system

Abstract

The system according to the embodiment aims to propose an optimal menu based on the user's preferences and health condition. [Solution] A system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, a proposal unit, a calculation unit, and a purchase unit. The reception unit inputs information. The acquisition unit acquires sale information based on the information input by the reception unit. The analysis unit analyzes the information acquired by the acquisition unit. The proposal unit proposes a menu based on the analysis results obtained by the analysis unit. The calculation unit calculates a cooking method or time based on the menu proposed by the proposal unit. The purchase unit purchases ingredients within a budget based on the information calculated by the calculation 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 has difficulty proposing optimal menus based on a user's preferences and health status, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu based on the user's preferences and health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, a proposal unit, a calculation unit, and a purchase unit. The reception unit inputs information. The acquisition unit acquires sale information based on the information input by the reception unit. The analysis unit analyzes the information acquired by the acquisition unit. The proposal unit proposes a menu based on the analysis results obtained by the analysis unit. The calculation unit calculates a cooking method or time based on the menu proposed by the proposal unit. The purchase unit purchases ingredients within a budget based on the information calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal menu based on the user's preferences and health condition. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu suggestion system according to an embodiment of the present invention proposes optimal menus to a user. This menu suggestion system suggests cost-effective menus by inputting information such as the ingredients purchased by the user, their location, their rice preferences, and the desired health status, acquiring sales information around the user's current location, and analyzing this information. The system also calculates cooking techniques and estimated cooking times based on the number of servings input by the user, and provides additional services to help users purchase ingredients within their monthly budget. For example, a user may input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." This information is entered into the system. The system then acquires sales information around the user's current location. For example, the system acquires sales information from supermarkets in the user's area to check whether chicken and cabbage are on sale. This information is automatically collected by the system. The system analyzes the input information and the acquired sales information. For example, the system combines the ingredients purchased by the user with the sales information to propose cost-effective menus. For example, the system suggests "healthy Japanese recipes using chicken and cabbage." This suggestion is based on the user's preferences and health status. The system also calculates cooking methods and estimated cooking times. For example, if a user inputs "serves two," the system provides information such as "It will take 20 minutes to make stir-fried chicken and cabbage." This allows the user to understand the specific cooking methods and time. The system also provides an additional service of purchasing ingredients within a monthly budget. For example, if a user inputs "My monthly food budget is 10,000 yen," the system will purchase the optimal ingredients within that budget and suggest a menu. This allows the user to purchase ingredients efficiently within their budget and maintain a healthy diet. In this way, the present invention is a system that proposes optimal menus based on the user's input information and nearby sales information, calculates cooking methods and time, and purchases ingredients within the budget. This allows the menu proposal system to propose optimal menus based on the user's input information and nearby sales information, calculate cooking methods and time, and purchase ingredients within the budget.

[0029] A menu suggestion system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, a suggestion unit, a calculation unit, and a purchasing unit. The reception unit inputs information such as the ingredients a user purchases, their location, their rice preferences, and the health status they are aiming for. For example, the reception unit can input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." The acquisition unit acquires sales information around the user's current location. For example, the acquisition unit acquires sales information from supermarkets in the user's area and checks whether chicken and cabbage are on sale. The analysis unit analyzes the input information and the acquired sales information. For example, the analysis unit combines the ingredients a user purchases with the sales information to suggest a cost-effective menu. The suggestion unit suggests a cost-effective menu based on the analysis results. For example, the suggestion unit suggests a "healthy Japanese recipe using chicken and cabbage." The calculation unit calculates a cooking method and estimated cooking time based on the number of servings input by the user. For example, if a user inputs "serves two," the calculation unit provides information such as "It takes 20 minutes to make stir-fried chicken and cabbage." The purchasing unit purchases ingredients within a monthly budget. For example, if a user inputs "my monthly ingredient purchasing budget is 10,000 yen," the purchasing unit purchases optimal ingredients within that budget and proposes a menu. As a result, the menu proposal system according to the embodiment can propose an optimal menu based on the user's input information and nearby sales information, calculate cooking methods and time, and purchase ingredients within the budget.

[0030] The reception unit can input information such as the ingredients purchased by the user, location, rice preferences, and desired health status. For example, the reception unit can input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." By inputting detailed information about the user, more accurate menu suggestions can be made.

[0031] The acquisition unit can acquire sales information around the current location. For example, the acquisition unit acquires sales information from supermarkets in the user's area and checks whether chicken or cabbage is on sale. By acquiring sales information around the current location, it is possible to propose cost-effective meals.

[0032] The analysis unit can analyze the input information and the acquired sale information. For example, the analysis unit can combine the ingredients purchased by the user with sale information to propose a meal plan with good value for money. By analyzing the input information and sale information, it is possible to propose the optimal meal plan.

[0033] The suggestion unit can suggest cost-effective menus based on the analysis results. For example, the suggestion unit suggests "healthy Japanese recipes using chicken and cabbage." This improves user satisfaction by suggesting cost-effective menus.

[0034] The calculation unit can calculate the cooking method and estimated cooking time according to the number of people input by the user. For example, if the user inputs "serves two," the calculation unit will provide information such as "it will take 20 minutes to make stir-fried chicken and cabbage." This allows for efficient cooking by calculating the cooking method and time according to the user's input.

[0035] The purchasing unit allows users to purchase ingredients within their monthly budget. For example, if a user inputs "My monthly food purchasing budget is 10,000 yen," the purchasing unit will purchase the optimal ingredients within that budget and suggest a menu. This allows users to purchase ingredients within their budget and live an economical diet.

[0036] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays ingredients and locations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest ingredients and locations to be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0037] When a user inputs information, the reception unit can automatically complete the input content based on the user's current health condition and dietary history. For example, if the user is on a diet, the reception unit can automatically display low-calorie ingredients as candidates. The reception unit can also complete the input content so that the user avoids ingredients to which the user has had an allergic reaction in the past. Furthermore, if the user wants to consume a specific nutrient, the reception unit can automatically suggest ingredients containing that nutrient. This reduces the effort required for input by completing the input content based on the user's health condition and dietary history.

