System

Abstract

To provide a system that generates an optimal menu based on individual health data and provide an efficient shopping list.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, an acquisition unit, and a provision unit. The reception unit receives data of a user. The generation unit analyzes the data received by the reception unit, and generates a menu based on personal health data. The acquisition unit acquires store information. The provision unit provides the user with a shopping list based on the menu generated by the generation unit and the information acquired by the acquisition unit.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies do not adequately generate menus based on personal health data or provide efficient shopping lists, leaving room for improvement.

[0005] The system according to the embodiment aims to generate optimal menus based on personal health data and provide efficient shopping lists. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an acquisition unit, and a provision unit. The reception unit accepts user data. The generation unit analyzes the data accepted by the reception unit and generates a menu based on the personal health data. The acquisition unit acquires store information. The provision unit provides the user with a shopping list based on the menu generated by the generation unit and the information acquired by the acquisition unit. Effect of the Invention

[0007] The system according to the embodiment can generate optimal menus based on personal health data and provide efficient shopping lists. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The menu generation system according to the embodiment of the present invention is a system that generates original menus for 365 days based on the user's personal data, tailored to the user's health, and provides a shopping list in cooperation with information on nearby supermarkets. This menu generation system uses AI to analyze the user's attributes (gender, address, family structure, height and weight, likes and dislikes, etc.), ideal monthly food expenses, dietary habits, and other personal requests, and generates an original menu tailored to the individual's health condition and lifestyle. Next, based on the generated menu and information on nearby supermarkets, the system shows what and how much to buy on which day in order to use up ingredients without wasting them, purchase good value ingredients, and keep food expenses within the budget. This is expected to be a perspective for housewives and people who cook at home every day. For example, the user inputs their own attributes and requests. For example, data such as gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and dietary habits are input. This data is analyzed by AI. Next, AI analyzes the input data and generates an original menu tailored to the individual's health condition and lifestyle. For example, a nutritionally balanced menu according to the health condition, an easy-to-make menu that matches the lifestyle, etc. are generated. Furthermore, a shopping list is generated based on the generated menu and information on nearby supermarkets. For example, what and how much to go shopping on which day is presented so that ingredients can be used up without waste, ingredients can be purchased at a good price, and food costs can be kept within the budget. This shopping list is displayed on the user's terminal device. With this mechanism, the user can easily obtain an original menu that matches his / her health condition and lifestyle, and can shop more efficiently. This is expected to be of help to housewives and people who cook at home every day. As a result, the menu generation system can provide an original menu that matches the health condition and lifestyle of the user, and generate an efficient shopping list.

[0029] The menu generation system according to the embodiment includes a reception unit, a generation unit, an acquisition unit, and a provision unit. The reception unit receives user data. The user data includes, for example, gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and dietary habits, but is not limited to such examples. The reception unit, for example, stores the data entered by the user in a database and transmits it to the generation unit for analysis. The generation unit uses a generation AI to analyze the data received by the reception unit and generates an original menu that matches the individual's health condition and lifestyle. The generation AI analyzes the user's data and generates an optimal menu, for example, using a neural network or deep learning technology. The generation unit generates, for example, a nutritionally balanced menu according to the health condition, or an easy-to-make menu that matches the lifestyle. The acquisition unit acquires supermarket information. The supermarket information includes, for example, the store's location, business hours, and inventory information, but is not limited to such examples. The acquisition unit, for example, collects supermarket information via the Internet and stores it in a database. The providing unit provides a shopping list to the user based on the menu generated by the generating unit and the information acquired by the acquiring unit. The providing unit, for example, displays the shopping list on the user's terminal device. The shopping list includes, for example, necessary ingredients, quantities, and purchasing locations, but is not limited to such examples. In this way, the menu generating system according to the embodiment can generate a menu tailored to health based on the user's data and provide an efficient shopping list.

