system

Abstract

The system of the embodiment aims to generate optimal menus based on an individual's goals and budget, and to provide a shopping list, order, and delivery service all in one. [Solution] The system according to the embodiment includes a reception unit, a menu creation unit, a shopping list providing unit, an ordering unit, and a delivery unit. The reception unit accepts input of an individual's purpose and budget. The menu creation unit creates an appropriate menu based on the information accepted by the reception unit. The shopping list providing unit provides a shopping list based on the menu created by the menu creation unit. The ordering unit places an order in cooperation with an online supermarket based on the shopping list provided by the shopping list providing unit. The delivery unit delivers the ingredients ordered by the ordering 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 the drawback of being difficult to generate optimal menus based on an individual's goals and budget, and to provide a shopping list, order, and deliver the food consistently.

[0005] The system of the embodiment aims to generate optimal menus based on an individual's goals and budget, and to provide a shopping list, order, and delivery service all in one. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a menu creation unit, a shopping list providing unit, an ordering unit, and a delivery unit. The reception unit receives input of an individual's purpose and budget. The menu creation unit creates an appropriate menu based on the information received by the reception unit. The shopping list providing unit provides a shopping list based on the menu created by the menu creation unit. The ordering unit places an order in cooperation with an online supermarket based on the shopping list provided by the shopping list providing unit. The delivery unit delivers the ingredients ordered by the ordering unit. [Effects of the Invention]

[0007] The system according to the embodiment generates optimal menus according to an individual's purpose and budget, and can provide shopping lists, orders, and even deliveries. [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 personalized menu provision system according to an embodiment of the present invention automatically generates menus tailored to individual needs and provides recipes. This system generates optimal menus based on input of an individual's goals (e.g., dieting) and budget. The generated menus consider nutritional balance and utilize seasonal ingredients. It also provides shopping lists that take food waste into consideration and allows ordering and delivery through online supermarkets. It also offers the option to rent cooking appliances. This system aims to reduce time and effort, enjoy healthy, balanced meals, and contribute to reducing food waste and the global environment. For example, a user can input their goals and budget, such as dieting or maintaining health, and their budget. For example, they might input "My weekly budget for dieting is 5,000 yen." This information is then entered into an AI. The AI ​​then analyzes the input information and generates optimal menus. The AI ​​generates menus tailored to individual needs, taking into account nutritional balance and seasonal ingredients. For example, if the goal is dieting, a low-calorie, nutritionally balanced menu is generated. A shopping list is provided based on the generated menu. The AI ​​lists the ingredients needed for the generated menu and provides a shopping list that takes food waste into consideration. For example, the list can be created to use up a week's worth of ingredients without waste. It also supports ordering and delivery through online supermarkets. Users can order from the online supermarket based on the provided shopping list and have the ingredients delivered to their home, eliminating the need for shopping. The system also offers the option to rent cooking appliances. Users can rent cooking appliances as needed to cook more efficiently. For example, they can rent slow cookers and food processors. This system saves time and effort, allowing users to enjoy healthy, balanced meals while contributing to reducing food waste and protecting the global environment. This allows the personalized menu system to automatically generate menus and provide recipes tailored to individual needs.

[0029] A personalized menu providing system according to an embodiment includes a reception unit, a menu creation unit, a shopping list providing unit, an ordering unit, and a delivery unit. The reception unit accepts input of a personal purpose and budget. Examples of personal purposes include, but are not limited to, dieting, maintaining health, and saving money. Examples of budgets include, but are not limited to, a monthly budget, a weekly budget, and a budget per meal. For example, the reception unit accepts a user's input such as, "I'm dieting, so my weekly budget is 5,000 yen." The menu creation unit creates an optimal menu based on the information accepted by the reception unit. The menu creation unit generates a menu that takes into account, for example, nutritional balance and utilizes seasonal ingredients. Examples of nutritional balance include, but are not limited to, recommended daily intakes and the proportions of specific nutrients. Examples of seasonal ingredients include, but are not limited to, seasonal ingredient lists and regional seasonal ingredients. For example, in the case of dieting, the menu creation unit generates a low-calorie, nutritionally balanced menu. The shopping list providing unit provides a shopping list based on the menu generated by the menu generating unit. The shopping list providing unit, for example, lists ingredients needed for the generated menu and provides a shopping list that takes food waste into consideration. Food waste includes, for example, but is not limited to, the type, amount, and cause of discarded ingredients. The shopping list providing unit creates a list, for example, to use up a week's worth of ingredients without waste. The ordering unit places an order in cooperation with an online supermarket based on the shopping list provided by the shopping list providing unit. The ordering unit enables, for example, a user to place an order with the online supermarket based on the provided shopping list and have the ingredients delivered to their home. Online supermarkets include, for example, specific online stores and regionally limited services, but are not limited to these examples. The delivery unit delivers the ingredients ordered by the ordering unit. For example, the delivery unit delivers the ingredients to a location specified by the user via a delivery company. Delivery includes, for example, but is not limited to, the type of delivery company, delivery time, delivery area, etc.As a result, the personalized menu provision system according to the embodiment can generate optimal menus based on an individual's goals and budget, and can provide shopping lists, orders, and even deliver the food to your home in an integrated manner.

[0030] Furthermore, the personalized menu providing system includes a rental unit that provides rental options for cooking appliances. The rental unit provides rental options for cooking appliances. Examples of cooking appliances include, but are not limited to, ovens, microwaves, rice cookers, slow cookers, and food processors. The rental unit, for example, enables a user to rent cooking appliances as needed and cook efficiently. For example, the rental unit can rent cooking appliances such as slow cookers and food processors. Thus, by providing rental options for cooking appliances, the user can cook efficiently.

[0031] The menu generation unit can generate a menu based on nutritional balance and utilizing seasonal ingredients. Nutritional balance includes, for example, recommended daily intakes and the proportions of specific nutrients, but is not limited to these examples. Seasonal ingredients include, for example, seasonal ingredient lists and ingredients that are in season by region, but are not limited to these examples. For example, in the case of dieting, the menu generation unit generates a low-calorie, nutritionally balanced menu. For example, in the case where emphasis is placed on nutritional balance, the menu generation unit provides a menu that includes detailed nutritional information. For example, in the case where emphasis is placed on seasonal ingredients, the menu generation unit provides a menu that uses ingredients that are in season. For example, the menu generation unit provides a menu that emphasizes specific nutrients based on the user's health condition. In this way, healthy, seasonal menus can be provided by taking nutritional balance into consideration and utilizing seasonal ingredients.

[0032] The shopping list providing unit can provide a shopping list based on food waste. Food waste includes, for example, the type, amount, and cause of discarded ingredients, but is not limited to these examples. For example, the shopping list providing unit creates a list to use up a week's worth of ingredients without waste. For example, the shopping list providing unit includes the minimum amount of ingredients needed in the list to reduce food waste. For example, the shopping list providing unit includes in the list how to store ingredients to reduce food waste. For example, the shopping list providing unit includes in the list how to use up ingredients to reduce food waste. In this way, by providing a shopping list that takes food waste into consideration, it is possible to reduce food waste.

