member is planning to move, the input unit can prioritize inputting ingredients and recipes for the new area. This makes it possible to input information taking 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 geographical location information data of family members to the generation AI and cause the generation AI to input highly relevant information.

[0033] When inputting preference or allergy information, the input unit can analyze the social media activity of the family and input related information. For example, the input unit can input preference information based on photos of food shared by the family on social media. The input unit can also input preference information based on cooking accounts that the family follows on social media. The input unit can also input preference information based on food posts that the family has "liked" on social media. This makes it possible to input information based on social media activity. 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 social media activity data of the family to the generation AI and cause the generation AI to input related information.

[0034] The refrigerator information input unit can analyze the refrigerator's past inventory history and select the optimal input method. The refrigerator information input unit, for example, automatically inputs frequently purchased ingredients based on the refrigerator's past inventory history. The refrigerator information input unit can also prioritize input of ingredients with an approaching expiration date based on the refrigerator's past inventory history. The refrigerator information input unit can also analyze the refrigerator's past inventory history and suggest the optimal input timing. This allows for the input method to be selected based on the past inventory history, thereby improving input efficiency. Some or all of the above-described processing in the refrigerator information input unit may be performed using, or without, AI, for example. For example, the refrigerator information input unit can input the refrigerator's past inventory history data to the generation AI and cause the generation AI to select the optimal input method.

[0035] When inputting the contents of the refrigerator, the refrigerator information input unit can perform filtering based on the family's current meal plan and consumption trends. For example, the refrigerator information input unit prioritizes input of necessary ingredients based on the family's meal plan. The refrigerator information input unit can also filter ingredients to avoid excessive purchases, taking into account the family's consumption trends. The refrigerator information input unit can also input recommendations or avoidance of specific ingredients based on the family's meal plan. This makes it possible to input information taking into account the meal plan and consumption trends. Some or all of the above-described processing in the refrigerator information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the refrigerator information input unit can input the family's meal plan data to the generation AI and have the generation AI perform filtering.

[0036] When inputting the contents of the refrigerator, the refrigerator information input unit can prioritize inputting highly relevant ingredients taking into account the geographical location information of the family members. For example, if the family members live in a specific area, the refrigerator information input unit can prioritize inputting ingredients that are easily available in that area. Furthermore, if the family members are traveling, the refrigerator information input unit can prioritize inputting ingredients available at the travel destination. Furthermore, if the family members are planning to move, the refrigerator information input unit can prioritize inputting ingredients available in the new area. This makes it possible to input information taking geographical location information into consideration. Some or all of the above-described processing in the refrigerator information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the refrigerator information input unit can input geographical location information data of the family members to the generation AI and cause the generation AI to input highly relevant ingredients.

[0037] When inputting the contents of the refrigerator, the refrigerator information input unit can analyze the social media activity of the family and input related ingredients. The refrigerator information input unit can input the contents of the refrigerator based on, for example, photos of food shared by the family on social media. The refrigerator information input unit can also input the contents of the refrigerator based on cooking accounts that the family follows on social media. The refrigerator information input unit can also input the contents of the refrigerator based on food posts that the family has "liked" on social media. This makes it possible to input information based on social media activity. Some or all of the above-described processing in the refrigerator information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the refrigerator information input unit can input the social media activity data of the family to the generation AI and cause the generation AI to input related ingredients.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the family's past meal history. For example, the analysis unit performs analysis taking into account preferences and allergy information based on the family's past meal history. The analysis unit can also prioritize analysis of frequently eaten food categories based on the family's past meal history. The analysis unit can also refer to the family's past meal history to suggest optimal ingredients and recipes. This makes it possible to perform analysis based on past meal history. 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 the family's past meal history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] The analysis unit can perform the analysis while taking into consideration the health condition and nutritional balance of the family members. For example, the analysis unit prioritizes the analysis of ingredients containing specific nutrients based on the health condition of the family members. The analysis unit can also filter ingredients to avoid overconsumption, taking into consideration the nutritional balance of the family members. The analysis unit can also analyze to recommend or avoid specific ingredients based on the results of the family members' health checkups. This enables analysis that takes into consideration the health condition and nutritional balance. 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 the family members' health condition data into the generation AI and have the generation AI perform the analysis.

[0040] The analysis unit can perform the analysis while taking into account the geographical location information of the family members. For example, if the family members live in a specific area, the analysis unit can prioritize analyzing ingredients that are easily available in that area. Furthermore, if the family members are traveling, the analysis unit can prioritize analyzing ingredients available at the travel destination. Furthermore, if the family members are planning to move, the analysis unit can prioritize analyzing ingredients available in the new area. This enables analysis that takes geographical location information into account. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the geographical location information data of the family members into the generation AI and have the generation AI perform the analysis.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the family. For example, the analysis unit can refer to literature related to the family's health status and suggest optimal ingredients and recipes. The analysis unit can also refer to literature related to the family's preferences and suggest optimal ingredients and recipes. The analysis unit can also refer to literature related to the family's allergy information and suggest optimal ingredients and recipes. In this way, by referring to related literature, the accuracy of the analysis is improved. 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 literature data related to the family into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the ingredients and recipes. For example, the suggestion unit makes detailed suggestions for important ingredients and recipes. The suggestion unit can also make concise suggestions for ingredients and recipes with low importance. The suggestion unit can also determine the importance based on family preferences and allergy information and adjust the level of detail of the proposal. This makes it possible to adjust the level of detail of the proposal according to the importance. 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 importance data of ingredients and recipes to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients or recipes. For example, the suggestion unit can apply a suggestion algorithm that emphasizes nutritional balance to main dish recipes. The suggestion unit can also apply a suggestion algorithm that emphasizes family preferences to dessert recipes. The suggestion unit can also apply a suggestion algorithm that emphasizes ease of preparation to snack recipes. This enables suggestion algorithms to be tailored to categories. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input category data of ingredients or recipes into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0044] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of ingredients and recipes. For example, the suggestion unit can prioritize suggesting ingredients that need to be consumed immediately. The suggestion unit can also prioritize suggesting ingredients that are close to their expiration date. The suggestion unit can also prioritize suggesting necessary ingredients based on a family's meal plan. This makes it possible to prioritize suggestions based on the time of submission. 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 time of submission of ingredients and recipes into the generation AI and have the generation AI determine the priority of the suggestions.