[0030] The reception unit receives the user's data. The user's data includes, but is not limited to, for example, gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and eating habits. For example, the reception unit stores the data entered by the user in a database and transmits it to the generation unit for analysis. Specifically, the information entered by the user through a dedicated application or website is stored in the database in real time. This ensures that the user's data is always kept up to date and can be accessed immediately when needed by the generation unit. Furthermore, the reception unit also has a function of periodically updating the user's data, and the database is automatically updated every time the user enters new information. For example, if the user loses weight or the family structure changes, the information is reflected immediately. This allows the generation unit to always generate a menu based on the latest data. Furthermore, the reception unit encrypts data and restricts access to the data in order to protect the user's privacy. This minimizes the risk of the user's personal information being leaked to a third party. Furthermore, the reception unit also has a function of analyzing the user's input data and detecting input errors and inconsistencies. For example, if a user mistakenly enters inaccurate data, the system will automatically issue a warning and prompt the user to enter the correct data. This will improve the accuracy of the data and allow the generation unit to generate more accurate menus.

[0031] The generation unit uses the generation AI to analyze the data received by the reception unit and generate an original menu that matches the individual's health condition and lifestyle. The generation AI analyzes the user's data using, for example, neural networks and deep learning technology to generate an optimal menu. Specifically, the generation AI receives data on the user's health condition and lifestyle as input, and proposes an optimal menu based on past data and nutritional knowledge. For example, if the user wants to go on a diet, the generation AI generates a low-calorie menu that takes into account calorie restrictions. Also, if the user wants to consume a lot of a specific nutrient, the generation AI proposes a menu that uses ingredients that are rich in that nutrient. The generation AI also takes into account the user's preferences and allergy information to generate a menu that satisfies the user. For example, if the user dislikes a specific ingredient, the generation AI generates a menu that avoids that ingredient. The generation AI can also adjust the cooking time and difficulty to suit the user's lifestyle. For example, it proposes simple menus that can be made in a short time for busy users, and proposes elaborate dishes for users who have time. In addition, the generation AI can also propose menus that use seasonal ingredients, taking into account the characteristics of the season and region. This allows the user to always enjoy fresh and delicious meals. The menu generator performs final adjustments, such as calculating nutritional balance and calories, before providing the generated menu to the user. This allows the user to eat a healthy and balanced meal.

[0032] The acquisition unit acquires supermarket information. The supermarket information includes, for example, the location of the store, business hours, and inventory information, but is not limited to such examples. For example, the acquisition unit collects supermarket information via the Internet and stores it in a database. Specifically, the acquisition unit acquires the latest inventory information and sale information using the supermarket's official website and API. This allows the user to always create a shopping list based on the latest information. Furthermore, the acquisition unit also has a function of preferentially acquiring information on the nearest supermarket based on the user's location information. For example, the convenience of shopping is improved by preferentially displaying information on supermarkets near the user's home or workplace. Furthermore, the acquisition unit updates supermarket inventory information in real time to ensure that the user can purchase the ingredients he or she needs. For example, if a specific ingredient is out of stock, it also has a function of suggesting a substitute. This allows the user to smoothly prepare ingredients without any trouble when shopping. Furthermore, the acquisition unit also collects supermarket sale information and coupon information and provides it to the user. This allows the user to shop at a good price and save on monthly food expenses. The acquisition unit can centrally manage this information and link with other systems and departments as necessary. For example, the acquired information may be stored in a cloud server and made accessible to the providing unit, thereby enabling the acquiring unit to collect information efficiently and effectively and improve the performance of the entire system.

[0033] The providing unit provides a shopping list to the user based on the menu generated by the generating unit and the information acquired by the acquiring unit. The providing unit, for example, displays the shopping list on the user's terminal device. The shopping list includes, for example, necessary ingredients, quantities, and purchase locations, but is not limited to such examples. Specifically, the providing unit displays the shopping list on the user's smartphone or tablet through a dedicated app, allowing the user to easily access it. Furthermore, the providing unit updates the shopping list in real time, allowing the user to shop based on the latest information. For example, if the inventory information of a supermarket changes, the providing unit immediately updates the shopping list and notifies the user. The providing unit also has a function of suggesting substitutes taking into account the user's preferences and allergy information. For example, if a specific ingredient is out of stock, another ingredient with the same nutritional value is suggested. This allows the user to smoothly prepare ingredients without any trouble when shopping. Furthermore, the providing unit displays sale information and coupon information on the shopping list, allowing the user to shop more advantageously. For example, if a specific ingredient is on sale, the information is displayed on the shopping list to notify the user. This allows the user to save on monthly food expenses. The providing unit can collect user feedback and continuously improve the accuracy and convenience of the shopping list. For example, the providing unit can provide a function that allows the user to submit opinions and requests regarding the shopping list, and improve the system based on that feedback. This allows the providing unit to provide better service to the user and increase satisfaction.