[0033] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates the purpose and budget that the user has frequently input in the past. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the purpose and budget to be used in a specific time period from the user's past input history. The past input history includes, for example, a history of input purpose and budget, an analysis algorithm, etc., but is not limited to these examples. In this way, the optimal input method for the user can be suggested by analyzing the past input history.

[0034] The reception unit can perform filtering based on the user's current health condition and lifestyle when inputting the purpose and budget. The reception unit, for example, suggests an appropriate budget range based on the user's health condition. The reception unit, for example, suggests a menu that can be easily prepared based on the user's lifestyle (e.g., busy daily life). The reception unit, for example, refers to the user's health data and suggests the optimal purpose for maintaining health. The health condition includes, for example, weight, blood pressure, medical history, etc., but is not limited to such examples. The lifestyle situation includes, for example, lifestyle habits, family composition, work situation, etc. In this way, by filtering based on the user's health condition and lifestyle situation, more appropriate purpose and budget can be suggested.

[0035] When inputting a purpose and budget, the reception unit can prioritize inputting relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit prioritizes suggesting ingredients available in that area. For example, the reception unit suggests a budget that reflects price information at nearby supermarkets based on the user's location information. For example, the reception unit suggests a purpose that matches the local food culture based on the user's geographical location information. Geographical location information includes, for example, GPS data, address information, etc., but is not limited to these examples. This makes it possible to provide information appropriate to the area by taking into account the user's geographical location information.

[0036] When inputting a purpose and budget, the reception unit can analyze the user's social media activity and input relevant information. The reception unit, for example, suggests related purposes based on photos of meals shared by the user on social media. The reception unit, for example, reflects ingredients and dishes of interest from the user's social media activity in the budget. The reception unit, for example, analyzes the user's social media activity and suggests purposes that match trends. Social media activity includes, for example, post content, number of likes, number of followers, etc., but is not limited to these examples. In this way, by analyzing social media activity, it is possible to provide information that matches the user's interests.

[0037] When generating a menu, the menu generation unit can set the level of detail of the menu based on nutritional balance and the importance of seasonal ingredients. For example, if emphasis is placed on nutritional balance, the menu generation unit provides a menu including detailed nutritional information. For example, if emphasis is placed on seasonal ingredients, the menu generation unit provides a menu using ingredients that match the season. For example, based on the user's health condition, the menu generation unit provides a menu that emphasizes specific nutrients. The level of detail of the menu includes, but is not limited to, the number of recipe steps, detailed information about ingredients, and the like. In this way, by adjusting the level of detail of the menu based on nutritional balance and the importance of seasonal ingredients, it is possible to provide an optimal menu for the user.

[0038] When generating a menu, the menu generation unit can apply a different menu algorithm depending on the user's health condition and ingredient allergy information. For example, if the user has a specific allergy, the menu generation unit provides a menu that excludes that ingredient. For example, the menu generation unit provides a menu that emphasizes specific nutrients based on the user's health condition. For example, the menu generation unit references the user's health data and provides a menu that is optimal for maintaining health. Allergy information includes, for example, allergies to specific ingredients, the severity of the allergy, and the like, but is not limited to such examples. In this way, by applying a menu algorithm depending on the user's health condition and allergy information, it is possible to provide a safe and appropriate menu for the user.

[0039] When generating a menu, the menu generation unit can determine the ranking of menus based on when ingredients were obtained. The menu generation unit, for example, provides a menu that prioritizes the use of seasonal ingredients. The menu generation unit, for example, provides a menu that uses ingredients that can be stored based on when ingredients were obtained. The menu generation unit, for example, provides a menu that matches a specific season based on when ingredients were obtained. The time when ingredients are obtained includes, for example, the time when ingredients are available by season, the time when ingredients are available by region, etc., but is not limited to such examples. In this way, by determining the priority of menus based on when ingredients are obtained, it is possible to provide a menu that makes use of seasonal ingredients.

[0040] When generating a menu, the menu generation unit can set the order of the menu based on the relevance of ingredients. For example, the menu generation unit provides dishes using the same ingredients consecutively to reduce ingredient waste. For example, the menu generation unit provides a menu that reduces cooking effort based on the relevance of ingredients. For example, the menu generation unit provides a balanced menu based on the relevance of ingredients. Examples of ingredient relevance include, but are not limited to, ingredient combinations and common cooking methods. In this way, by adjusting the order of the menu based on the relevance of ingredients, ingredient waste can be reduced and efficient cooking can be achieved.

[0041] When providing a shopping list, the shopping list providing unit can set the level of detail of the list taking food waste into consideration. For example, the shopping list providing unit includes the minimum necessary ingredients in the list to reduce food waste. For example, the shopping list providing unit includes in the list how to store ingredients to reduce food waste. For example, the shopping list providing unit includes in the list how to use up ingredients to reduce food waste. The level of detail of the list includes, for example, detailed information about ingredients, detailed information about where to purchase them, etc., but is not limited to these examples. In this way, by adjusting the level of detail of the list taking food waste into consideration, it is possible to reduce food waste.

[0042] When providing a shopping list, the shopping list providing unit can generate an appropriate list by referring to the user's past purchase history. The shopping list providing unit generates an optimal list, for example, based on ingredients purchased by the user in the past. The shopping list providing unit, for example, predicts necessary ingredients from the user's past purchase history and includes them in the list. The shopping list providing unit, for example, analyzes the user's past purchase history to generate an efficient list. The past purchase history includes, for example, but is not limited to, the type, quantity, and purchase frequency of ingredients purchased. In this way, by referring to the past purchase history, the optimal shopping list for the user can be provided.

[0043] When providing a shopping list, the shopping list providing unit can prioritize relevant lists by taking into account the user's geographical location information. For example, the shopping list providing unit includes ingredients available at nearby supermarkets in the list based on the user's location information. For example, the shopping list providing unit includes local specialties in the list based on the user's location information. For example, the shopping list providing unit suggests an optimal shopping route based on the user's location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. In this way, a shopping list suitable for the region can be provided by taking into account the user's geographical location information.

[0044] When providing a shopping list, the shopping list providing unit can analyze the user's social media activity and provide a relevant list. For example, the shopping list providing unit includes necessary ingredients in the list based on recipes shared by the user on social media. For example, the shopping list providing unit includes ingredients of interest to the user based on the user's social media activity. For example, the shopping list providing unit analyzes the user's social media activity and provides a list tailored to trends. Social media activity includes, for example, post content, number of likes, number of followers, etc., but is not limited to these examples. In this way, by analyzing social media activity, a shopping list tailored to the user's interests can be provided.