[0045] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients and recipes. For example, the suggestion unit suggests multiple recipes that use the same ingredients by associating them with each other. The suggestion unit can also prioritize suggesting highly relevant recipes based on family preferences and allergy information. The suggestion unit can also prioritize suggesting highly relevant ingredients based on the family's meal plan. This enables the suggestion order to be based on the relevance. 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 relevance data of ingredients and recipes into a generation AI and cause the generation AI to adjust the suggestion order.

[0046] When creating a shopping list, the shopping list suggestion unit can create an optimal list by referring to the family's past purchase history. For example, the shopping list suggestion unit automatically adds frequently purchased ingredients to the list based on the family's past purchase history. The shopping list suggestion unit can also prioritize ingredients with an approaching expiration date based on the family's past purchase history and add them to the list. The shopping list suggestion unit can also create an optimal shopping list by referring to the family's past purchase history. This makes it possible to create a shopping list based on the past purchase history. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, or without, AI, for example. For example, the shopping list suggestion unit can input the family's past purchase history data into a generation AI and cause the generation AI to create an optimal list.

[0047] When creating a shopping list, the shopping list suggestion unit can customize the list by taking into account the family's current meal plan and consumption trends. For example, the shopping list suggestion unit adds necessary ingredients to the list based on the family's meal plan. The shopping list suggestion unit can also customize the list to avoid excessive purchases by taking into account the family's consumption trends. The shopping list suggestion unit can also customize the list to recommend or avoid specific ingredients based on the family's meal plan. This makes it possible to customize the shopping list by taking into account the meal plan and consumption trends. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, or without, AI. For example, the shopping list suggestion unit can input family meal plan data into the generation AI and have the generation AI customize the list.

[0048] When creating a shopping list, the shopping list suggestion unit can create an optimal list by taking into account the geographical location information of family members. For example, if a family member lives in a specific area, the shopping list suggestion unit can add ingredients that are easily available in that area to the list. Furthermore, if a family member is traveling, the shopping list suggestion unit can also add ingredients for the travel destination to the list. Furthermore, if a family member is planning to move, the shopping list suggestion unit can also add ingredients for the new area to the list. This makes it possible to create a shopping list that takes geographical location information into consideration. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the shopping list suggestion unit can input geographical location information data of family members into the generation AI and cause the generation AI to create an optimal list.

[0049] When creating a shopping list, the shopping list suggestion unit can customize the list by analyzing the social media activities of the family. For example, the shopping list suggestion unit customizes the shopping list based on photos of food shared by the family on social media. The shopping list suggestion unit can also customize the shopping list based on cooking accounts that the family follows on social media. The shopping list suggestion unit can also customize the shopping list based on food posts that the family has "liked" on social media. This makes it possible to customize the shopping list based on social media activities. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, or without, AI, for example. For example, the shopping list suggestion unit can input social media activity data of the family into the generation AI and cause the generation AI to customize the list.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can perform analysis taking into account the frequency and time periods of family meals, in addition to the family's food preferences and allergy information. For example, if the family eats breakfast frequently, the analysis unit can prioritize suggesting recipes suitable for breakfast. Also, if the family places importance on dinner, the analysis unit can prioritize suggesting recipes suitable for dinner. Furthermore, if the family prefers specific meals on specific days of the week, the analysis unit can suggest recipes tailored to those days. This makes it possible to perform analysis taking into account the frequency and time periods of family meals, thereby enabling more appropriate meal suggestions.

[0052] The shopping list suggestion unit can prioritize adding specific brands and products to the list based on the family's purchasing history. For example, if a family member frequently purchases products of a specific brand, it can prioritize adding products of that brand to the list. Also, if a family member has a preference for a specific product, it can prioritize adding that product to the list. Furthermore, it can also add to the list products that the family member has purchased in the past that they were particularly satisfied with. This makes it possible to create a shopping list that takes into account the family's purchasing history, resulting in more efficient shopping.

[0053] The input unit can take into consideration the variations in family meals when inputting family preferences and allergy information. For example, if a family member frequently eats a particular dish, the input unit can suggest new recipes related to that dish. Also, if a family member frequently uses a particular ingredient, the input unit can suggest new recipes using that ingredient. Furthermore, if a family member likes a particular food category, the input unit can suggest new recipes related to that category. This makes it possible to input information that takes into consideration the variations in family meals, enabling more diverse meal suggestions to be made.