[0038] The reception unit can present regional ingredients and dishes as input candidates based on the user's geographical location information. For example, if the user lives in Hokkaido, the reception unit can display regional ingredients (e.g., salmon and potatoes) as candidates. Furthermore, if the user lives in Okinawa, the reception unit can also display regional dishes (e.g., bitter melon chanpuru) as candidates. Furthermore, if the user lives in Kyoto, the reception unit can also display regional ingredients (e.g., Kyoto vegetables) as candidates. By presenting regional ingredients and dishes as input candidates, it becomes possible to propose a menu suited to the region.

[0039] The reception unit can analyze the user's social media activity and prompt the user to input related ingredients and dishes. For example, the reception unit can display related ingredients as input candidates based on photos of dishes shared by the user on social media. The reception unit can also analyze posts from cooking accounts the user follows and display related dishes as input candidates. Furthermore, the reception unit can display related ingredients and dishes as input candidates based on posts of dishes that the user has "liked." This allows input that matches the user's interests by suggesting related ingredients and dishes based on social media activity.

[0040] The acquisition unit can analyze past sales information and select an appropriate acquisition method. For example, the acquisition unit can acquire sales information for a specific day of the week or time period based on past sales information. The acquisition unit can also analyze past sales information and prioritize acquisition of sales information for specific stores. Furthermore, the acquisition unit can also prioritize acquisition of sales information for specific ingredients based on past sales information. This allows for efficient information acquisition by selecting the optimal acquisition method based on past sales information.

[0041] When acquiring sale information, the acquisition unit can perform filtering based on the user's current purchase history and areas of interest. For example, the acquisition unit prioritizes acquiring sale information for ingredients that the user has purchased in the past. The acquisition unit can also prioritize acquiring sale information for ingredients or dishes in which the user has shown interest. Furthermore, the acquisition unit can filter and provide related sale information based on the user's purchase history. In this way, highly relevant information can be provided by filtering based on the purchase history and areas of interest.

[0042] When acquiring sales information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit can prioritize acquiring sales information for the area where the user lives. The acquisition unit can also prioritize acquiring sales information for stores that the user frequently visits. Furthermore, the acquisition unit can prioritize acquiring sales information for the nearest store based on the user's geographical location information. This makes it possible to provide information that is appropriate for the area by acquiring highly relevant information by taking into account the geographical location information.

[0043] When acquiring sale information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit acquires sale information related to ingredients or dishes shared by the user on social media. The acquisition unit can also prioritize acquisition of sale information for stores or brands that the user follows. Furthermore, the acquisition unit can also acquire sale information related to posts that the user has "liked." In this way, by acquiring related information based on social media activity, it is possible to provide information that matches the user's interests.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between ingredients. For example, the analysis unit can propose an optimal menu by taking into account the interrelationships between nutrients in ingredients. The analysis unit can also propose a balanced menu by taking into account the interrelationships between the flavors of ingredients. Furthermore, the analysis unit can propose an efficient menu by taking into account the interrelationships between cooking methods of ingredients. In this way, by taking into account the interrelationships between ingredients, more accurate analysis results can be provided.

[0045] The analysis unit can perform the analysis while taking into account the user's attribute information. The analysis unit can propose an optimal menu, for example, taking into account the user's age and gender. The analysis unit can also propose a healthy menu, taking into account the user's health condition. Furthermore, the analysis unit can also propose a menu that will provide high satisfaction, taking into account the user's food preferences. This makes it possible to provide individually optimized analysis results by taking into account the user's attribute information.

[0046] The analysis unit can perform the analysis while taking into account the geographical distribution of ingredients. For example, the analysis unit can propose an optimal menu by taking into account ingredients specific to a region. The analysis unit can also propose an efficient menu by taking into account ingredients that are easily available in a geographical area. Furthermore, the analysis unit can also propose a healthy menu by taking into account ingredients that are fresh in a geographical area. In this way, by taking into account the geographical distribution of ingredients, it is possible to provide analysis results that are appropriate for a region.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature. For example, the analysis unit can refer to the latest nutritional research to suggest healthy menus. The analysis unit can also refer to literature on cooking methods to suggest efficient menus. Furthermore, the analysis unit can refer to literature on ingredient combinations to suggest balanced menus. In this way, by referring to related literature, more accurate analysis results can be provided.

[0048] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the ingredients when making a suggestion. For example, the suggestion unit can suggest detailed cooking methods for main ingredients. The suggestion unit can also suggest simple cooking methods for supplementary ingredients. Furthermore, the suggestion unit can provide detailed information for ingredients in which the user is particularly interested. In this way, by adjusting the level of detail of the suggestion based on the importance of the ingredients, it is possible to provide important information to the user.

[0049] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients. For example, the suggestion unit makes suggestions that emphasize nutritional value for vegetables. The suggestion unit can also make suggestions that emphasize cooking time for meat. Furthermore, the suggestion unit can also make suggestions that emphasize freshness for seafood. This makes it possible to make more appropriate suggestions by applying suggestion algorithms according to the category of ingredients.

[0050] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the ingredients. For example, the proposal unit can give priority to proposals that use ingredients that are close to their expiration date. The proposal unit can also give priority to proposals that use fresh ingredients. Furthermore, the proposal unit can also give priority to proposals that use seasonal ingredients. In this way, by determining the priority of proposals based on the time of submission of ingredients, proposals can be made at an appropriate time.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of ingredients when suggesting ingredients. For example, the suggestion unit can suggest ingredients for the main dish first, followed by ingredients for the side dish. The suggestion unit can also suggest ingredients that use the same cooking method together. Furthermore, the suggestion unit can suggest related ingredients in an order taking nutritional balance into consideration. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of ingredients.

[0052] When calculating, the calculation unit can analyze the user's past cooking history and select the optimal calculation method. The calculation unit calculates the optimal cooking method, for example, based on the cooking methods used by the user in the past. The calculation unit can also calculate a method for shortening cooking time from the user's past cooking history. Furthermore, the calculation unit can analyze the user's past cooking history and calculate the most efficient cooking method. In this way, an efficient cooking method can be provided by selecting the optimal calculation method based on the past cooking history.