[0045] The ordering unit can set the level of detail of the order based on the importance of the product when placing an order. For example, the ordering unit provides detailed information for important products and prompts the user to confirm the order. For example, the ordering unit provides concise information for less important products, allowing the user to complete the order quickly. The ordering unit processes the order with priority based on the importance of the product. The importance of the product includes, but is not limited to, importance based on the user's needs and frequency of use of the product. In this way, by adjusting the level of detail of the order based on the importance of the product, it is possible to provide products that are important to the user with priority.

[0046] When placing an order, the ordering unit can apply different ordering algorithms depending on the product category. For example, for fresh food, the ordering unit applies an ordering algorithm that emphasizes freshness. For example, for preserved food, the ordering unit applies an ordering algorithm that takes stock status into consideration. For example, for cooking appliances, the ordering unit applies an ordering algorithm that includes a rental option. Product categories include, but are not limited to, food, daily necessities, and home appliances. This allows optimal order processing by applying different ordering algorithms depending on the product category.

[0047] When placing an order, the ordering unit can determine the order priority based on the availability of the product. For example, the ordering unit processes orders for products that are needed urgently as a priority. For example, the ordering unit processes orders for products that will be available later at a later date. For example, the ordering unit proposes an optimal ordering schedule based on the availability of the product. The availability of the product includes, but is not limited to, seasonal product availability and regional product availability. In this way, by determining the order priority based on the availability of the product, it is possible to provide the required product at the appropriate time.

[0048] The ordering unit can set the order sequence based on the relevance of products when placing an order. For example, the ordering unit orders products from the same category consecutively to process efficiently. For example, the ordering unit processes the order in an order that reduces cooking effort based on the relevance of products. For example, the ordering unit provides a balanced order based on the relevance of products. Product relevance includes, but is not limited to, product combinations, common usage methods, and the like. This allows for efficient order processing by adjusting the order sequence based on the relevance of products.

[0049] When delivering goods, the delivery unit can select an appropriate delivery method by referring to the user's past delivery history. The delivery unit, for example, suggests an optimal method based on delivery methods used by the user in the past. The delivery unit, for example, suggests a delivery method that avoids congestion based on the user's past delivery history. The delivery unit, for example, analyzes the user's past delivery history and suggests the most efficient delivery method. The past delivery history includes, for example, the type, quantity, and delivery frequency of delivered goods. In this way, by referring to the past delivery history, the optimal delivery method for the user can be provided.

[0050] The delivery unit can set a delivery method based on the user's current living situation at the time of delivery. For example, if the user is busy, the delivery unit suggests a quick delivery method. For example, if the user is relaxed, the delivery unit provides a detailed delivery schedule. For example, the delivery unit suggests an optimal delivery method based on the user's living situation. Living situations include, but are not limited to, lifestyle habits, family composition, work situation, etc. In this way, by customizing the delivery method based on the user's living situation, it is possible to provide an optimal delivery method for the user.

[0051] The delivery unit can select an appropriate delivery method by taking into consideration the user's geographical location information when delivering. The delivery unit, for example, proposes an optimal delivery route based on the user's location information. The delivery unit, for example, provides a delivery method tailored to the characteristics of the area based on the user's location information. The delivery unit, for example, proposes an optimal delivery schedule based on the user's location information. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to these examples. In this way, a delivery method suited to the area can be provided by taking into consideration the user's geographical location information.

[0052] The delivery unit can analyze the user's social media activity and set the delivery method when delivering. The delivery unit, for example, suggests the optimal delivery method based on information shared by the user on social media. The delivery unit, for example, suggests a delivery method that is of interest to the user based on the user's social media activity. The delivery unit, for example, analyzes the user's social media activity and provides a delivery method that matches the trends. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. In this way, by analyzing social media activity, it is possible to provide a delivery method that matches the user's interests.

[0053] The rental unit can suggest an appropriate home appliance by referring to the user's past rental history at the time of rental. The rental unit suggests the most suitable home appliance, for example, based on the home appliances the user has rented in the past. The rental unit, for example, predicts and suggests the home appliances the user will need based on the user's past rental history. The rental unit, for example, analyzes the user's past rental history and suggests home appliances that do not require waste. The past rental history includes, for example, the type, quantity, and rental frequency of the home appliances rented, but is not limited to such examples. In this way, the most suitable home appliance can be provided to the user by referring to the past rental history.

[0054] The rental unit can set the type of home appliance based on the user's current cooking needs at the time of rental. For example, if the user wants to cook a specific dish, the rental unit suggests the home appliance that is best suited to that dish. For example, the rental unit customizes and suggests the necessary home appliance based on the user's cooking needs. For example, the rental unit refers to the user's cooking needs and suggests home appliances that allow efficient cooking. Cooking needs include, for example, the type of dish to be cooked, cooking frequency, cooking time, etc., but are not limited to such examples. In this way, by customizing the type of home appliance based on the user's cooking needs, it is possible to provide the user with the home appliance that is best suited to the user.

[0055] The rental unit can suggest an appropriate home appliance at the time of rental, taking into consideration the user's geographical location information. For example, the rental unit suggests home appliances available in the vicinity based on the user's location information. For example, the rental unit provides home appliances tailored to local characteristics based on the user's location information. For example, the rental unit suggests an optimal rental schedule based on the user's location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. This allows home appliances suited to the local area to be provided by taking into consideration the user's geographical location information.

[0056] At the time of rental, the rental department can analyze the user's social media activity to set the type of home appliance. For example, the rental department suggests the most suitable home appliance based on information shared by the user on social media. For example, the rental department suggests home appliances that the user is interested in based on the user's social media activity. For example, the rental department analyzes the user's social media activity and provides home appliances that match trends. Social media activity includes, for example, post content, number of likes, number of followers, etc., but is not limited to such examples. In this way, by analyzing social media activity, it is possible to provide home appliances that match the user's interests.

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

[0058] The reception unit can analyze the user's past meal history and suggest appropriate menus. For example, it can suggest similar menus based on the dishes and ingredients that the user has liked to eat in the past. It can also generate menus that do not include ingredients that the user has avoided in the past or allergy information, taking into account such ingredients. It can also analyze the nutritional balance from the user's meal history and suggest menus that supplement necessary nutrients. This makes it possible to provide more personalized menus by utilizing the user's past meal history.

[0059] The shopping list providing unit can analyze the user's purchasing history and generate an appropriate shopping list. For example, it can predict the ingredients needed based on ingredients purchased in the past by the user and include them in the list. Furthermore, it can also prioritize ingredients used frequently by considering the frequency of use of ingredients purchased in the past by the user. It can also generate a waste-free shopping list from the user's purchasing history and reduce food waste. This makes it possible to provide an efficient shopping list by utilizing the user's purchasing history.

[0060] The delivery unit can select an appropriate delivery method by taking into account the user's geographical location information. For example, it can propose an optimal delivery route based on the user's location information. Furthermore, it can also provide a delivery method that suits the characteristics of the area based on the user's location information. It can also propose an optimal delivery schedule based on the user's location information. In this way, it is possible to provide a delivery method that is suitable for the area by taking into account the user's geographical location information.