[0054] The shopping list suggestion unit can prioritize adding specific stores or online shops to the list based on the family's purchasing history. For example, if a family member frequently shops at a specific store, products from that store can be prioritized and added to the list. Also, if a family member uses a specific online shop, products from that online shop can be prioritized and added to the list. Furthermore, stores or online shops that the family member has used in the past that have given them a particularly high level of satisfaction can be added to the list. This makes it possible to create a shopping list that takes the family member's purchasing history into consideration, resulting in more efficient shopping.

[0055] The input unit can take into consideration the seasonality of family meals when inputting family preferences and allergy information. For example, the input unit can prioritize input of dishes that family members like in a particular season. The input unit can also suggest appropriate recipes taking into consideration seasonal ingredients. Furthermore, the input unit can prioritize input of ingredients containing specific nutrients taking into consideration the nutritional balance of each season. This makes it possible to input information taking into consideration the seasonality of family meals, thereby realizing more appropriate meal suggestions.

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

[0057] Step 1: The input unit inputs family preferences and allergy information. For example, a user can input family preferences and allergy information through an app or platform. For family preferences, the user can input that they like spicy food or a particular ingredient. Step 2: The refrigerator information input unit inputs information about ingredients in the refrigerator. For example, information about vegetables, meat, seasonings, etc. in the refrigerator can be input. The refrigerator information input unit can also input information about ingredients in the refrigerator into the app. Step 3: The analysis unit analyzes the information entered by the input unit and the refrigerator information input unit. For example, it can analyze the family's preferences, allergy information, and the contents of the refrigerator to suggest optimal ingredients and recipes. It can also suggest recipes that do not contain dairy products, taking into account the family's preferences and allergy information. Step 4: The suggestion unit suggests the best ingredients and recipes based on the information analyzed by the analysis unit. For example, it can suggest recipes using chicken and vegetables from the refrigerator. It can also suggest dairy-free recipes taking into account the family's preferences and allergies. Step 5: The shopping list suggestion unit suggests a list of ingredients to purchase based on the recipe suggested by the suggestion unit. For example, it can list ingredients that are missing from the refrigerator or ingredients needed for a recipe. It can also suggest a list that will help the user shop efficiently.

[0058] (Example 2) A cooking assistant system according to an embodiment of the present invention utilizes the contents of a refrigerator and family preferences and allergy information to suggest ingredients and recipes to purchase. The cooking assistant system allows users to input their family's preferences and profiles through an app or platform, and analyzes this information to suggest optimal ingredients and recipe options. This not only effectively utilizes household ingredients, reduces food waste, but also enables meal planning tailored to individual preferences and allergies. For example, a user inputs family preferences and allergy information through an app or platform. For example, if one family member has a dairy allergy, the user can input this information. The family also inputs their preferences, such as a preference for spicy food or a preference for specific ingredients. Next, the user inputs the contents of the refrigerator into the app. For example, the user inputs information about the vegetables, meat, and seasonings in the refrigerator. This allows the app to grasp the current contents of the refrigerator. Based on this information, the app suggests optimal ingredients and recipe options. For example, the app suggests recipes using chicken and vegetables in the refrigerator. It also suggests dairy-free recipes, taking into account the family's preferences and allergy information. The app also suggests a list of ingredients to purchase. For example, it lists ingredients missing from the refrigerator and ingredients needed for recipes. This allows users to shop efficiently. This system makes it possible to effectively utilize ingredients available at home and reduce food waste. It also enables meal planning tailored to individual preferences and allergies. For example, a family member with a dairy allergy can enjoy meals without worry by suggesting dairy-free recipes. This cooking assistant can be a partner for busy households, supporting meal preparation and nutritional management. For example, it can suggest recipes that take into account the contents of the refrigerator and the preferences of the family, so that a parent who is busy at work can start cooking immediately after returning home. This makes meal preparation go smoothly and ensures meals that satisfy the whole family. The cooking assistant system analyzes family preferences, allergy information, and the contents of the refrigerator to suggest the optimal ingredients and recipes, reducing food waste and enabling meal planning tailored to individual preferences and allergies.

[0059] A cooking assistant system according to an embodiment includes an input unit, a refrigerator information input unit, an analysis unit, a suggestion unit, and a shopping list suggestion unit. The input unit inputs family preferences and allergy information. For example, the input unit allows a user to input family preferences and allergy information through an app or platform. The input unit can also input family preferences such as a preference for spicy food or a preference for specific ingredients. The refrigerator information input unit inputs information about ingredients in the refrigerator. For example, the refrigerator information input unit can input information about vegetables, meat, seasonings, etc. in the refrigerator. The refrigerator information input unit can also input information about ingredients in the refrigerator to an app. The analysis unit analyzes the information input by the input unit and the refrigerator information input unit. For example, the analysis unit can analyze the family preferences and allergy information and the contents of the refrigerator to suggest optimal ingredients and recipes. The analysis unit can also suggest recipes that do not use dairy products, taking into account the family preferences and allergy information. The suggestion unit suggests optimal ingredients and recipes based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest recipes using chicken and vegetables in the refrigerator. The suggestion unit can also suggest dairy-free recipes taking into account family preferences and allergy information. The shopping list suggestion unit suggests a list of ingredients to purchase based on the recipe suggested by the suggestion unit. For example, the shopping list suggestion unit can list ingredients that are missing from the refrigerator or ingredients needed for a recipe. The shopping list suggestion unit can also suggest a list to help the user shop efficiently. As a result, the cooking assistant system according to the embodiment analyzes family preferences, allergy information, and the contents of the refrigerator to suggest optimal ingredients and recipes, thereby reducing food waste and realizing meal plans tailored to individual preferences and allergies.