[0034] The reception unit can receive data including the user's attributes and requests as user data. The reception unit receives data such as the user's gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and dietary habits. This allows the reception unit to accurately receive the user's attributes and requests. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the data input by the user to AI and have the AI ​​analyze the data.

[0035] The generation unit can analyze the user's data using the generation AI and generate an original menu based on the individual's health data or lifestyle. The generation unit can, for example, use the generation AI to analyze the user's data and generate an original menu that matches the individual's health condition and lifestyle. The generation AI can, for example, use neural networks or deep learning technology to analyze the user's data and generate an optimal menu. The generation unit can, for example, generate a nutritionally balanced menu according to the health condition or an easy-to-make menu that matches the lifestyle. This allows the generation unit to generate a menu that matches the user's health condition and lifestyle. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's data into the generation AI and cause the generation AI to generate a menu.

[0036] The providing unit can display the shopping list on the user's terminal device. The providing unit displays the shopping list on the user's terminal device, for example, based on the menu generated by the generating unit and the information acquired by the acquiring unit. The shopping list includes, for example, necessary ingredients, quantities, and purchase locations, but is not limited to such examples. This allows the providing unit to allow the user to check the shopping list on the terminal device. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the menu generated by the generating unit and the information acquired by the acquiring unit to AI, and cause the AI ​​to generate the shopping list.

[0037] The reception unit can analyze the user's past data input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past data input history and suggests an optimal input method. For example, data that the user frequently inputs in the past is automatically displayed as a candidate. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest data to be used in a specific time period from the user's past input history. This allows the reception unit to suggest an optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past data input history to AI and have the AI ​​execute the suggestion of an optimal input method.

[0038] The reception unit can customize the input items based on the user's current living situation and areas of interest when inputting data. For example, the reception unit customizes the input items based on the user's current living situation and areas of interest when inputting data. For example, if the user is interested in health, health-related input items can be preferentially displayed. Also, if the user is busy, items that can be easily input can be preferentially displayed. Also, if the user is interested in a specific ingredient, input items related to that ingredient can be displayed. This allows the reception unit to provide input items according to the user's living situation and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into AI and cause AI to customize the input items.

[0039] The reception unit can display highly relevant input items preferentially in consideration of the geographical location information of the user when inputting data. For example, the reception unit displays highly relevant input items preferentially in consideration of the geographical location information of the user when inputting data. For example, if the user lives in a specific area, input items related to the area are preferentially displayed. In addition, if the user is traveling, input items related to the travel destination can be displayed. In addition, if the user is planning to move, input items related to the new address can be displayed. In this way, the reception unit can provide input items based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute the display of highly relevant input items.

[0040] The reception unit can analyze the user's social media activity at the time of data input and suggest related input items. For example, the reception unit can analyze the user's social media activity at the time of data input and suggest related input items. For example, the reception unit can suggest ingredients that the user frequently mentions on social media as input items. In addition, the reception unit can suggest related input items based on recipes shared by the user on social media. In addition, the reception unit can suggest related input items based on health information that the user follows on social media. In this way, the reception unit can provide input items based on the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity to AI and cause the AI ​​to execute the suggestion of related input items.

[0041] The generating unit can adjust the nutritional balance based on the user's health condition when generating a menu. The generating unit adjusts the nutritional balance based on the user's health condition when generating a menu. For example, when the user is on a diet, a low-calorie, nutritionally balanced menu is generated. Also, when the user needs a specific nutrient, a menu including the nutrient can be generated. Also, when the user has an allergy, a menu not including an allergenic ingredient can be generated. In this way, the generating unit can provide a nutritionally balanced menu according to the user's health condition. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's health data into AI and cause AI to adjust the nutritional balance.