[0061] The reception unit can refer to the user's health data and suggest optimal goals for maintaining health. For example, it can suggest an appropriate diet plan and nutritionally balanced menu based on the user's health data, such as weight, blood pressure, and medical history. Furthermore, it can provide menus that emphasize specific nutrients based on the user's health condition. It can also analyze the user's health data and include ingredients necessary for maintaining health in the list. This makes it possible to utilize the user's health data to provide healthier menus.

[0062] The shopping list providing unit can provide a relevant list preferentially, taking into consideration the user's geographical location information. For example, ingredients available at nearby supermarkets can be included in the list based on the user's location information. Furthermore, local specialties can be included in the list based on the user's location information. It is also possible to suggest an optimal shopping route based on the user's location information. In this way, a shopping list suited to the area can be provided by taking into consideration the user's geographical location information.

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

[0064] Step 1: The reception unit accepts input of personal goals and budgets. Personal goals include dieting, maintaining health, saving money, etc., and budgets include monthly budgets, weekly budgets, and budgets per meal. For example, the reception unit accepts input from a user such as "My weekly budget for dieting is 5,000 yen." Step 2: The menu generator generates an optimal menu based on the information received by the receiver. The menu is generated taking into consideration nutritional balance and utilizing seasonal ingredients. For example, if the purpose is to lose weight, a low-calorie, nutritionally balanced menu is generated. Step 3: The shopping list provider provides a shopping list based on the menu generated by the menu generator. It lists the ingredients needed for the generated menu and provides a shopping list that takes food waste into consideration. For example, it creates a list that uses up a week's worth of ingredients without waste. Step 4: The ordering unit places an order in cooperation with the online supermarket based on the shopping list provided by the shopping list providing unit. This enables the user to place an order with the online supermarket based on the provided shopping list and have the ingredients delivered to their home. Step 5: The delivery department delivers the food ordered by the ordering department to the user's designated location via a delivery company.

[0065] (Example 2) A personalized menu provision system according to an embodiment of the present invention automatically generates menus tailored to individual needs and provides recipes. This system generates optimal menus based on input of an individual's goals (e.g., dieting) and budget. The generated menus consider nutritional balance and utilize seasonal ingredients. It also provides shopping lists that take food waste into consideration and allows ordering and delivery through online supermarkets. It also offers the option to rent cooking appliances. This system aims to reduce time and effort, enjoy healthy, balanced meals, and contribute to reducing food waste and the global environment. For example, a user can input their goals and budget, such as dieting or maintaining health, and their budget. For example, they might input "My weekly budget for dieting is 5,000 yen." This information is then entered into an AI. The AI ​​then analyzes the input information and generates optimal menus. The AI ​​generates menus tailored to individual needs, taking into account nutritional balance and seasonal ingredients. For example, if the goal is dieting, a low-calorie, nutritionally balanced menu is generated. A shopping list is provided based on the generated menu. The AI ​​lists the ingredients needed for the generated menu and provides a shopping list that takes food waste into consideration. For example, the list can be created to use up a week's worth of ingredients without waste. It also supports ordering and delivery through online supermarkets. Users can order from the online supermarket based on the provided shopping list and have the ingredients delivered to their home, eliminating the need for shopping. The system also offers the option to rent cooking appliances. Users can rent cooking appliances as needed to cook more efficiently. For example, they can rent slow cookers and food processors. This system saves time and effort, allowing users to enjoy healthy, balanced meals while contributing to reducing food waste and protecting the global environment. This allows the personalized menu system to automatically generate menus and provide recipes tailored to individual needs.

[0066] A personalized menu providing system according to an embodiment includes a reception unit, a menu creation unit, a shopping list providing unit, an ordering unit, and a delivery unit. The reception unit accepts input of a personal purpose and budget. Examples of personal purposes include, but are not limited to, dieting, maintaining health, and saving money. Examples of budgets include, but are not limited to, a monthly budget, a weekly budget, and a budget per meal. For example, the reception unit accepts a user's input such as, "I'm dieting, so my weekly budget is 5,000 yen." The menu creation unit creates an optimal menu based on the information accepted by the reception unit. The menu creation unit generates a menu that takes into account, for example, nutritional balance and utilizes seasonal ingredients. Examples of nutritional balance include, but are not limited to, recommended daily intakes and the proportions of specific nutrients. Examples of seasonal ingredients include, but are not limited to, seasonal ingredient lists and regional seasonal ingredients. For example, in the case of dieting, the menu creation unit generates a low-calorie, nutritionally balanced menu. The shopping list providing unit provides a shopping list based on the menu generated by the menu generating unit. The shopping list providing unit, for example, lists ingredients needed for the generated menu and provides a shopping list that takes food waste into consideration. Food waste includes, for example, but is not limited to, the type, amount, and cause of discarded ingredients. The shopping list providing unit creates a list, for example, to use up a week's worth of ingredients without waste. The ordering unit places an order in cooperation with an online supermarket based on the shopping list provided by the shopping list providing unit. The ordering unit enables, for example, a user to place an order with the online supermarket based on the provided shopping list and have the ingredients delivered to their home. Online supermarkets include, for example, specific online stores and regionally limited services, but are not limited to these examples. The delivery unit delivers the ingredients ordered by the ordering unit. For example, the delivery unit delivers the ingredients to a location specified by the user via a delivery company. Delivery includes, for example, but is not limited to, the type of delivery company, delivery time, delivery area, etc.As a result, the personalized menu provision system according to the embodiment can generate optimal menus based on an individual's goals and budget, and can provide shopping lists, orders, and even deliver the food to your home in an integrated manner.

[0067] Furthermore, the personalized menu providing system includes a rental unit that provides rental options for cooking appliances. The rental unit provides rental options for cooking appliances. Examples of cooking appliances include, but are not limited to, ovens, microwaves, rice cookers, slow cookers, and food processors. The rental unit, for example, enables a user to rent cooking appliances as needed and cook efficiently. For example, the rental unit can rent cooking appliances such as slow cookers and food processors. Thus, by providing rental options for cooking appliances, the user can cook efficiently.

[0068] The menu generation unit can generate a menu based on nutritional balance and utilizing seasonal ingredients. Nutritional balance includes, for example, recommended daily intakes and the proportions of specific nutrients, but is not limited to these examples. Seasonal ingredients include, for example, seasonal ingredient lists and ingredients that are in season by region, but are not limited to these examples. For example, in the case of dieting, the menu generation unit generates a low-calorie, nutritionally balanced menu. For example, in the case where emphasis is placed on nutritional balance, the menu generation unit provides a menu that includes detailed nutritional information. For example, in the case where emphasis is placed on seasonal ingredients, the menu generation unit provides a menu that uses ingredients that are in season. For example, the menu generation unit provides a menu that emphasizes specific nutrients based on the user's health condition. In this way, healthy, seasonal menus can be provided by taking nutritional balance into consideration and utilizing seasonal ingredients.