[0053] The calculation unit can customize the calculation method based on the user's current living situation when making the calculation. For example, if the user is busy, the calculation unit calculates a cooking method that can be done in a short amount of time. Furthermore, if the user values ​​health, the calculation unit can also calculate a healthy cooking method. Furthermore, if the user eats a meal with their family, the calculation unit can also calculate a cooking method that will satisfy the whole family. In this way, by customizing the calculation method based on the user's current living situation, the optimal cooking method can be provided to the user.

[0054] During calculation, the calculation unit can select the optimal calculation method by taking into account the user's geographical location information. For example, the calculation unit calculates the optimal cooking method by taking into account ingredients in the area where the user lives. The calculation unit can also calculate the optimal cooking method by taking into account ingredients at stores frequently visited by the user. Furthermore, the calculation unit can calculate the most efficient cooking method based on the user's geographical location information. In this way, by selecting the optimal calculation method by taking into account the geographical location information, it is possible to provide a cooking method that is suitable for the area.

[0055] During the calculation, the calculation unit can analyze the user's social media activity and suggest a calculation method. For example, the calculation unit calculates the optimal cooking method based on cooking methods shared by the user on social media. The calculation unit can also analyze posts from cooking accounts the user follows and calculate related cooking methods. Furthermore, the calculation unit can calculate the optimal cooking method based on cooking posts that the user has "liked." In this way, by suggesting a calculation method based on social media activity, it is possible to provide a cooking method that matches the user's interests.

[0056] At the time of purchase, the purchasing unit can analyze the user's past purchasing history and select the optimal purchasing method. The purchasing unit can suggest the optimal purchasing method, for example, based on ingredients the user has purchased in the past. The purchasing unit can also suggest an efficient purchasing method based on the user's past purchasing history. Furthermore, the purchasing unit can analyze the user's past purchasing history and suggest the most cost-effective purchasing method. This allows for efficient purchasing by selecting the optimal purchasing method based on the user's past purchasing history.

[0057] The purchasing unit can customize the purchasing method based on the user's current living situation at the time of purchase. For example, if the user is busy, the purchasing unit can suggest an online purchasing method. Also, if the user values ​​health, the purchasing unit can suggest a method for prioritizing the purchase of healthy ingredients. Furthermore, if the user eats with their family, the purchasing unit can suggest a method for purchasing ingredients that will satisfy the whole family. In this way, by customizing the purchasing method based on the user's current living situation, the optimal purchasing method can be provided to the user.

[0058] At the time of purchase, the purchasing unit can select the optimal purchasing method by taking into account the user's geographical location information. The purchasing unit can, for example, suggest the optimal purchasing method by taking into account stores in the area where the user lives. The purchasing unit can also suggest the optimal purchasing method by taking into account sales information at stores frequently visited by the user. Furthermore, the purchasing unit can also suggest the most efficient purchasing method based on the user's geographical location information. In this way, by selecting the optimal purchasing method by taking into account the geographical location information, a purchasing method suited to the area can be provided.

[0059] At the time of purchase, the purchasing unit can analyze the user's social media activity and suggest purchasing methods. For example, the purchasing unit can suggest purchasing methods related to ingredients or dishes that the user has shared on social media. The purchasing unit can also suggest the optimal purchasing method by taking into account sales information from stores and brands that the user follows. Furthermore, the purchasing unit can also suggest methods for purchasing ingredients related to posts that the user has "liked." In this way, by suggesting purchasing methods based on social media activity, it is possible to provide purchasing methods that match the user's interests.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can input the user's food preferences and allergy information. For example, the user can input information such as "I don't like green peppers" or "I'm allergic to nuts." This allows the system to propose menus that take the user's preferences and allergies into consideration. The reception unit can also input the user's meal frequency and meal times. For example, the user can input information such as "three meals a day," "breakfast at 7:00, lunch at 12:00, and dinner at 19:00." This allows the system to propose menus that match the user's meal times. Furthermore, the reception unit can also input the user's food storage method and storage period. For example, the user can input information such as "store in the refrigerator" and "storage period is one week." This allows the system to propose menus that match the user's food storage conditions.

[0062] The acquisition unit can prioritize acquisition of sale information for ingredients purchased in the past based on the user's purchasing history. For example, sale information for ingredients that the user has frequently purchased in the past can be prioritized. This makes it possible to efficiently acquire sale information for ingredients that the user frequently purchases. The acquisition unit can also filter sale information based on the user's areas of interest. For example, sale information for ingredients or dishes in which the user has shown interest can be prioritized. This makes it possible to provide sale information that matches the user's interests. Furthermore, the acquisition unit can filter and provide related sale information based on the user's purchasing history. For example, sale information related to ingredients that the user has purchased in the past can be prioritized. This makes it possible to provide highly relevant sale information.

[0063] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients. For example, suggestions can be made that emphasize nutritional value for vegetables. For meat, suggestions can be made that emphasize cooking time. For seafood, suggestions can be made that emphasize freshness. This makes it possible to make more appropriate suggestions by applying suggestion algorithms according to the category of ingredients. For example, when suggesting vegetables, emphasis can be placed on the vitamin and mineral content, and when suggesting meat, priority can be given to recipes that can be cooked in a short time. For seafood, suggestions can also be made on cooking methods and storage methods to maintain freshness. This makes it possible to make optimal suggestions according to the user's needs.

[0064] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display ingredients and locations that the user has frequently input in the past as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ingredients and locations to be used in a specific time period from the user's past input history. This improves input efficiency by suggesting the optimal input method based on the past input history. For example, it can reduce the effort required for input by displaying ingredients that the user has frequently purchased in the past as candidates and prioritizing suggestions of input methods that have been used in the past. It can also predict and suggest ingredients to be used in a specific time period, enabling more efficient input.

[0065] The acquisition unit can analyze past sales information and select an appropriate acquisition method. For example, it can acquire sales information for a specific day of the week or time period based on past sales information. It can also analyze past sales information and prioritize acquisition of sales information for a specific store. It can also prioritize acquisition of sales information for a specific food ingredient based on past sales information. This enables efficient information acquisition by selecting the optimal acquisition method based on past sales information. For example, acquiring sales information for a specific day of the week or time period based on past sales information enables efficient information collection. It can also provide useful information to users by prioritizing acquisition of sales information for a specific store. It can also provide information that meets the needs of users by prioritizing acquisition of sales information for a specific food ingredient.