[0042] The generation unit can adjust the cooking time according to the user's lifestyle when generating a menu. The generation unit, for example, adjusts the cooking time according to the user's lifestyle when generating a menu. For example, if the user is busy, a menu that can be cooked in a short time is generated. Also, if the user has time, an elaborate menu can be generated. Also, if the user cooks during a specific time period, a menu that matches that time period can be generated. In this way, the generation unit can provide a menu with a cooking time that matches the user's lifestyle. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle data into AI and cause AI to adjust the cooking time.

[0043] The generation unit can suggest a menu that matches the user's preferences by referring to the user's past meal history when generating a menu. The generation unit can suggest a menu that matches the user's preferences by referring to the user's past meal history when generating a menu. For example, the generation unit can suggest a similar menu based on dishes that the user liked to eat in the past. Also, based on ingredients that the user avoided in the past, it can suggest a menu that does not include those ingredients. Also, it can suggest a menu that matches a specific season from the user's past meal history. In this way, the generation unit can provide a menu that matches the user's preferences based on the user's past meal history. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past meal history into AI and cause the AI ​​to suggest a menu that matches the user's preferences.

[0044] The generation unit can generate a safe menu by considering the user's allergy information when generating a menu. The generation unit generates a safe menu by considering the user's allergy information, for example, when generating a menu. For example, if the user is allergic to a specific ingredient, a menu that does not include that ingredient is generated. In addition, if the user has multiple allergies, a menu that takes all of them into consideration can be generated. In addition, if the user discovers a new allergy, the menu can be generated by immediately reflecting that information. In this way, the generation unit can provide a safe menu based on the user's allergy information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's allergy information to AI and cause the AI ​​to generate a safe menu.

[0045] The acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method when acquiring supermarket information. For example, the acquisition unit analyzes the user's past purchase history and selects an optimal acquisition method when acquiring supermarket information. For example, related supermarket information is acquired based on products frequently purchased by the user in the past. Also, product information to be purchased in a specific time period can be preferentially acquired from the user's past purchase history. Also, the acquisition unit can analyze the user's past purchase pattern and acquire optimal supermarket information. In this way, the acquisition unit can provide optimal supermarket information based on the user's past purchase history. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past purchase history into AI and cause AI to select an optimal acquisition method.

[0046] The acquisition unit can perform filtering based on the user's current living situation and field of interest when acquiring supermarket information. For example, the acquisition unit performs filtering based on the user's current living situation and field of interest when acquiring supermarket information. For example, if the user is interested in health, health-related product information is preferentially acquired. In addition, if the user is interested in a specific ingredient, product information related to that ingredient can also be acquired. In addition, if the user leads a busy life, product information that can be easily cooked can also be acquired preferentially. In this way, the acquisition unit can provide supermarket information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and field of interest into AI and cause AI to perform filtering.

[0047] The acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring the super information. For example, the acquisition unit preferentially acquires highly relevant information in consideration of the geographical location information of the user when acquiring the super information. For example, if the user lives in a specific area, super information related to the area is preferentially acquired. In addition, if the user is traveling, super information related to the travel destination can be acquired. In addition, if the user is planning to move, super information related to the new address can be acquired. In this way, the acquisition unit can provide super information based on the geographical location information of the user. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to AI and cause the AI ​​to acquire highly relevant information.

[0048] The acquisition unit can analyze the social media activity of the user and acquire related information when acquiring supermarket information. For example, the acquisition unit can analyze the social media activity of the user and acquire related information when acquiring supermarket information. For example, related supermarket information can be acquired based on products that the user frequently mentions on social media. Also, related product information can be acquired based on recipes that the user shares on social media. Also, related product information can be acquired based on health information that the user follows on social media. In this way, the acquisition unit can provide supermarket information based on the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity to AI and cause the AI ​​to acquire related information.