[0069] The shopping list providing unit can provide a shopping list based on food waste. Food waste includes, for example, the type, amount, and cause of discarded ingredients, but is not limited to these examples. For example, the shopping list providing unit creates a list to use up a week's worth of ingredients without waste. For example, the shopping list providing unit includes the minimum amount of ingredients needed in the list to reduce food waste. For example, the shopping list providing unit includes in the list how to store ingredients to reduce food waste. For example, the shopping list providing unit includes in the list how to use up ingredients to reduce food waste. In this way, by providing a shopping list that takes food waste into consideration, it is possible to reduce food waste.

[0070] The reception unit can estimate the user's emotions and adjust the timing of inputting the purpose and budget based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit delays the input timing to provide a relaxing environment. For example, if the user is relaxed, the reception unit accelerates the input timing to encourage smooth input. For example, if the user is in a hurry, the reception unit optimizes the input timing to complete the input quickly. User emotions include, but are not limited to, joy, sadness, stress, etc. Emotion estimation algorithms include, but are not limited to, facial expression recognition, voice analysis, text analysis, etc. As a result, by adjusting the input timing according to the user's emotions, it is possible to reduce the user's stress and promote smooth input.

[0071] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates the purpose and budget that the user has frequently input in the past. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the purpose and budget to be used in a specific time period from the user's past input history. The past input history includes, for example, a history of input purpose and budget, an analysis algorithm, etc., but is not limited to these examples. In this way, the optimal input method for the user can be suggested by analyzing the past input history.

[0072] The reception unit can perform filtering based on the user's current health condition and lifestyle when inputting the purpose and budget. The reception unit, for example, suggests an appropriate budget range based on the user's health condition. The reception unit, for example, suggests a menu that can be easily prepared based on the user's lifestyle (e.g., busy daily life). The reception unit, for example, refers to the user's health data and suggests the optimal purpose for maintaining health. The health condition includes, for example, weight, blood pressure, medical history, etc., but is not limited to such examples. The lifestyle situation includes, for example, lifestyle habits, family composition, work situation, etc. In this way, by filtering based on the user's health condition and lifestyle situation, more appropriate purpose and budget can be suggested.

[0073] The reception unit can estimate the user's emotions and determine the priority of the objectives and budget to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes inputting the budget and postpones simple objectives. For example, when the user is relaxed, the reception unit prioritizes inputting the objectives and postpones detailed budget setting. For example, when the user is in a hurry, the reception unit allows the user to input the most important items first. Prioritization of objectives and budgets includes, but is not limited to, a method of setting priorities based on the user's emotions. In this way, by determining the priority of inputs according to the user's emotions, it is possible to reduce the user's stress and promote smooth input.

[0074] When inputting a purpose and budget, the reception unit can prioritize inputting relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit prioritizes suggesting ingredients available in that area. For example, the reception unit suggests a budget that reflects price information at nearby supermarkets based on the user's location information. For example, the reception unit suggests a purpose that matches the local food culture based on the user's geographical location information. Geographical location information includes, for example, GPS data, address information, etc., but is not limited to these examples. This makes it possible to provide information appropriate to the area by taking into account the user's geographical location information.

[0075] When inputting a purpose and budget, the reception unit can analyze the user's social media activity and input relevant information. The reception unit, for example, suggests related purposes based on photos of meals shared by the user on social media. The reception unit, for example, reflects ingredients and dishes of interest from the user's social media activity in the budget. The reception unit, for example, analyzes the user's social media activity and suggests purposes that match trends. Social media activity includes, for example, post content, number of likes, number of followers, etc., but is not limited to these examples. In this way, by analyzing social media activity, it is possible to provide information that matches the user's interests.

[0076] The menu generation unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions. For example, if the user is relaxed, the menu generation unit provides detailed recipes and cooking instructions. For example, if the user is in a hurry, the menu generation unit provides concise and to-the-point recipes. For example, if the user is excited, the menu generation unit provides visually appealing recipes. The way the menu is presented includes, but is not limited to, text format, image format, audio format, etc. In this way, by adjusting the way the menu is presented according to the user's emotions, it is possible to provide the optimal menu for the user.

[0077] When generating a menu, the menu generation unit can set the level of detail of the menu based on nutritional balance and the importance of seasonal ingredients. For example, if emphasis is placed on nutritional balance, the menu generation unit provides a menu including detailed nutritional information. For example, if emphasis is placed on seasonal ingredients, the menu generation unit provides a menu using ingredients that match the season. For example, based on the user's health condition, the menu generation unit provides a menu that emphasizes specific nutrients. The level of detail of the menu includes, but is not limited to, the number of recipe steps, detailed information about ingredients, and the like. In this way, by adjusting the level of detail of the menu based on nutritional balance and the importance of seasonal ingredients, it is possible to provide an optimal menu for the user.

[0078] When generating a menu, the menu generation unit can apply a different menu algorithm depending on the user's health condition and ingredient allergy information. For example, if the user has a specific allergy, the menu generation unit provides a menu that excludes that ingredient. For example, the menu generation unit provides a menu that emphasizes specific nutrients based on the user's health condition. For example, the menu generation unit references the user's health data and provides a menu that is optimal for maintaining health. Allergy information includes, for example, allergies to specific ingredients, the severity of the allergy, and the like, but is not limited to such examples. In this way, by applying a menu algorithm depending on the user's health condition and allergy information, it is possible to provide a safe and appropriate menu for the user.

[0079] The menu generation unit can estimate the user's emotions and adjust the length of the menu based on the estimated user's emotions. For example, when the user is in a hurry, the menu generation unit provides a menu that can be prepared in a short time. For example, when the user is relaxed, the menu generation unit provides a menu that can be enjoyed over time. For example, when the user is excited, the menu generation unit provides a menu that combines multiple dishes. The length of the menu includes, but is not limited to, a one-week menu, a one-month menu, etc. In this way, by adjusting the length of the menu according to the user's emotions, it is possible to provide the optimal menu for the user.

[0080] When generating a menu, the menu generation unit can determine the ranking of menus based on when ingredients were obtained. The menu generation unit, for example, provides a menu that prioritizes the use of seasonal ingredients. The menu generation unit, for example, provides a menu that uses ingredients that can be stored based on when ingredients were obtained. The menu generation unit, for example, provides a menu that matches a specific season based on when ingredients were obtained. The time when ingredients are obtained includes, for example, the time when ingredients are available by season, the time when ingredients are available by region, etc., but is not limited to such examples. In this way, by determining the priority of menus based on when ingredients are obtained, it is possible to provide a menu that makes use of seasonal ingredients.