[0066] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between ingredients. For example, it can propose an optimal menu by taking into account the interrelationships between nutrients in ingredients. It can also propose a balanced menu by taking into account the interrelationships between flavors of ingredients. It can also propose an efficient menu by taking into account the interrelationships between cooking methods of ingredients. In this way, by taking into account the interrelationships between ingredients, it is possible to provide more accurate analysis results. For example, it can propose a menu with a balanced amount of vitamins and minerals by taking into account the interrelationships between nutrients. It can also propose a menu with a harmonious taste by taking into account the interrelationships between flavors. It can also propose a menu that can be cooked efficiently by taking into account the interrelationships between cooking methods.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit inputs information such as the ingredients the user is purchasing, their location, their rice preferences, and the health status they are aiming for. For example, a user can input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." Step 2: The acquisition unit acquires sales information for the area around the user's current location. For example, it acquires sales information for supermarkets in the user's area and checks whether chicken or cabbage is on sale. Step 3: The analysis unit analyzes the input information and the acquired sales information. For example, it combines the ingredients the user is purchasing with the sales information to suggest cost-effective meals. Step 4: The proposal team will propose cost-effective menus based on the analysis results. For example, they will suggest a "healthy Japanese recipe using chicken and cabbage." Step 5: The calculation unit calculates the cooking method and estimated cooking time according to the number of servings entered by the user. For example, if the user enters "2 servings," it will provide information such as "It will take 20 minutes to make stir-fried chicken and cabbage." Step 6: The purchasing department purchases ingredients within the monthly budget. For example, if the user inputs "My monthly ingredient budget is 10,000 yen," the purchasing department will purchase the optimal ingredients within that budget and suggest a menu.

[0069] (Example 2) A menu suggestion system according to an embodiment of the present invention proposes optimal menus to a user. This menu suggestion system suggests cost-effective menus by inputting information such as the ingredients purchased by the user, their location, their rice preferences, and the desired health status, acquiring sales information around the user's current location, and analyzing this information. The system also calculates cooking techniques and estimated cooking times based on the number of servings input by the user, and provides additional services to help users purchase ingredients within their monthly budget. For example, a user may input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." This information is entered into the system. The system then acquires sales information around the user's current location. For example, the system acquires sales information from supermarkets in the user's area to check whether chicken and cabbage are on sale. This information is automatically collected by the system. The system analyzes the input information and the acquired sales information. For example, the system combines the ingredients purchased by the user with the sales information to propose cost-effective menus. For example, the system suggests "healthy Japanese recipes using chicken and cabbage." This suggestion is based on the user's preferences and health status. The system also calculates cooking methods and estimated cooking times. For example, if a user inputs "serves two," the system provides information such as "It will take 20 minutes to make stir-fried chicken and cabbage." This allows the user to understand the specific cooking methods and time. The system also provides an additional service of purchasing ingredients within a monthly budget. For example, if a user inputs "My monthly food budget is 10,000 yen," the system will purchase the optimal ingredients within that budget and suggest a menu. This allows the user to purchase ingredients efficiently within their budget and maintain a healthy diet. In this way, the present invention is a system that proposes optimal menus based on the user's input information and nearby sales information, calculates cooking methods and time, and purchases ingredients within the budget. This allows the menu proposal system to propose optimal menus based on the user's input information and nearby sales information, calculate cooking methods and time, and purchase ingredients within the budget.

[0070] A menu suggestion system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, a suggestion unit, a calculation unit, and a purchasing unit. The reception unit inputs information such as the ingredients a user purchases, their location, their rice preferences, and the health status they are aiming for. For example, the reception unit can input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." The acquisition unit acquires sales information around the user's current location. For example, the acquisition unit acquires sales information from supermarkets in the user's area and checks whether chicken and cabbage are on sale. The analysis unit analyzes the input information and the acquired sales information. For example, the analysis unit combines the ingredients a user purchases with the sales information to suggest a cost-effective menu. The suggestion unit suggests a cost-effective menu based on the analysis results. For example, the suggestion unit suggests a "healthy Japanese recipe using chicken and cabbage." The calculation unit calculates a cooking method and estimated cooking time based on the number of servings input by the user. For example, if a user inputs "serves two," the calculation unit provides information such as "It takes 20 minutes to make stir-fried chicken and cabbage." The purchasing unit purchases ingredients within a monthly budget. For example, if a user inputs "my monthly ingredient purchasing budget is 10,000 yen," the purchasing unit purchases optimal ingredients within that budget and proposes a menu. As a result, the menu proposal system according to the embodiment can propose an optimal menu based on the user's input information and nearby sales information, calculate cooking methods and time, and purchase ingredients within the budget.

[0071] The reception unit can input information such as the ingredients purchased by the user, location, rice preferences, and desired health status. For example, the reception unit can input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." By inputting detailed information about the user, more accurate menu suggestions can be made.

[0072] The acquisition unit can acquire sales information around the current location. For example, the acquisition unit acquires sales information from supermarkets in the user's area and checks whether chicken or cabbage is on sale. By acquiring sales information around the current location, it is possible to propose cost-effective meals.

[0073] The analysis unit can analyze the input information and the acquired sale information. For example, the analysis unit can combine the ingredients purchased by the user with sale information to propose a meal plan with good value for money. By analyzing the input information and sale information, it is possible to propose the optimal meal plan.

[0074] The suggestion unit can suggest cost-effective menus based on the analysis results. For example, the suggestion unit suggests "healthy Japanese recipes using chicken and cabbage." This improves user satisfaction by suggesting cost-effective menus.

[0075] The calculation unit can calculate the cooking method and estimated cooking time according to the number of people input by the user. For example, if the user inputs "serves two," the calculation unit will provide information such as "it will take 20 minutes to make stir-fried chicken and cabbage." This allows for efficient cooking by calculating the cooking method and time according to the user's input.

[0076] The purchasing unit allows users to purchase ingredients within their monthly budget. For example, if a user inputs "My monthly food purchasing budget is 10,000 yen," the purchasing unit will purchase the optimal ingredients within that budget and suggest a menu. This allows users to purchase ingredients within their budget and live an economical diet.

[0077] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This improves ease of input by providing an interface that corresponds to the user's emotions.