[0060] The input unit can estimate the emotions of family members and adjust the timing of inputting preference and allergy information based on the estimated emotions of the family members. For example, if a family member is relaxed, the input unit can set a time for the family member to input detailed preference and allergy information. Furthermore, if a family member is busy, the input unit can also allow the family member to input preference and allergy information in the form of a simple question. Furthermore, if a family member is feeling stressed, the input unit can set a reminder for the family member to input detailed information later. This allows for more appropriate information input by adjusting the input timing according to the family member'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, or without an AI. For example, the input unit can input emotional data of the family members into the generation AI and cause the generation AI to estimate the emotions.

[0061] The input unit can analyze the family's past meal history and select an appropriate input method. The input unit can, for example, simplify the input of preferences based on the family's favorite dishes in the past. The input unit can also automatically complete the input of allergy information based on ingredients that the family has avoided in the past. The input unit can also prioritize displaying categories of frequently eaten dishes based on the family's meal history. This allows for more efficient input by selecting an input method based on the family's past meal history. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the family's past meal history data into the generation AI and have the generation AI select the optimal input method.

[0062] The input unit can filter the input of preference and allergy information based on the family's current health condition and nutritional balance. For example, the input unit prioritizes input of ingredients containing specific nutrients based on the family's health condition. The input unit can also filter ingredients to avoid overconsumption, taking into account the family's nutritional balance. The input unit can also input recommendations for or to avoid specific ingredients based on the results of the family's health check. This makes it possible to input information that takes into account the health condition and nutritional balance. Some or all of the above-mentioned processing in the input unit can be performed using, or without, AI. For example, the input unit can input the family's health condition data into the generation AI and have the generation AI perform filtering.

[0063] The input unit can estimate the emotions of family members and determine the priority of information to be input based on the estimated emotions of the family members. For example, when a family member is relaxed, the input unit prioritizes input of detailed preferences and allergy information. Furthermore, when a family member is in a hurry, the input unit can prioritize input of only basic preferences and allergy information. Furthermore, when a family member is stressed, the input unit can be simplified so that detailed information can be input later. This enables efficient information input by determining the priority of information according to the emotions of the family members. 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, or without an AI. For example, the input unit can input emotional data of the family members to the generation AI and have the generation AI determine the priority of the information.

[0064] When inputting preference and allergy information, the input unit can prioritize inputting highly relevant information taking into account the geographical location information of family members. For example, if a family member lives in a specific area, the input unit can prioritize inputting ingredients that are easily available in that area. Furthermore, if a family member is traveling, the input unit can prioritize inputting ingredients and recipes for the travel destination. Furthermore, if a family member is planning to move, the input unit can prioritize inputting ingredients and recipes for the new area. This makes it possible to input information taking 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 geographical location information data of family members to the generation AI and cause the generation AI to input highly relevant information.

[0065] When inputting preference or allergy information, the input unit can analyze the social media activity of the family and input related information. For example, the input unit can input preference information based on photos of food shared by the family on social media. The input unit can also input preference information based on cooking accounts that the family follows on social media. The input unit can also input preference information based on food posts that the family has "liked" on social media. This makes it possible to input information based on social media activity. 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 social media activity data of the family to the generation AI and cause the generation AI to input related information.

[0066] The refrigerator information input unit can estimate the emotions of family members and adjust the timing of inputting the refrigerator contents based on the estimated emotions of the family members. For example, if the family members are relaxed, the refrigerator information input unit can set a time for them to input detailed information about the refrigerator contents. If the family members are busy, the refrigerator information input unit can also allow them to input the refrigerator contents in the form of a simple question. If the family members are stressed, the refrigerator information input unit can also set a reminder so that detailed information can be input later. This allows more appropriate information input by adjusting the timing of inputting the refrigerator contents based on the family members' emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the refrigerator information input unit can be performed using an AI, for example, or without an AI. For example, the refrigerator information input unit can input emotional data about the family members to the generation AI and have the generation AI execute emotion estimation.

[0067] The refrigerator information input unit can analyze the refrigerator's past inventory history and select the optimal input method. The refrigerator information input unit, for example, automatically inputs frequently purchased ingredients based on the refrigerator's past inventory history. The refrigerator information input unit can also prioritize input of ingredients with an approaching expiration date based on the refrigerator's past inventory history. The refrigerator information input unit can also analyze the refrigerator's past inventory history and suggest the optimal input timing. This allows for the input method to be selected based on the past inventory history, thereby improving input efficiency. Some or all of the above-described processing in the refrigerator information input unit may be performed using, or without, AI, for example. For example, the refrigerator information input unit can input the refrigerator's past inventory history data to the generation AI and cause the generation AI to select the optimal input method.