[0049] The providing unit can generate an optimal list by referring to the user's past purchase history when providing a shopping list. For example, the providing unit generates an optimal list by referring to the user's past purchase history when providing a shopping list. For example, the shopping list is generated based on products frequently purchased by the user in the past. Products to be purchased during a specific time period can also be included in the list from the user's past purchase history. The optimal shopping list can also be generated by analyzing the user's past purchase patterns. In this way, the providing unit can provide an optimal shopping list based on the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history into AI and cause AI to generate an optimal list.

[0050] The providing unit can customize the shopping list based on the user's current living situation when providing the shopping list. For example, the providing unit customizes the list based on the user's current living situation when providing the shopping list. For example, if the user is interested in health, health-related products can be included in the list. Also, if the user leads a busy life, products that can be easily cooked can be included in the list. Also, if the user is interested in a specific ingredient, the ingredient can be included in the list. In this way, the providing unit can provide a shopping list according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI and cause AI to customize the list.

[0051] The providing unit can generate an optimal list in consideration of the geographical location information of the user when providing a shopping list. The providing unit generates an optimal list in consideration of the geographical location information of the user when providing a shopping list, for example. For example, if the user lives in a specific area, products related to the area can be included in the list. Also, if the user is traveling, products related to the travel destination can be included in the list. Also, if the user is planning to move, products related to the new address can be included in the list. In this way, the providing unit can provide a shopping list based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to generate an optimal list.

[0052] The providing unit can analyze the social media activity of the user and propose the list when providing the shopping list. For example, the providing unit can analyze the social media activity of the user and propose the list when providing the shopping list. For example, the providing unit can include products that the user frequently mentions on social media in the list. Also, based on recipes shared by the user on social media, related products can be included in the list. Also, based on health information that the user follows on social media, related products can be included in the list. In this way, the providing unit can provide a shopping list based on the social media activity of the user. Part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity into AI and cause the AI ​​to execute the list suggestion.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When accepting data of a user, the reception unit can refer to the user's past meal history and customize input items based on the dishes the user liked to eat in the past and the ingredients the user avoided. For example, the reception unit can preferentially display dishes that the user frequently ate in the past and suggest input items that do not include ingredients the user avoided. In addition, the reception unit can display input items that are suitable for a specific season based on the user's past meal history. This allows the reception unit to provide optimal input items based on the user's past meal history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past meal history to AI and have the AI ​​execute the suggestion of optimal input items.

[0055] When analyzing the user's data, the generation unit can analyze the user's social media activity and generate a related menu. For example, a menu including ingredients frequently mentioned by the user on social media is generated. Also, a related menu can be generated based on recipes shared by the user on social media. Also, a related menu can be generated based on health information followed by the user on social media. In this way, the generation unit can provide an optimal menu based on the user's social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into AI and cause the AI ​​to generate an optimal menu.

[0056] When displaying a shopping list on a user's terminal device, the providing unit can display highly relevant information preferentially, taking into consideration the user's geographical location information. For example, if a user lives in a specific area, products related to that area can be included in the list. Also, if a user is traveling, products related to the travel destination can be included in the list. Also, if a user is planning to move, products related to the new address can be included in the list. This allows the providing unit to provide a shopping list based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to AI and cause AI to generate an optimal list.

[0057] When acquiring supermarket information, the acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method. For example, related supermarket information can be acquired based on products that the user frequently purchased in the past. Also, it is possible to preferentially acquire product information to be purchased in a specific time period from the user's past purchase history. Also, it is possible to analyze the user's past purchase patterns and acquire optimal supermarket information. In this way, the acquisition unit can provide optimal supermarket information based on the user's past purchase history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past purchase history into AI and cause the AI ​​to select the optimal acquisition method.

[0058] When providing a shopping list, the providing unit can generate an optimal list by referring to the user's past purchase history. For example, the shopping list is generated based on products that the user frequently purchased in the past. Also, products to be purchased during a specific time period can be included in the list from the user's past purchase history. Also, the providing unit can analyze the user's past purchase patterns and generate an optimal shopping list. In this way, the providing unit can provide an optimal shopping list based on the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history into AI and cause AI to generate an optimal list.