[0078] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays ingredients and locations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest ingredients and locations to be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0079] When a user inputs information, the reception unit can automatically complete the input content based on the user's current health condition and dietary history. For example, if the user is on a diet, the reception unit can automatically display low-calorie ingredients as candidates. The reception unit can also complete the input content so that the user avoids ingredients to which the user has had an allergic reaction in the past. Furthermore, if the user wants to consume a specific nutrient, the reception unit can automatically suggest ingredients containing that nutrient. This reduces the effort required for input by completing the input content based on the user's health condition and dietary history.

[0080] The reception unit can estimate the user's emotions and determine the priority of input based on the estimated user's emotions. For example, when the user is in a hurry, the reception unit allows the user to input important information (location and health condition) with priority. When the user is relaxed, the reception unit can also allow the user to input detailed information (food preferences and desired health condition). Furthermore, when the user is feeling stressed, the reception unit can allow the user to input only the minimum amount of information. In this way, input priority is determined according to the user's emotions, improving input efficiency.

[0081] The reception unit can present regional ingredients and dishes as input candidates based on the user's geographical location information. For example, if the user lives in Hokkaido, the reception unit can display regional ingredients (e.g., salmon and potatoes) as candidates. Furthermore, if the user lives in Okinawa, the reception unit can also display regional dishes (e.g., bitter melon chanpuru) as candidates. Furthermore, if the user lives in Kyoto, the reception unit can also display regional ingredients (e.g., Kyoto vegetables) as candidates. By presenting regional ingredients and dishes as input candidates, it becomes possible to propose a menu suited to the region.

[0082] The reception unit can analyze the user's social media activity and prompt the user to input related ingredients and dishes. For example, the reception unit can display related ingredients as input candidates based on photos of dishes shared by the user on social media. The reception unit can also analyze posts from cooking accounts the user follows and display related dishes as input candidates. Furthermore, the reception unit can display related ingredients and dishes as input candidates based on posts of dishes that the user has "liked." This allows input that matches the user's interests by suggesting related ingredients and dishes based on social media activity.

[0083] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring sales information based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can acquire sales information at a normal timing. Also, if the user is in a hurry, the acquisition unit can instantly acquire sales information and provide it promptly. Furthermore, if the user is feeling stressed, the acquisition unit can delay acquisition of sales information to reduce stress. In this way, by adjusting the timing of acquiring sales information according to the user's emotions, it is possible to provide information at an appropriate time.

[0084] The acquisition unit can analyze past sales information and select an appropriate acquisition method. For example, the acquisition unit can acquire sales information for a specific day of the week or time period based on past sales information. The acquisition unit can also analyze past sales information and prioritize acquisition of sales information for specific stores. Furthermore, the acquisition unit can also prioritize acquisition of sales information for specific ingredients based on past sales information. This allows for efficient information acquisition by selecting the optimal acquisition method based on past sales information.

[0085] When acquiring sale information, the acquisition unit can perform filtering based on the user's current purchase history and areas of interest. For example, the acquisition unit prioritizes acquiring sale information for ingredients that the user has purchased in the past. The acquisition unit can also prioritize acquiring sale information for ingredients or dishes in which the user has shown interest. Furthermore, the acquisition unit can filter and provide related sale information based on the user's purchase history. In this way, highly relevant information can be provided by filtering based on the purchase history and areas of interest.

[0086] The acquisition unit can estimate the user's emotions and determine the priority of sales information to be acquired based on the estimated user emotions. For example, when the user is in a hurry, the acquisition unit can prioritize acquiring the most important sales information. Furthermore, when the user is relaxed, the acquisition unit can also acquire detailed sales information. Furthermore, when the user is feeling stressed, the acquisition unit can prioritize acquiring specific sales information to reduce stress. In this way, by determining the priority of sales information according to the user's emotions, important information can be provided preferentially.

[0087] When acquiring sales information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit can prioritize acquiring sales information for the area where the user lives. The acquisition unit can also prioritize acquiring sales information for stores that the user frequently visits. Furthermore, the acquisition unit can prioritize acquiring sales information for the nearest store based on the user's geographical location information. This makes it possible to provide information that is appropriate for the area by acquiring highly relevant information by taking into account the geographical location information.

[0088] When acquiring sale information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit acquires sale information related to ingredients or dishes shared by the user on social media. The acquisition unit can also prioritize acquisition of sale information for stores or brands that the user follows. Furthermore, the acquisition unit can also acquire sale information related to posts that the user has "liked." In this way, by acquiring related information based on social media activity, it is possible to provide information that matches the user's interests.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can relax certain criteria and perform an analysis when the user is feeling stressed in order to reduce stress. In this way, by adjusting the analysis criteria according to the user's emotions, more appropriate analysis results can be provided.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between ingredients. For example, the analysis unit can propose an optimal menu by taking into account the interrelationships between nutrients in ingredients. The analysis unit can also propose a balanced menu by taking into account the interrelationships between the flavors of ingredients. Furthermore, the analysis unit can propose an efficient menu by taking into account the interrelationships between cooking methods of ingredients. In this way, by taking into account the interrelationships between ingredients, more accurate analysis results can be provided.

[0091] The analysis unit can perform the analysis while taking into account the user's attribute information. The analysis unit can propose an optimal menu, for example, taking into account the user's age and gender. The analysis unit can also propose a healthy menu, taking into account the user's health condition. Furthermore, the analysis unit can also propose a menu that will provide high satisfaction, taking into account the user's food preferences. This makes it possible to provide individually optimized analysis results by taking into account the user's attribute information.

[0092] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. For example, when the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results. In addition, when the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, when the user is feeling stressed, the analysis unit can prioritize displaying specific analysis results to reduce stress. In this way, important information can be provided preferentially by adjusting the display order according to the user's emotions.