[0068] When inputting the contents of the refrigerator, the refrigerator information input unit can perform filtering based on the family's current meal plan and consumption trends. For example, the refrigerator information input unit prioritizes input of necessary ingredients based on the family's meal plan. The refrigerator information input unit can also filter ingredients to avoid excessive purchases, taking into account the family's consumption trends. The refrigerator information input unit can also input recommendations or avoidance of specific ingredients based on the family's meal plan. This makes it possible to input information taking into account the meal plan and consumption trends. Some or all of the above-described processing in the refrigerator information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the refrigerator information input unit can input the family's meal plan data to the generation AI and have the generation AI perform filtering.

[0069] The refrigerator information input unit can estimate the emotions of family members and determine the priority of ingredients to be input based on the estimated emotions of the family members. For example, when the family members are relaxed, the refrigerator information input unit prioritizes input of detailed ingredient information. Furthermore, when the family members are in a hurry, the refrigerator information input unit can also prioritize input of only basic ingredient information. Furthermore, when the family members are stressed, the refrigerator information input unit can be simplified so that detailed information can be input later. This enables efficient information input by determining the priority of ingredients according to the emotions of the family members. 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 refrigerator information input unit can be performed using, for example, an AI, or without an AI. For example, the refrigerator information input unit can input emotional data of the family members to the generation AI and have the generation AI determine the priority of ingredients.

[0070] When inputting the contents of the refrigerator, the refrigerator information input unit can prioritize inputting highly relevant ingredients taking into account the geographical location information of the family members. For example, if the family members live in a specific area, the refrigerator information input unit can prioritize inputting ingredients that are easily available in that area. Furthermore, if the family members are traveling, the refrigerator information input unit can prioritize inputting ingredients available at the travel destination. Furthermore, if the family members are planning to move, the refrigerator information input unit can prioritize inputting ingredients available in the new area. This makes it possible to input information taking geographical location information into consideration. Some or all of the above-described processing in the refrigerator information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the refrigerator information input unit can input geographical location information data of the family members to the generation AI and cause the generation AI to input highly relevant ingredients.

[0071] When inputting the contents of the refrigerator, the refrigerator information input unit can analyze the social media activity of the family and input related ingredients. The refrigerator information input unit can input the contents of the refrigerator based on, for example, photos of food shared by the family on social media. The refrigerator information input unit can also input the contents of the refrigerator based on cooking accounts that the family follows on social media. The refrigerator information input unit can also input the contents of the refrigerator based on food posts that the family has "liked" on social media. This makes it possible to input information based on social media activity. Some or all of the above-described processing in the refrigerator information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the refrigerator information input unit can input the social media activity data of the family to the generation AI and cause the generation AI to input related ingredients.

[0072] The analysis unit can estimate the emotions of family members and adjust the analysis criteria based on the estimated emotions of family members. For example, if a family member is relaxed, the analysis unit can perform a detailed analysis and suggest optimal ingredients and recipes. If a family member is in a hurry, the analysis unit can also perform a basic analysis and quickly suggest ingredients and recipes. If a family member is feeling stressed, the analysis unit can set a reminder to perform a detailed analysis later. This enables more appropriate analysis by adjusting the analysis criteria according to the family member'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 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-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input family member emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0073] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the family's past meal history. For example, the analysis unit performs analysis taking into account preferences and allergy information based on the family's past meal history. The analysis unit can also prioritize analysis of frequently eaten food categories based on the family's past meal history. The analysis unit can also refer to the family's past meal history to suggest optimal ingredients and recipes. This makes it possible to perform analysis based on past meal history. 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 the family's past meal history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0074] The analysis unit can perform the analysis while taking into consideration the health condition and nutritional balance of the family members. For example, the analysis unit prioritizes the analysis of ingredients containing specific nutrients based on the health condition of the family members. The analysis unit can also filter ingredients to avoid overconsumption, taking into consideration the nutritional balance of the family members. The analysis unit can also analyze to recommend or avoid specific ingredients based on the results of the family members' health checkups. This enables analysis that takes into consideration the health condition and nutritional balance. 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 the family members' health condition data into the generation AI and have the generation AI perform the analysis.

[0075] The analysis unit can estimate the emotions of family members and adjust the display order of the analysis results based on the estimated emotions of the family members. For example, if the family members are relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the family members are in a hurry, the analysis unit can prioritize displaying only basic analysis results. Furthermore, if the family members are stressed, the analysis unit can simplify the display order so that detailed analysis results can be displayed later. This allows for more appropriate information provision by adjusting the display order of the analysis results according to the emotions of the family members. 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input emotional data of the family members into the generation AI and have the generation AI adjust the display order of the analysis results.