[0059] When providing the shopping list, the providing unit can customize the list based on the user's current living situation. For example, if the user is interested in health, health-related products can be included in the list. Also, if the user leads a busy life, products that can be easily cooked can be included in the list. Also, if the user is interested in a particular ingredient, the ingredient can be included in the list. In this way, the providing unit can provide a shopping list according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI and have the AI ​​customize the list.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit receives user data. The user data includes, for example, gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, eating habits, etc. The reception unit stores the data entered by the user in a database and sends it to the generation unit for analysis. Step 2: The generation unit uses generation AI to analyze the data received by the reception unit and generate original menus tailored to the individual's health condition and lifestyle. The generation AI uses neural networks and deep learning technology to analyze the user's data and generate optimal menus. The generation unit generates nutritionally balanced menus based on the user's health condition and easy-to-prepare menus that fit the user's lifestyle. Step 3: The acquisition unit acquires supermarket information. The supermarket information includes store locations, business hours, inventory information, etc. The acquisition unit collects supermarket information via the Internet and stores it in a database. Step 4: The providing unit provides the user with a shopping list based on the menu generated by the generating unit and the information acquired by the acquiring unit. The providing unit displays the shopping list on the user's terminal device. The shopping list includes necessary ingredients, quantities, purchasing locations, etc.

[0062] (Example 2) The menu generation system according to the embodiment of the present invention is a system that generates original menus for 365 days based on the user's personal data, tailored to the user's health, and provides a shopping list in cooperation with information on nearby supermarkets. This menu generation system uses AI to analyze the user's attributes (gender, address, family structure, height and weight, likes and dislikes, etc.), ideal monthly food expenses, dietary habits, and other personal requests, and generates an original menu tailored to the individual's health condition and lifestyle. Next, based on the generated menu and information on nearby supermarkets, the system shows what and how much to buy on which day in order to use up ingredients without wasting them, purchase good value ingredients, and keep food expenses within the budget. This is expected to be a perspective for housewives and people who cook at home every day. For example, the user inputs their own attributes and requests. For example, data such as gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and dietary habits are input. This data is analyzed by AI. Next, AI analyzes the input data and generates an original menu tailored to the individual's health condition and lifestyle. For example, a nutritionally balanced menu according to the health condition, an easy-to-make menu that matches the lifestyle, etc. are generated. Furthermore, a shopping list is generated based on the generated menu and information on nearby supermarkets. For example, what and how much to go shopping on which day is presented so that ingredients can be used up without waste, ingredients can be purchased at a good price, and food costs can be kept within the budget. This shopping list is displayed on the user's terminal device. With this mechanism, the user can easily obtain an original menu that matches his / her health condition and lifestyle, and can shop more efficiently. This is expected to be of help to housewives and people who cook at home every day. As a result, the menu generation system can provide an original menu that matches the health condition and lifestyle of the user, and generate an efficient shopping list.

[0063] The menu generation system according to the embodiment includes a reception unit, a generation unit, an acquisition unit, and a provision unit. The reception unit receives user data. The user data includes, for example, gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and dietary habits, but is not limited to such examples. The reception unit, for example, stores the data entered by the user in a database and transmits it to the generation unit for analysis. The generation unit uses a generation AI to analyze the data received by the reception unit and generates an original menu that matches the individual's health condition and lifestyle. The generation AI analyzes the user's data and generates an optimal menu, for example, using a neural network or deep learning technology. The generation unit generates, for example, a nutritionally balanced menu according to the health condition, or an easy-to-make menu that matches the lifestyle. The acquisition unit acquires supermarket information. The supermarket information includes, for example, the store's location, business hours, and inventory information, but is not limited to such examples. The acquisition unit, for example, collects supermarket information via the Internet and stores it in a database. The providing unit provides a shopping list to the user based on the menu generated by the generating unit and the information acquired by the acquiring unit. The providing unit, for example, displays the shopping list on the user's terminal device. The shopping list includes, for example, necessary ingredients, quantities, and purchasing locations, but is not limited to such examples. In this way, the menu generating system according to the embodiment can generate a menu tailored to health based on the user's data and provide an efficient shopping list.