[0081] When generating a menu, the menu generation unit can set the order of the menu based on the relevance of ingredients. For example, the menu generation unit provides dishes using the same ingredients consecutively to reduce ingredient waste. For example, the menu generation unit provides a menu that reduces cooking effort based on the relevance of ingredients. For example, the menu generation unit provides a balanced menu based on the relevance of ingredients. Examples of ingredient relevance include, but are not limited to, ingredient combinations and common cooking methods. In this way, by adjusting the order of the menu based on the relevance of ingredients, ingredient waste can be reduced and efficient cooking can be achieved.

[0082] The shopping list providing unit can estimate the user's emotions and adjust the display method of the shopping list based on the estimated user's emotions. For example, when the user is nervous, the shopping list providing unit provides a simple, highly visible list. For example, when the user is relaxed, the shopping list providing unit provides a list including detailed information. For example, when the user is in a hurry, the shopping list providing unit provides a list that focuses on the main points. Methods for displaying the shopping list include, but are not limited to, a list format, a grid format, and voice guidance. In this way, by adjusting the display method of the shopping list according to the user's emotions, a list that is easy for the user to view can be provided.

[0083] When providing a shopping list, the shopping list providing unit can set the level of detail of the list taking food waste into consideration. For example, the shopping list providing unit includes the minimum necessary ingredients in the list to reduce food waste. For example, the shopping list providing unit includes in the list how to store ingredients to reduce food waste. For example, the shopping list providing unit includes in the list how to use up ingredients to reduce food waste. The level of detail of the list includes, for example, detailed information about ingredients, detailed information about where to purchase them, etc., but is not limited to these examples. In this way, by adjusting the level of detail of the list taking food waste into consideration, it is possible to reduce food waste.

[0084] When providing a shopping list, the shopping list providing unit can generate an appropriate list by referring to the user's past purchase history. The shopping list providing unit generates an optimal list, for example, based on ingredients purchased by the user in the past. The shopping list providing unit, for example, predicts necessary ingredients from the user's past purchase history and includes them in the list. The shopping list providing unit, for example, analyzes the user's past purchase history to generate an efficient list. The past purchase history includes, for example, but is not limited to, the type, quantity, and purchase frequency of ingredients purchased. In this way, by referring to the past purchase history, the optimal shopping list for the user can be provided.

[0085] The shopping list providing unit can estimate the user's emotions and determine the priority of the shopping list based on the estimated user's emotions. For example, if the user is feeling stressed, the shopping list providing unit prioritizes important ingredients in the list. For example, if the user is relaxed, the shopping list providing unit provides a detailed list and adjusts the priority. For example, if the user is in a hurry, the shopping list providing unit prioritizes the most necessary ingredients in the list. The priority of the shopping list includes, but is not limited to, a method of setting priorities based on the user's emotions. In this way, by determining the priority of the shopping list according to the user's emotions, ingredients important to the user can be provided preferentially.

[0086] When providing a shopping list, the shopping list providing unit can prioritize relevant lists by taking into account the user's geographical location information. For example, the shopping list providing unit includes ingredients available at nearby supermarkets in the list based on the user's location information. For example, the shopping list providing unit includes local specialties in the list based on the user's location information. For example, the shopping list providing unit suggests an optimal shopping route based on the user's location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. In this way, a shopping list suitable for the region can be provided by taking into account the user's geographical location information.

[0087] When providing a shopping list, the shopping list providing unit can analyze the user's social media activity and provide a relevant list. For example, the shopping list providing unit includes necessary ingredients in the list based on recipes shared by the user on social media. For example, the shopping list providing unit includes ingredients of interest to the user based on the user's social media activity. For example, the shopping list providing unit analyzes the user's social media activity and provides a list tailored to trends. Social media activity includes, for example, post content, number of likes, number of followers, etc., but is not limited to these examples. In this way, by analyzing social media activity, a shopping list tailored to the user's interests can be provided.

[0088] The ordering unit can estimate the user's emotions and adjust the way the order is expressed based on the estimated user's emotions. For example, the ordering unit provides detailed order information when the user is relaxed. For example, the ordering unit provides concise and to-the-point order information when the user is in a hurry. For example, the ordering unit provides visually appealing order information when the user is excited. Order expression methods include, but are not limited to, text format, image format, audio format, etc. In this way, by adjusting the way the order is expressed depending on the user's emotions, it is possible to provide the user with optimal order information.

[0089] The ordering unit can set the level of detail of the order based on the importance of the product when placing an order. For example, the ordering unit provides detailed information for important products and prompts the user to confirm the order. For example, the ordering unit provides concise information for less important products, allowing the user to complete the order quickly. The ordering unit processes the order with priority based on the importance of the product. The importance of the product includes, but is not limited to, importance based on the user's needs and frequency of use of the product. In this way, by adjusting the level of detail of the order based on the importance of the product, it is possible to provide products that are important to the user with priority.

[0090] When placing an order, the ordering unit can apply different ordering algorithms depending on the product category. For example, for fresh food, the ordering unit applies an ordering algorithm that emphasizes freshness. For example, for preserved food, the ordering unit applies an ordering algorithm that takes stock status into consideration. For example, for cooking appliances, the ordering unit applies an ordering algorithm that includes a rental option. Product categories include, but are not limited to, food, daily necessities, and home appliances. This allows optimal order processing by applying different ordering algorithms depending on the product category.

[0091] The ordering unit can estimate the user's emotions and adjust the length of the order based on the estimated user emotions. For example, if the user is in a hurry, the ordering unit provides a short and to-the-point order. For example, if the user is relaxed, the ordering unit provides a longer order with detailed explanations. For example, if the user is excited, the ordering unit provides an order with visually stimulating effects. Examples of the order length include, but are not limited to, the number of items purchased in one order and the duration of the order. In this way, by adjusting the order length according to the user's emotions, optimal order information can be provided to the user.

[0092] When placing an order, the ordering unit can determine the order priority based on the availability of the product. For example, the ordering unit processes orders for products that are needed urgently as a priority. For example, the ordering unit processes orders for products that will be available later at a later date. For example, the ordering unit proposes an optimal ordering schedule based on the availability of the product. The availability of the product includes, but is not limited to, seasonal product availability and regional product availability. In this way, by determining the order priority based on the availability of the product, it is possible to provide the required product at the appropriate time.

[0093] The ordering unit can set the order sequence based on the relevance of products when placing an order. For example, the ordering unit orders products from the same category consecutively to process efficiently. For example, the ordering unit processes the order in an order that reduces cooking effort based on the relevance of products. For example, the ordering unit provides a balanced order based on the relevance of products. Product relevance includes, but is not limited to, product combinations, common usage methods, and the like. This allows for efficient order processing by adjusting the order sequence based on the relevance of products.

[0094] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, when the user is relaxed, the delivery unit provides detailed delivery information. For example, when the user is in a hurry, the delivery unit provides concise and to-the-point delivery information. For example, when the user is excited, the delivery unit provides visually appealing delivery information. The delivery method includes, but is not limited to, the type of delivery company, delivery time, delivery area, etc. In this way, by adjusting the delivery method according to the user's emotions, it is possible to provide the optimal delivery method for the user.