[0076] The analysis unit can perform the analysis while taking into account the geographical location information of the family members. For example, if the family members live in a specific area, the analysis unit can prioritize analyzing ingredients that are easily available in that area. Furthermore, if the family members are traveling, the analysis unit can prioritize analyzing ingredients available at the travel destination. Furthermore, if the family members are planning to move, the analysis unit can prioritize analyzing ingredients available in the new area. This enables analysis that takes geographical location information into account. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the geographical location information data of the family members into the generation AI and have the generation AI perform the analysis.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the family. For example, the analysis unit can refer to literature related to the family's health status and suggest optimal ingredients and recipes. The analysis unit can also refer to literature related to the family's preferences and suggest optimal ingredients and recipes. The analysis unit can also refer to literature related to the family's allergy information and suggest optimal ingredients and recipes. In this way, by referring to related literature, the accuracy of the analysis is improved. 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 literature data related to the family into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] The suggestion unit can estimate the emotions of the family members and adjust the way the suggestions are expressed based on the estimated emotions of the family members. For example, if the family members are relaxed, the suggestion unit can make detailed suggestions and provide many options. If the family members are in a hurry, the suggestion unit can make concise suggestions and quickly provide options. If the family members are stressed, the suggestion unit can set a reminder to make detailed suggestions later. This enables the way the suggestions are expressed to suit the emotions of the family members. The emotion estimation is realized 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-mentioned 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 emotional data of the family members into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0079] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the ingredients and recipes. For example, the suggestion unit makes detailed suggestions for important ingredients and recipes. The suggestion unit can also make concise suggestions for ingredients and recipes with low importance. The suggestion unit can also determine the importance based on family preferences and allergy information and adjust the level of detail of the proposal. This makes it possible to adjust the level of detail of the proposal according to the importance. 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 importance data of ingredients and recipes to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0080] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients or recipes. For example, the suggestion unit can apply a suggestion algorithm that emphasizes nutritional balance to main dish recipes. The suggestion unit can also apply a suggestion algorithm that emphasizes family preferences to dessert recipes. The suggestion unit can also apply a suggestion algorithm that emphasizes ease of preparation to snack recipes. This enables suggestion algorithms to be tailored to categories. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input category data of ingredients or recipes into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0081] The suggestion unit can estimate the emotions of the family members and adjust the length of the suggestions based on the estimated emotions of the family members. For example, if the family members are relaxed, the suggestion unit can provide detailed suggestions and many options. If the family members are in a hurry, the suggestion unit can provide concise suggestions and quickly provide options. If the family members are stressed, the suggestion unit can set a reminder to provide detailed suggestions later. This enables the length of the suggestions to be adjusted according to the emotions of the family members. The emotion estimation is realized 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-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input emotional data of the family members into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0082] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of ingredients and recipes. For example, the suggestion unit can prioritize suggesting ingredients that need to be consumed immediately. The suggestion unit can also prioritize suggesting ingredients that are close to their expiration date. The suggestion unit can also prioritize suggesting necessary ingredients based on a family's meal plan. This makes it possible to prioritize suggestions based on the time of submission. 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 time of submission of ingredients and recipes into the generation AI and have the generation AI determine the priority of the suggestions.

[0083] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients and recipes. For example, the suggestion unit suggests multiple recipes that use the same ingredients by associating them with each other. The suggestion unit can also prioritize suggesting highly relevant recipes based on family preferences and allergy information. The suggestion unit can also prioritize suggesting highly relevant ingredients based on the family's meal plan. This enables the suggestion order to be based on the relevance. 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 relevance data of ingredients and recipes into a generation AI and cause the generation AI to adjust the suggestion order.

[0084] The shopping list suggestion unit can estimate the emotions of family members and adjust the method for creating the shopping list based on the estimated emotions of the family members. For example, if a family member is relaxed, the shopping list suggestion unit can create a detailed shopping list. If a family member is in a hurry, the shopping list suggestion unit can also create a concise shopping list. If a family member is feeling stressed, the shopping list suggestion unit can set a reminder to create a detailed shopping list later. This enables a method for creating a shopping list according to the emotions of the family members. Emotion estimation is realized 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 these examples. Some or all of the above-mentioned processing in the shopping list suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the shopping list suggestion unit can input emotional data of the family members into the generation AI and cause the generation AI to adjust the method for creating the shopping list.

[0085] When creating a shopping list, the shopping list suggestion unit can create an optimal list by referring to the family's past purchase history. For example, the shopping list suggestion unit automatically adds frequently purchased ingredients to the list based on the family's past purchase history. The shopping list suggestion unit can also prioritize ingredients with an approaching expiration date based on the family's past purchase history and add them to the list. The shopping list suggestion unit can also create an optimal shopping list by referring to the family's past purchase history. This makes it possible to create a shopping list based on the past purchase history. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, or without, AI, for example. For example, the shopping list suggestion unit can input the family's past purchase history data into a generation AI and cause the generation AI to create an optimal list.

[0086] When creating a shopping list, the shopping list suggestion unit can customize the list by taking into account the family's current meal plan and consumption trends. For example, the shopping list suggestion unit adds necessary ingredients to the list based on the family's meal plan. The shopping list suggestion unit can also customize the list to avoid excessive purchases by taking into account the family's consumption trends. The shopping list suggestion unit can also customize the list to recommend or avoid specific ingredients based on the family's meal plan. This makes it possible to customize the shopping list by taking into account the meal plan and consumption trends. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, or without, AI. For example, the shopping list suggestion unit can input family meal plan data into the generation AI and have the generation AI customize the list.

[0087] The shopping list suggestion unit can estimate the emotions of family members and prioritize the shopping list based on the estimated emotions of the family members. For example, if the family members are relaxed, the shopping list suggestion unit can prioritize creating a detailed shopping list. If the family members are in a hurry, the shopping list suggestion unit can also prioritize creating only a basic shopping list. If the family members are stressed, the shopping list suggestion unit can also be simplified so that a detailed shopping list can be created later. This enables prioritization of the shopping list according to the emotions of the family members. The emotion estimation is realized 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 shopping list suggestion unit can be performed using, for example, AI, or without AI. For example, the shopping list suggestion unit can input family member emotion data into the generation AI and have the generation AI determine the prioritization of the shopping list.