[0064] The reception unit receives the user's data. The user's data includes, but is not limited to, for example, gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and eating habits. For example, the reception unit stores the data entered by the user in a database and transmits it to the generation unit for analysis. Specifically, the information entered by the user through a dedicated application or website is stored in the database in real time. This ensures that the user's data is always kept up to date and can be accessed immediately when needed by the generation unit. Furthermore, the reception unit also has a function of periodically updating the user's data, and the database is automatically updated every time the user enters new information. For example, if the user loses weight or the family structure changes, the information is reflected immediately. This allows the generation unit to always generate a menu based on the latest data. Furthermore, the reception unit encrypts data and restricts access to the data in order to protect the user's privacy. This minimizes the risk of the user's personal information being leaked to a third party. Furthermore, the reception unit also has a function of analyzing the user's input data and detecting input errors and inconsistencies. For example, if a user mistakenly enters inaccurate data, the system will automatically issue a warning and prompt the user to enter the correct data. This will improve the accuracy of the data and allow the generation unit to generate more accurate menus.

[0065] The generation unit uses the generation AI to analyze the data received by the reception unit and generate an original menu that matches the individual's health condition and lifestyle. The generation AI analyzes the user's data using, for example, neural networks and deep learning technology to generate an optimal menu. Specifically, the generation AI receives data on the user's health condition and lifestyle as input, and proposes an optimal menu based on past data and nutritional knowledge. For example, if the user wants to go on a diet, the generation AI generates a low-calorie menu that takes into account calorie restrictions. Also, if the user wants to consume a lot of a specific nutrient, the generation AI proposes a menu that uses ingredients that are rich in that nutrient. The generation AI also takes into account the user's preferences and allergy information to generate a menu that satisfies the user. For example, if the user dislikes a specific ingredient, the generation AI generates a menu that avoids that ingredient. The generation AI can also adjust the cooking time and difficulty to suit the user's lifestyle. For example, it proposes simple menus that can be made in a short time for busy users, and proposes elaborate dishes for users who have time. In addition, the generation AI can also propose menus that use seasonal ingredients, taking into account the characteristics of the season and region. This allows the user to always enjoy fresh and delicious meals. The menu generator performs final adjustments, such as calculating nutritional balance and calories, before providing the generated menu to the user. This allows the user to eat a healthy and balanced meal.

[0066] The acquisition unit acquires supermarket information. The supermarket information includes, for example, the location of the store, business hours, and inventory information, but is not limited to such examples. For example, the acquisition unit collects supermarket information via the Internet and stores it in a database. Specifically, the acquisition unit acquires the latest inventory information and sale information using the supermarket's official website and API. This allows the user to always create a shopping list based on the latest information. Furthermore, the acquisition unit also has a function of preferentially acquiring information on the nearest supermarket based on the user's location information. For example, the convenience of shopping is improved by preferentially displaying information on supermarkets near the user's home or workplace. Furthermore, the acquisition unit updates supermarket inventory information in real time to ensure that the user can purchase the ingredients he or she needs. For example, if a specific ingredient is out of stock, it also has a function of suggesting a substitute. This allows the user to smoothly prepare ingredients without any trouble when shopping. Furthermore, the acquisition unit also collects supermarket sale information and coupon information and provides it to the user. This allows the user to shop at a good price and save on monthly food expenses. The acquisition unit can centrally manage this information and link with other systems and departments as necessary. For example, the acquired information may be stored in a cloud server and made accessible to the providing unit, thereby enabling the acquiring unit to collect information efficiently and effectively and improve the performance of the entire system.