[0095] When delivering goods, the delivery unit can select an appropriate delivery method by referring to the user's past delivery history. The delivery unit, for example, suggests an optimal method based on delivery methods used by the user in the past. The delivery unit, for example, suggests a delivery method that avoids congestion based on the user's past delivery history. The delivery unit, for example, analyzes the user's past delivery history and suggests the most efficient delivery method. The past delivery history includes, for example, the type, quantity, and delivery frequency of delivered goods. In this way, by referring to the past delivery history, the optimal delivery method for the user can be provided.

[0096] The delivery unit can set a delivery method based on the user's current living situation at the time of delivery. For example, if the user is busy, the delivery unit suggests a quick delivery method. For example, if the user is relaxed, the delivery unit provides a detailed delivery schedule. For example, the delivery unit suggests an optimal delivery method based on the user's living situation. Living situations include, but are not limited to, lifestyle habits, family composition, work situation, etc. In this way, by customizing the delivery method based on the user's living situation, it is possible to provide an optimal delivery method for the user.

[0097] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, when the user is feeling stressed, the delivery unit prioritizes important deliveries. For example, when the user is relaxed, the delivery unit provides a detailed delivery schedule. For example, when the user is in a hurry, the delivery unit prioritizes the most necessary deliveries. Delivery priorities include, but are not limited to, methods of setting priorities based on the user's emotions. In this way, by determining delivery priorities according to the user's emotions, deliveries that are important to the user can be provided preferentially.

[0098] The delivery unit can select an appropriate delivery method by taking into consideration the user's geographical location information when delivering. The delivery unit, for example, proposes an optimal delivery route based on the user's location information. The delivery unit, for example, provides a delivery method tailored to the characteristics of the area based on the user's location information. The delivery unit, for example, proposes an optimal delivery schedule based on the user's location information. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to these examples. In this way, a delivery method suited to the area can be provided by taking into consideration the user's geographical location information.

[0099] The delivery unit can analyze the user's social media activity and set the delivery method when delivering. The delivery unit, for example, suggests the optimal delivery method based on information shared by the user on social media. The delivery unit, for example, suggests a delivery method that is of interest to the user based on the user's social media activity. The delivery unit, for example, analyzes the user's social media activity and provides a delivery method that matches the trends. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. In this way, by analyzing social media activity, it is possible to provide a delivery method that matches the user's interests.

[0100] The rental unit can estimate the user's emotions and adjust the rental method for the cooking appliance based on the estimated user's emotions. For example, if the user is relaxed, the rental unit provides detailed rental information. For example, if the user is in a hurry, the rental unit provides concise and to-the-point rental information. For example, if the user is excited, the rental unit provides visually appealing rental information. The rental method may include, but is not limited to, a rental period, a rental fee, and a rental procedure. This allows the rental method to be adjusted according to the user's emotions, thereby providing optimal rental information for the user.

[0101] The rental unit can suggest an appropriate home appliance by referring to the user's past rental history at the time of rental. The rental unit suggests the most suitable home appliance, for example, based on the home appliances the user has rented in the past. The rental unit, for example, predicts and suggests the home appliances the user will need based on the user's past rental history. The rental unit, for example, analyzes the user's past rental history and suggests home appliances that do not require waste. The past rental history includes, for example, the type, quantity, and rental frequency of the home appliances rented, but is not limited to such examples. In this way, the most suitable home appliance can be provided to the user by referring to the past rental history.

[0102] The rental unit can set the type of home appliance based on the user's current cooking needs at the time of rental. For example, if the user wants to cook a specific dish, the rental unit suggests the home appliance that is best suited to that dish. For example, the rental unit customizes and suggests the necessary home appliance based on the user's cooking needs. For example, the rental unit refers to the user's cooking needs and suggests home appliances that allow efficient cooking. Cooking needs include, for example, the type of dish to be cooked, cooking frequency, cooking time, etc., but are not limited to such examples. In this way, by customizing the type of home appliance based on the user's cooking needs, it is possible to provide the user with the home appliance that is best suited to the user.

[0103] The rental unit can estimate the user's emotions and determine rental priorities based on the estimated user emotions. For example, when the user is feeling stressed, the rental unit prioritizes renting important home appliances. For example, when the user is relaxed, the rental unit provides a detailed rental schedule. For example, when the user is in a hurry, the rental unit prioritizes renting the most necessary home appliances. Examples of rental priorities include, but are not limited to, a method of setting priorities based on the user's emotions. In this way, by determining rental priorities according to the user's emotions, home appliances that are important to the user can be provided preferentially.

[0104] The rental unit can suggest an appropriate home appliance at the time of rental, taking into consideration the user's geographical location information. For example, the rental unit suggests home appliances available in the vicinity based on the user's location information. For example, the rental unit provides home appliances tailored to local characteristics based on the user's location information. For example, the rental unit suggests an optimal rental schedule based on the user's location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. This allows home appliances suited to the local area to be provided by taking into consideration the user's geographical location information.

[0105] At the time of rental, the rental department can analyze the user's social media activity to set the type of home appliance. For example, the rental department suggests the most suitable home appliance based on information shared by the user on social media. For example, the rental department suggests home appliances that the user is interested in based on the user's social media activity. For example, the rental department analyzes the user's social media activity and provides home appliances that match trends. Social media activity includes, for example, post content, number of likes, number of followers, etc., but is not limited to such examples. In this way, by analyzing social media activity, it is possible to provide home appliances that match the user's interests. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, menu creation unit, shopping list providing unit, ordering unit, delivery unit, and rental 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 receives input of the user's purpose and budget. The menu creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal menu taking into account nutritional balance and seasonal ingredients. The shopping list providing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a shopping list that takes food waste into consideration based on the generated menu. The order unit is implemented, for example, by the control unit 46A of the smart device 14 and places an order in cooperation with an online supermarket. The delivery unit is implemented, for example, by the control unit 46A of the smart device 14 and delivers the ordered ingredients to the home. The rental unit is implemented, for example, by the control unit 46A of the smart device 14 and provides rental options for cooking appliances. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, menu creation unit, shopping list providing unit, ordering unit, delivery unit, and rental unit, is realized by, for example, 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 receives input of the user's purpose and budget. The menu creation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates an optimal menu taking into account nutritional balance and seasonal ingredients. The shopping list providing unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and provides a shopping list that takes food waste into consideration based on the generated menu. The order unit is realized by, for example, the control unit 46A of the smart glasses 214 and places an order in cooperation with an online supermarket. The delivery unit is realized by, for example, the control unit 46A of the smart glasses 214 and delivers the ordered ingredients to the home. The rental unit is realized by, for example, the control unit 46A of the smart glasses 214 and provides rental options for cooking appliances. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, menu creation unit, shopping list providing unit, ordering unit, delivery unit, and rental unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of the user's purpose and budget. The menu creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal menu taking into account nutritional balance and seasonal ingredients. The shopping list providing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a shopping list that takes food waste into consideration based on the generated menu. The order unit is implemented, for example, by the control unit 46A of the headset terminal 314 and places an order in cooperation with an online supermarket. The delivery unit is implemented, for example, by the control unit 46A of the headset terminal 314 and delivers the ordered ingredients to the home. The rental unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides rental options for cooking appliances. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, menu creation unit, shopping list providing unit, ordering unit, delivery unit, and rental unit, described above, 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 receives input of the user's purpose and budget. The menu creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal menu taking into account nutritional balance and seasonal ingredients. The shopping list providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a shopping list that takes food waste into consideration based on the generated menu. The order unit is realized, for example, by the control unit 46A of the robot 414 and places an order in cooperation with an online supermarket. The delivery unit is realized, for example, by the control unit 46A of the robot 414 and delivers the ordered ingredients to the user's home. The rental unit is realized, for example, by the control unit 46A of the robot 414 and provides rental options for cooking appliances.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can analyze the user's past meal history and suggest appropriate menus. For example, it can suggest similar menus based on the dishes and ingredients that the user has liked to eat in the past. It can also generate menus that do not include ingredients that the user has avoided in the past or allergy information, taking into account such ingredients. It can also analyze the nutritional balance from the user's meal history and suggest menus that supplement necessary nutrients. This makes it possible to provide more personalized menus by utilizing the user's past meal history.