[0093] The analysis unit can perform the analysis while taking into account the geographical distribution of ingredients. For example, the analysis unit can propose an optimal menu by taking into account ingredients specific to a region. The analysis unit can also propose an efficient menu by taking into account ingredients that are easily available in a geographical area. Furthermore, the analysis unit can also propose a healthy menu by taking into account ingredients that are fresh in a geographical area. In this way, by taking into account the geographical distribution of ingredients, it is possible to provide analysis results that are appropriate for a region.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature. For example, the analysis unit can refer to the latest nutritional research to suggest healthy menus. The analysis unit can also refer to literature on cooking methods to suggest efficient menus. Furthermore, the analysis unit can refer to literature on ingredient combinations to suggest balanced menus. In this way, by referring to related literature, more accurate analysis results can be provided.

[0095] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit can also make suggestions that are concise and to the point. Furthermore, if the user is feeling stressed, the suggestion unit can make suggestions using softer expressions to reduce stress. In this way, by adjusting the way the suggestions are expressed according to the user's emotions, the likelihood of the suggestions being accepted increases.

[0096] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the ingredients when making a suggestion. For example, the suggestion unit can suggest detailed cooking methods for main ingredients. The suggestion unit can also suggest simple cooking methods for supplementary ingredients. Furthermore, the suggestion unit can provide detailed information for ingredients in which the user is particularly interested. In this way, by adjusting the level of detail of the suggestion based on the importance of the ingredients, it is possible to provide important information to the user.

[0097] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients. For example, the suggestion unit makes suggestions that emphasize nutritional value for vegetables. The suggestion unit can also make suggestions that emphasize cooking time for meat. Furthermore, the suggestion unit can also make suggestions that emphasize freshness for seafood. This makes it possible to make more appropriate suggestions by applying suggestion algorithms according to the category of ingredients.

[0098] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make longer suggestions with detailed explanations. Furthermore, if the user is feeling stressed, the suggestion unit can also make concise, easy-to-understand suggestions to reduce stress. In this way, adjusting the length of the suggestions according to the user's emotions increases the likelihood of the suggestions being accepted.

[0099] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the ingredients. For example, the proposal unit can give priority to proposals that use ingredients that are close to their expiration date. The proposal unit can also give priority to proposals that use fresh ingredients. Furthermore, the proposal unit can also give priority to proposals that use seasonal ingredients. In this way, by determining the priority of proposals based on the time of submission of ingredients, proposals can be made at an appropriate time.

[0100] The suggestion unit can adjust the order of suggestions based on the relevance of ingredients when suggesting ingredients. For example, the suggestion unit can suggest ingredients for the main dish first, followed by ingredients for the side dish. The suggestion unit can also suggest ingredients that use the same cooking method together. Furthermore, the suggestion unit can suggest related ingredients in an order taking nutritional balance into consideration. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of ingredients.

[0101] The calculation unit can estimate the user's emotions and adjust the calculation method for the cooking method and time based on the estimated user's emotions. For example, if the user is relaxed, the calculation unit can calculate a detailed cooking method and time. If the user is in a hurry, the calculation unit can also calculate a simplified cooking method and a method that can cook in a short time. Furthermore, if the user is feeling stressed, the calculation unit can calculate a simple and quick cooking method to reduce stress. In this way, by adjusting the calculation method according to the user's emotions, it is possible to provide a more appropriate cooking method and time.

[0102] When calculating, the calculation unit can analyze the user's past cooking history and select the optimal calculation method. The calculation unit calculates the optimal cooking method, for example, based on the cooking methods used by the user in the past. The calculation unit can also calculate a method for shortening cooking time from the user's past cooking history. Furthermore, the calculation unit can analyze the user's past cooking history and calculate the most efficient cooking method. In this way, an efficient cooking method can be provided by selecting the optimal calculation method based on the past cooking history.

[0103] The calculation unit can customize the calculation method based on the user's current living situation when making the calculation. For example, if the user is busy, the calculation unit calculates a cooking method that can be done in a short amount of time. Furthermore, if the user values ​​health, the calculation unit can also calculate a healthy cooking method. Furthermore, if the user eats a meal with their family, the calculation unit can also calculate a cooking method that will satisfy the whole family. In this way, by customizing the calculation method based on the user's current living situation, the optimal cooking method can be provided to the user.

[0104] The calculation unit can estimate the user's emotions and determine the priority of calculation results based on the estimated user's emotions. For example, when the user is in a hurry, the calculation unit can prioritize displaying the most important calculation results. Furthermore, when the user is relaxed, the calculation unit can also display detailed calculation results. Furthermore, when the user is feeling stressed, the calculation unit can prioritize displaying specific calculation results to reduce stress. In this way, by prioritizing the calculation results according to the user's emotions, important information can be provided preferentially.

[0105] During calculation, the calculation unit can select the optimal calculation method by taking into account the user's geographical location information. For example, the calculation unit calculates the optimal cooking method by taking into account ingredients in the area where the user lives. The calculation unit can also calculate the optimal cooking method by taking into account ingredients at stores frequently visited by the user. Furthermore, the calculation unit can calculate the most efficient cooking method based on the user's geographical location information. In this way, by selecting the optimal calculation method by taking into account the geographical location information, it is possible to provide a cooking method that is suitable for the area.

[0106] During the calculation, the calculation unit can analyze the user's social media activity and suggest a calculation method. For example, the calculation unit calculates the optimal cooking method based on cooking methods shared by the user on social media. The calculation unit can also analyze posts from cooking accounts the user follows and calculate related cooking methods. Furthermore, the calculation unit can calculate the optimal cooking method based on cooking posts that the user has "liked." In this way, by suggesting a calculation method based on social media activity, it is possible to provide a cooking method that matches the user's interests.

[0107] The purchasing unit can estimate the user's emotions and adjust the purchasing method based on the estimated user's emotions. For example, if the user is relaxed, the purchasing unit can suggest a detailed purchasing method. Also, if the user is in a hurry, the purchasing unit can suggest a simplified purchasing method. Furthermore, if the user is feeling stressed, the purchasing unit can suggest an easy and quick purchasing method to reduce stress. In this way, by adjusting the purchasing method according to the user's emotions, a more appropriate purchasing method can be provided.

[0108] At the time of purchase, the purchasing unit can analyze the user's past purchasing history and select the optimal purchasing method. The purchasing unit can suggest the optimal purchasing method, for example, based on ingredients the user has purchased in the past. The purchasing unit can also suggest an efficient purchasing method based on the user's past purchasing history. Furthermore, the purchasing unit can analyze the user's past purchasing history and suggest the most cost-effective purchasing method. This allows for efficient purchasing by selecting the optimal purchasing method based on the user's past purchasing history.