[0088] When creating a shopping list, the shopping list suggestion unit can create an optimal list by taking into account the geographical location information of family members. For example, if a family member lives in a specific area, the shopping list suggestion unit can add ingredients that are easily available in that area to the list. Furthermore, if a family member is traveling, the shopping list suggestion unit can also add ingredients for the travel destination to the list. Furthermore, if a family member is planning to move, the shopping list suggestion unit can also add ingredients for the new area to the list. This makes it possible to create a shopping list that takes geographical location information into consideration. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the shopping list suggestion unit can input geographical location information data of family members into the generation AI and cause the generation AI to create an optimal list.

[0089] When creating a shopping list, the shopping list suggestion unit can customize the list by analyzing the social media activities of the family. For example, the shopping list suggestion unit customizes the shopping list based on photos of food shared by the family on social media. The shopping list suggestion unit can also customize the shopping list based on cooking accounts that the family follows on social media. The shopping list suggestion unit can also customize the shopping list based on food posts that the family has "liked" on social media. This makes it possible to customize the shopping list based on social media activities. Some or all of the above-described processing in the shopping list suggestion unit may be performed using, or without, AI, for example. For example, the shopping list suggestion unit can input social media activity data of the family into the generation AI and cause the generation AI to customize the list. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, refrigerator information input unit, analysis unit, suggestion unit, and shopping list suggestion unit, described above, is realized by, for example, at least one of the smart device 14 and the data processing device 12. For example, the input unit can input family preferences and allergy information using the reception device 38 of the smart device 14. The refrigerator information input unit can take a picture of the contents of the refrigerator using the camera 42 of the smart device 14 and input that information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal ingredients and recipes based on the analysis results. The shopping list suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of ingredients to purchase based on the suggested recipe. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, refrigerator information input unit, analysis unit, suggestion unit, and shopping list suggestion unit, described above, is realized by, for example, 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 family preferences and allergy information by voice. The refrigerator information input unit can use the camera 42 of the smart glasses 214 to take a picture of the contents of the refrigerator and input that information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal ingredients and recipes based on the analysis results. The shopping list suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of ingredients to purchase based on the suggested recipe. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, refrigerator information input unit, analysis unit, suggestion unit, and shopping list suggestion 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 input family preferences and allergy information by voice using the microphone 238 of the headset terminal 314. The refrigerator information input unit can take a picture of the contents of the refrigerator using the camera 42 of the headset terminal 314 and input that information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal ingredients and recipes based on the analysis results. The shopping list suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of ingredients to purchase based on the suggested recipe. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, refrigerator information input unit, analysis unit, suggestion unit, and shopping list suggestion 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 input family preferences and allergy information by voice using the microphone 238 of the robot 414. The refrigerator information input unit can take a picture of the contents of the refrigerator using the camera 42 of the robot 414 and input that information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal ingredients and recipes based on the analysis results. The shopping list suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates a list of ingredients to purchase based on the suggested recipe.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can perform analysis taking into account the frequency and time periods of family meals, in addition to the family's food preferences and allergy information. For example, if the family eats breakfast frequently, the analysis unit can prioritize suggesting recipes suitable for breakfast. Also, if the family places importance on dinner, the analysis unit can prioritize suggesting recipes suitable for dinner. Furthermore, if the family prefers specific meals on specific days of the week, the analysis unit can suggest recipes tailored to those days. This makes it possible to perform analysis taking into account the frequency and time periods of family meals, thereby enabling more appropriate meal suggestions.

[0092] The suggestion unit can estimate the emotions of family members and adjust the difficulty of the suggested recipes based on the estimated emotions of family members. For example, if a family member is relaxed, it can suggest a more difficult recipe. If a family member is busy, it can suggest a simple recipe. Furthermore, if a family member is stressed, it can suggest a recipe that is easy to make. This makes it possible to adjust the difficulty of the recipes according to the emotions of family members, thereby realizing more appropriate meal suggestions.

[0093] The shopping list suggestion unit can prioritize adding specific brands and products to the list based on the family's purchasing history. For example, if a family member frequently purchases products of a specific brand, it can prioritize adding products of that brand to the list. Also, if a family member has a preference for a specific product, it can prioritize adding that product to the list. Furthermore, it can also add to the list products that the family member has purchased in the past that they were particularly satisfied with. This makes it possible to create a shopping list that takes into account the family's purchasing history, resulting in more efficient shopping.

[0094] The analysis unit can estimate the emotions of family members and adjust the display format of the analysis results based on the estimated emotions of the family members. For example, if the family members are relaxed, detailed analysis results can be displayed in graphs or charts. If the family members are in a hurry, the analysis results can be displayed in simple text format. Furthermore, if the family members are feeling stressed, a reminder can be set so that the detailed analysis results can be checked later. This makes it possible to display analysis results in a format that suits the emotions of the family members, thereby providing more appropriate information.

[0095] The input unit can take into consideration the variations in family meals when inputting family preferences and allergy information. For example, if a family member frequently eats a particular dish, the input unit can suggest new recipes related to that dish. Also, if a family member frequently uses a particular ingredient, the input unit can suggest new recipes using that ingredient. Furthermore, if a family member likes a particular food category, the input unit can suggest new recipes related to that category. This makes it possible to input information that takes into consideration the variations in family meals, enabling more diverse meal suggestions to be made.