[0108] The menu generation unit can estimate the user's emotions and adjust the menu variations based on the estimated user's emotions. For example, if the user is feeling stressed, it can suggest a menu using ingredients that have a relaxing effect. If the user is happy, it can also provide a luxurious menu suitable for a special event. Also, if the user is tired, it can suggest a menu that is easy to prepare. In this way, it is possible to provide the optimal menu according to the user's emotions.

[0109] The shopping list providing unit can analyze the user's purchasing history and generate an appropriate shopping list. For example, it can predict the ingredients needed based on ingredients purchased in the past by the user and include them in the list. Furthermore, it can also prioritize ingredients used frequently by considering the frequency of use of ingredients purchased in the past by the user. It can also generate a waste-free shopping list from the user's purchasing history and reduce food waste. This makes it possible to provide an efficient shopping list by utilizing the user's purchasing history.

[0110] The ordering unit can estimate the user's emotions and determine the order priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize ordering important ingredients. If the user is relaxed, it can provide detailed order information and adjust the priority. Also, if the user is in a hurry, it can prioritize ordering the most necessary ingredients. In this way, it is possible to determine the order priority according to the user's emotions and realize efficient ordering.

[0111] The delivery unit can select an appropriate delivery method by taking into account the user's geographical location information. For example, it can propose an optimal delivery route based on the user's location information. Furthermore, it can also provide a delivery method that suits the characteristics of the area based on the user's location information. It can also propose an optimal delivery schedule based on the user's location information. In this way, it is possible to provide a delivery method that is suitable for the area by taking into account the user's geographical location information.

[0112] The rental unit can estimate the user's emotions and adjust the rental method of the cooking appliance based on the estimated user's emotions. For example, if the user is relaxed, detailed rental information can be provided. If the user is in a hurry, concise rental information can be provided. Also, if the user is excited, visually appealing rental information can be provided. In this way, the rental method can be adjusted according to the user's emotions, and optimal rental information can be provided.

[0113] The reception unit can refer to the user's health data and suggest optimal goals for maintaining health. For example, it can suggest an appropriate diet plan and nutritionally balanced menu based on the user's health data, such as weight, blood pressure, and medical history. Furthermore, it can provide menus that emphasize specific nutrients based on the user's health condition. It can also analyze the user's health data and include ingredients necessary for maintaining health in the list. This makes it possible to utilize the user's health data to provide healthier menus.

[0114] The menu generation unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed recipe and cooking steps can be provided. If the user is in a hurry, a concise and to-the-point recipe can be provided. Also, if the user is excited, a visually appealing recipe can be provided. In this way, the way the menu is presented can be adjusted according to the user's emotions, and an optimal menu can be provided.

[0115] The shopping list providing unit can provide a relevant list preferentially, taking into consideration the user's geographical location information. For example, ingredients available at nearby supermarkets can be included in the list based on the user's location information. Furthermore, local specialties can be included in the list based on the user's location information. It is also possible to suggest an optimal shopping route based on the user's location information. In this way, a shopping list suited to the area can be provided by taking into consideration the user's geographical location information.

[0116] The ordering unit can estimate the user's emotions and adjust the way the order is expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed order information can be provided. If the user is in a hurry, concise and to the point order information can be provided. Also, if the user is excited, visually appealing order information can be provided. In this way, the way the order is expressed can be adjusted according to the user's emotions, and optimal order information can be provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit accepts input of personal goals and budgets. Personal goals include dieting, maintaining health, saving money, etc., and budgets include monthly budgets, weekly budgets, and budgets per meal. For example, the reception unit accepts input from a user such as "My weekly budget for dieting is 5,000 yen." Step 2: The menu generator generates an optimal menu based on the information received by the receiver. The menu is generated taking into consideration nutritional balance and utilizing seasonal ingredients. For example, if the purpose is to lose weight, a low-calorie, nutritionally balanced menu is generated. Step 3: The shopping list provider provides a shopping list based on the menu generated by the menu generator. It lists the ingredients needed for the generated menu and provides a shopping list that takes food waste into consideration. For example, it creates a list that uses up a week's worth of ingredients without waste. Step 4: The ordering unit places an order in cooperation with the online supermarket based on the shopping list provided by the shopping list providing unit. This enables the user to place an order with the online supermarket based on the provided shopping list and have the ingredients delivered to their home. Step 5: The delivery department delivers the food ordered by the ordering department to the user's designated location via a delivery company.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 that accepts input of personal objectives and budgets; a menu creation unit that creates an appropriate menu based on the information received by the reception unit; a shopping list providing unit that provides a shopping list based on the menu generated by the menu generating unit; an ordering unit that places an order in cooperation with an online supermarket based on the shopping list provided by the shopping list providing unit; a delivery unit that delivers the ingredients ordered by the ordering unit A system characterized by:

2. Has a rental department that offers rental options for kitchen appliances The system of claim 1 .

3. The menu generation unit Create menus based on nutritional balance and using seasonal ingredients The system of claim 1 .

4. The shopping list providing unit Providing a food waste-based shopping list The system of claim 1 .

5. The reception unit Inferring user emotions and adjusting the timing of inputting goals and budgets based on the estimated user emotions The system of claim 1 .

6. The reception unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

7. The reception unit When entering purpose and budget, filtering is performed based on the user's current health and living situation. The system of claim 1 .

8. The reception unit Estimate user emotions and prioritize input objectives and budgets based on the estimated user emotions The system of claim 1 .

9. The reception unit When entering purpose and budget, the app takes into account the user's geographic location to prioritize relevant information. The system of claim 1 .

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