[0109] The purchasing unit can customize the purchasing method based on the user's current living situation at the time of purchase. For example, if the user is busy, the purchasing unit can suggest an online purchasing method. Also, if the user values ​​health, the purchasing unit can suggest a method for prioritizing the purchase of healthy ingredients. Furthermore, if the user eats with their family, the purchasing unit can suggest a method for purchasing ingredients that will satisfy the whole family. In this way, by customizing the purchasing method based on the user's current living situation, the optimal purchasing method can be provided to the user.

[0110] The purchasing unit can estimate the user's emotions and determine purchasing priorities based on the estimated user emotions. For example, if the user is in a hurry, the purchasing unit prioritizes purchasing the most important ingredients. Also, if the user is relaxed, the purchasing unit can create a detailed purchasing plan. Furthermore, if the user is feeling stressed, the purchasing unit can suggest an easy and quick purchasing method to reduce stress. In this way, by determining purchasing priorities according to the user's emotions, important ingredients can be purchased preferentially.

[0111] At the time of purchase, the purchasing unit can select the optimal purchasing method by taking into account the user's geographical location information. The purchasing unit can, for example, suggest the optimal purchasing method by taking into account stores in the area where the user lives. The purchasing unit can also suggest the optimal purchasing method by taking into account sales information at stores frequently visited by the user. Furthermore, the purchasing unit can also suggest the most efficient purchasing method based on the user's geographical location information. In this way, by selecting the optimal purchasing method by taking into account the geographical location information, a purchasing method suited to the area can be provided.

[0112] At the time of purchase, the purchasing unit can analyze the user's social media activity and suggest purchasing methods. For example, the purchasing unit can suggest purchasing methods related to ingredients or dishes that the user has shared on social media. The purchasing unit can also suggest the optimal purchasing method by taking into account sales information from stores and brands that the user follows. Furthermore, the purchasing unit can also suggest methods for purchasing ingredients related to posts that the user has "liked." In this way, by suggesting purchasing methods based on social media activity, it is possible to provide purchasing methods that match the user's interests. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, acquisition unit, analysis unit, suggestion unit, calculation unit, and purchase unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, and inputs information such as the ingredients purchased, location, rice preferences, and desired health status of the user. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12, and acquires sales information around the user's current location. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the input information and the acquired sales information. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, and proposes cost-effective menus based on the analysis results. The calculation unit is implemented by the specific processing unit 290 of the data processing device 12, and calculates cooking methods and estimated cooking times according to the number of servings input by the user. The purchase unit is implemented by the specific processing unit 290 of the data processing device 12, and purchases ingredients monthly within a budget. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, acquisition unit, analysis unit, suggestion unit, calculation unit, and purchase unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and inputs information such as the ingredients purchased by the user, their location, food preferences, and desired health status. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires sales information around the user's current location. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information and the acquired sales information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a cost-effective menu based on the analysis results. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and calculates cooking methods and estimated cooking times according to the number of servings input by the user. The purchase unit is realized by the specific processing unit 290 of the data processing device 12, and purchases ingredients monthly within a budget. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, acquisition unit, analysis unit, proposal unit, calculation unit, and purchase unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314, and inputs information such as the ingredients purchased by the user, their location, food preferences, and desired health status. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires sales information around the user's current location. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information and the acquired sales information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes cost-effective menus based on the analysis results. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and calculates cooking methods and estimated cooking times corresponding to the number of servings input by the user. The purchase unit is realized by the specific processing unit 290 of the data processing device 12, and purchases ingredients monthly within a budget. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, acquisition unit, analysis unit, proposal unit, calculation unit, and purchase unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and inputs information such as the ingredients purchased by the user, their location, food preferences, and desired health status. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires sales information around the user's current location. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input information and the acquired sales information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes a cost-effective menu based on the analysis results. The calculation unit is realized by the specific processing unit 290 of the data processing device 12, and calculates cooking methods and estimated cooking times according to the number of servings input by the user. The purchase unit is realized by the specific processing unit 290 of the data processing device 12, and purchases ingredients monthly within a budget.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The reception unit can input the user's food preferences and allergy information. For example, the user can input information such as "I don't like green peppers" or "I'm allergic to nuts." This allows the system to propose menus that take the user's preferences and allergies into consideration. The reception unit can also input the user's meal frequency and meal times. For example, the user can input information such as "three meals a day," "breakfast at 7:00, lunch at 12:00, and dinner at 19:00." This allows the system to propose menus that match the user's meal times. Furthermore, the reception unit can also input the user's food storage method and storage period. For example, the user can input information such as "store in the refrigerator" and "storage period is one week." This allows the system to propose menus that match the user's food storage conditions.

[0115] The acquisition unit can prioritize acquisition of sale information for ingredients purchased in the past based on the user's purchasing history. For example, sale information for ingredients that the user has frequently purchased in the past can be prioritized. This makes it possible to efficiently acquire sale information for ingredients that the user frequently purchases. The acquisition unit can also filter sale information based on the user's areas of interest. For example, sale information for ingredients or dishes in which the user has shown interest can be prioritized. This makes it possible to provide sale information that matches the user's interests. Furthermore, the acquisition unit can filter and provide related sale information based on the user's purchasing history. For example, sale information related to ingredients that the user has purchased in the past can be prioritized. This makes it possible to provide highly relevant sale information.

[0116] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a simplified analysis can be performed. Furthermore, if the user is feeling stressed, the analysis can be performed by relaxing certain criteria to reduce stress. In this way, by adjusting the analysis criteria according to the user's emotions, more appropriate analysis results can be provided. For example, a relaxed user can be provided with detailed nutrient analysis results, while a user in a hurry can be provided with simple nutritional balance suggestions. Furthermore, a user who is feeling stressed can be provided with suggestions for ingredients that will help reduce stress.