[0096] The suggestion unit can estimate the emotions of family members and adjust the presentation method of the suggested recipes based on the estimated emotions of the family members. For example, if the family members are relaxed, detailed explanations of the recipe and cooking steps can be provided in the form of a video. If the family members are in a hurry, the recipe can be provided in a simple text format. Furthermore, if the family members are feeling stressed, a reminder can be set so that the detailed recipe can be checked later. This makes it possible to present recipes in a way that suits the emotions of the family members, thereby providing more appropriate information.

[0097] The shopping list suggestion unit can prioritize adding specific stores or online shops to the list based on the family's purchasing history. For example, if a family member frequently shops at a specific store, products from that store can be prioritized and added to the list. Also, if a family member uses a specific online shop, products from that online shop can be prioritized and added to the list. Furthermore, stores or online shops that the family member has used in the past that have given them a particularly high level of satisfaction can be added to the list. This makes it possible to create a shopping list that takes the family member's purchasing history into consideration, resulting in more efficient shopping.

[0098] The analysis unit can estimate the emotions of family members and adjust the priority of analysis based on the estimated emotions of family members. For example, if a family member is relaxed, detailed analysis can be prioritized. If a family member is in a hurry, basic analysis can be prioritized. Furthermore, if a family member is feeling stressed, a reminder can be set to perform a detailed analysis later. This makes it possible to prioritize analysis according to the emotions of family members, and to provide more appropriate information.

[0099] The input unit can take into consideration the seasonality of family meals when inputting family preferences and allergy information. For example, the input unit can prioritize input of dishes that family members like in a particular season. The input unit can also suggest appropriate recipes taking into consideration seasonal ingredients. Furthermore, the input unit can prioritize input of ingredients containing specific nutrients taking into consideration the nutritional balance of each season. This makes it possible to input information taking into consideration the seasonality of family meals, thereby realizing more appropriate meal suggestions.

[0100] The suggestion unit can estimate the emotions of family members and adjust the recipe variations it suggests based on the estimated emotions of the family members. For example, if the family members are relaxed, it can provide a variety of recipe options. If the family members are in a hurry, it can prioritize suggestions of simple and easy-to-make recipes. Furthermore, if the family members are feeling stressed, it can set a reminder so that they can check the detailed recipe later. This makes it possible to adjust the recipe variations according to the emotions of the family members, resulting in more appropriate meal suggestions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit inputs family preferences and allergy information. For example, a user can input family preferences and allergy information through an app or platform. For family preferences, the user can input that they like spicy food or a particular ingredient. Step 2: The refrigerator information input unit inputs information about ingredients in the refrigerator. For example, information about vegetables, meat, seasonings, etc. in the refrigerator can be input. The refrigerator information input unit can also input information about ingredients in the refrigerator into the app. Step 3: The analysis unit analyzes the information entered by the input unit and the refrigerator information input unit. For example, it can analyze the family's preferences, allergy information, and the contents of the refrigerator to suggest optimal ingredients and recipes. It can also suggest recipes that do not contain dairy products, taking into account the family's preferences and allergy information. Step 4: The suggestion unit suggests the best ingredients and recipes based on the information analyzed by the analysis unit. For example, it can suggest recipes using chicken and vegetables from the refrigerator. It can also suggest dairy-free recipes taking into account the family's preferences and allergies. Step 5: The shopping list suggestion unit suggests a list of ingredients to purchase based on the recipe suggested by the suggestion unit. For example, it can list ingredients that are missing from the refrigerator or ingredients needed for a recipe. It can also suggest a list that will help the user shop efficiently.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 section for entering family preferences and allergy information; an analysis unit that analyzes the information input by the input unit; a refrigerator information input unit for inputting the contents of the refrigerator; the analysis unit that analyzes the information input by the refrigerator information input unit; a suggestion unit that suggests appropriate ingredients and recipes based on the information analyzed by the analysis unit; a purchase list suggestion unit that suggests a list of ingredients to be purchased based on the recipe suggested by the suggestion unit. A system characterized by:

2. The input unit Estimate the emotions of family members and adjust the timing of inputting preferences and allergy information based on the estimated emotions of family members The system of claim 1 .

3. The input unit Analyze the family's past dietary history and select the appropriate input method The system of claim 1 .

4. The input unit Filtering based on your family's current health and nutritional balance when entering preferences and allergy information The system of claim 1 .

5. The input unit Estimate the family's feelings and prioritize the information to be entered based on the estimated family's feelings The system of claim 1 .

6. The input unit When entering preferences and allergies, the app takes into account your family's geographic location to prioritize the most relevant information. The system of claim 1 .

7. The input unit Analyzes your family's social media activity and populates relevant information when you enter preferences or allergies The system of claim 1 .

8. The refrigerator information input unit Estimate the emotions of family members and adjust the timing of inputting the contents of the refrigerator based on the estimated emotions of family members The system of claim 1 .

9. The refrigerator information input unit Analyze the refrigerator's past inventory history and select the optimal input method The system of claim 1 .

10. The refrigerator information input unit Filter your fridge contents based on your family's current meal plans and consumption habits The system of claim 1 .

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