[0117] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients. For example, suggestions can be made that emphasize nutritional value for vegetables. For meat, suggestions can be made that emphasize cooking time. For seafood, suggestions can be made that emphasize freshness. This makes it possible to make more appropriate suggestions by applying suggestion algorithms according to the category of ingredients. For example, when suggesting vegetables, emphasis can be placed on the vitamin and mineral content, and when suggesting meat, priority can be given to recipes that can be cooked in a short time. For seafood, suggestions can also be made on cooking methods and storage methods to maintain freshness. This makes it possible to make optimal suggestions according to the user's needs.

[0118] The calculation unit can estimate the user's emotions and adjust the calculation method for the cooking method and time based on the estimated user's emotions. For example, if the user is relaxed, a detailed cooking method and time can be calculated. Furthermore, if the user is in a hurry, a simplified cooking method and a method that can be cooked in a short time can be calculated. Furthermore, if the user is feeling stressed, a simple and quick cooking method can be calculated to reduce stress. In this way, by adjusting the calculation method according to the user's emotions, more appropriate cooking methods and times can be provided. For example, a detailed step-by-step cooking method can be provided to a user who is relaxed, and a time-saving recipe can be suggested to a user who is in a hurry. Furthermore, an easy and error-free cooking method can be suggested to a user who is feeling stressed.

[0119] The purchasing unit can estimate the user's emotions and adjust the purchasing method based on the estimated user's emotions. For example, if the user is relaxed, a detailed purchasing method can be suggested. Furthermore, if the user is in a hurry, a simplified purchasing method can be suggested. Furthermore, if the user is feeling stressed, an easy and quick purchasing method can be suggested to reduce stress. In this way, by adjusting the purchasing method according to the user's emotions, a more appropriate purchasing method can be provided. For example, detailed purchasing procedures and recommended purchasing locations can be suggested to a relaxed user, and the shortest purchasing method can be suggested to a user in a hurry. Furthermore, an easy and quick purchasing method to reduce stress can be suggested to a user feeling stressed.

[0120] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display ingredients and locations that the user has frequently input in the past as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ingredients and locations to be used in a specific time period from the user's past input history. This improves input efficiency by suggesting the optimal input method based on the past input history. For example, it can reduce the effort required for input by displaying ingredients that the user has frequently purchased in the past as candidates and prioritizing suggestions of input methods that have been used in the past. It can also predict and suggest ingredients to be used in a specific time period, enabling more efficient input.

[0121] The acquisition unit can analyze past sales information and select an appropriate acquisition method. For example, it can acquire sales information for a specific day of the week or time period based on past sales information. It can also analyze past sales information and prioritize acquisition of sales information for a specific store. It can also prioritize acquisition of sales information for a specific food ingredient based on past sales information. This enables efficient information acquisition by selecting the optimal acquisition method based on past sales information. For example, acquiring sales information for a specific day of the week or time period based on past sales information enables efficient information collection. It can also provide useful information to users by prioritizing acquisition of sales information for a specific store. It can also provide information that meets the needs of users by prioritizing acquisition of sales information for a specific food ingredient.

[0122] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between ingredients. For example, it can propose an optimal menu by taking into account the interrelationships between nutrients in ingredients. It can also propose a balanced menu by taking into account the interrelationships between flavors of ingredients. It can also propose an efficient menu by taking into account the interrelationships between cooking methods of ingredients. In this way, by taking into account the interrelationships between ingredients, it is possible to provide more accurate analysis results. For example, it can propose a menu with a balanced amount of vitamins and minerals by taking into account the interrelationships between nutrients. It can also propose a menu with a harmonious taste by taking into account the interrelationships between flavors. It can also propose a menu that can be cooked efficiently by taking into account the interrelationships between cooking methods.

[0123] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide a detailed explanation. If the user is in a hurry, the suggestion unit can provide a concise and to-the-point suggestion. If the user is feeling stressed, the suggestion unit can provide a softer-spoken suggestion to reduce stress. This adjusts the way the suggestions are expressed according to the user's emotions, thereby improving the likelihood of the suggestions being accepted. For example, a relaxed user can be provided with a detailed step-by-step explanation, while a user in a hurry can be provided with a concise and to-the-point suggestion. If the user is feeling stressed, the suggestion unit can provide a softer-spoken suggestion to improve the likelihood of the suggestions being accepted.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The reception unit inputs information such as the ingredients the user is purchasing, their location, their rice preferences, and the health status they are aiming for. For example, a user can input information such as "I buy chicken, cabbage, and carrots," "I live in Tokyo," "I like Japanese food," and "I'm on a diet." Step 2: The acquisition unit acquires sales information for the area around the user's current location. For example, it acquires sales information for supermarkets in the user's area and checks whether chicken or cabbage is on sale. Step 3: The analysis unit analyzes the input information and the acquired sales information. For example, it combines the ingredients the user is purchasing with the sales information to suggest cost-effective meals. Step 4: The proposal team will propose cost-effective menus based on the analysis results. For example, they will suggest a "healthy Japanese recipe using chicken and cabbage." Step 5: The calculation unit calculates the cooking method and estimated cooking time according to the number of servings entered by the user. For example, if the user enters "2 servings," it will provide information such as "It will take 20 minutes to make stir-fried chicken and cabbage." Step 6: The purchasing department purchases ingredients within the monthly budget. For example, if the user inputs "My monthly ingredient budget is 10,000 yen," the purchasing department will purchase the optimal ingredients within that budget and suggest a menu.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting information; an acquisition unit that acquires sale information based on the information input by the reception unit; an analysis unit that analyzes the information acquired by the acquisition unit; a suggestion unit that suggests a menu based on the analysis results obtained by the analysis unit; a calculation unit that calculates a cooking method or time based on the menu proposed by the proposal unit; a purchasing unit that purchases ingredients within the budget based on the information calculated by the calculation unit. A system characterized by:

2. The reception unit Enter information about the ingredients the user purchases, their location, food preferences, and the health status they want to achieve. The system of claim 1 .

3. The acquisition unit Get sales information around your current location The system of claim 1 .

4. The analysis unit Analyze the entered information and the acquired sales information The system of claim 1 .

5. The proposal unit Propose cost-effective menus based on analysis results The system of claim 1 .

6. The calculation unit Calculate cooking methods and estimated cooking times according to the number of people entered by the user The system of claim 1 .

7. The purchasing department Purchase food each month within your budget The system of claim 1 .

8. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions. The system of claim 1 .

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