[0067] The providing unit provides a shopping list to the user based on the menu generated by the generating unit and the information acquired by the acquiring unit. The providing unit, for example, displays the shopping list on the user's terminal device. The shopping list includes, for example, necessary ingredients, quantities, and purchase locations, but is not limited to such examples. Specifically, the providing unit displays the shopping list on the user's smartphone or tablet through a dedicated app, allowing the user to easily access it. Furthermore, the providing unit updates the shopping list in real time, allowing the user to shop based on the latest information. For example, if the inventory information of a supermarket changes, the providing unit immediately updates the shopping list and notifies the user. The providing unit also has a function of suggesting substitutes taking into account the user's preferences and allergy information. For example, if a specific ingredient is out of stock, another ingredient with the same nutritional value is suggested. This allows the user to smoothly prepare ingredients without any trouble when shopping. Furthermore, the providing unit displays sale information and coupon information on the shopping list, allowing the user to shop more advantageously. For example, if a specific ingredient is on sale, the information is displayed on the shopping list to notify the user. This allows the user to save on monthly food expenses. The providing unit can collect user feedback and continuously improve the accuracy and convenience of the shopping list. For example, the providing unit can provide a function that allows the user to submit opinions and requests regarding the shopping list, and improve the system based on that feedback. This allows the providing unit to provide better service to the user and increase satisfaction.

[0068] The reception unit can receive data including the user's attributes and requests as user data. The reception unit receives data such as the user's gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, and dietary habits. This allows the reception unit to accurately receive the user's attributes and requests. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the data input by the user to AI and have the AI ​​analyze the data.

[0069] The generation unit can analyze the user's data using the generation AI and generate an original menu based on the individual's health data or lifestyle. The generation unit can, for example, use the generation AI to analyze the user's data and generate an original menu that matches the individual's health condition and lifestyle. The generation AI can, for example, use neural networks or deep learning technology to analyze the user's data and generate an optimal menu. The generation unit can, for example, generate a nutritionally balanced menu according to the health condition or an easy-to-make menu that matches the lifestyle. This allows the generation unit to generate a menu that matches the user's health condition and lifestyle. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's data into the generation AI and cause the generation AI to generate a menu.

[0070] The providing unit can display the shopping list on the user's terminal device. The providing unit displays the shopping list on the user's terminal device, for example, based on the menu generated by the generating unit and the information acquired by the acquiring unit. The shopping list includes, for example, necessary ingredients, quantities, and purchase locations, but is not limited to such examples. This allows the providing unit to allow the user to check the shopping list on the terminal device. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the menu generated by the generating unit and the information acquired by the acquiring unit to AI, and cause the AI ​​to generate the shopping list.

[0071] The reception unit can estimate the user's emotion and adjust the data input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the data input interface based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Also, when the user is in a hurry, voice input can be prioritized to enable data to be input quickly. In this way, the reception unit can provide an interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The reception unit can analyze the user's past data input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past data input history and suggests an optimal input method. For example, data that the user frequently inputs in the past is automatically displayed as a candidate. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest data to be used in a specific time period from the user's past input history. This allows the reception unit to suggest an optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past data input history to AI and have the AI ​​execute the suggestion of an optimal input method.

[0073] The reception unit can customize the input items based on the user's current living situation and areas of interest when inputting data. For example, the reception unit customizes the input items based on the user's current living situation and areas of interest when inputting data. For example, if the user is interested in health, health-related input items can be preferentially displayed. Also, if the user is busy, items that can be easily input can be preferentially displayed. Also, if the user is interested in a specific ingredient, input items related to that ingredient can be displayed. This allows the reception unit to provide input items according to the user's living situation and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest into AI and cause AI to customize the input items.

[0074] The reception unit can estimate the user's emotion and determine the priority of input data based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of input data based on the estimated user's emotion. For example, when the user is stressed, important data input is prioritized. Also, when the user is relaxed, detailed data input can be performed. Also, when the user is in a hurry, minimum data input can be prioritized. This allows the reception unit to determine the priority of data input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can display highly relevant input items preferentially in consideration of the geographical location information of the user when inputting data. For example, the reception unit displays highly relevant input items preferentially in consideration of the geographical location information of the user when inputting data. For example, if the user lives in a specific area, input items related to the area are preferentially displayed. In addition, if the user is traveling, input items related to the travel destination can be displayed. In addition, if the user is planning to move, input items related to the new address can be displayed. In this way, the reception unit can provide input items based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute the display of highly relevant input items.

[0076] The reception unit can analyze the user's social media activity at the time of data input and suggest related input items. For example, the reception unit can analyze the user's social media activity at the time of data input and suggest related input items. For example, the reception unit can suggest ingredients that the user frequently mentions on social media as input items. In addition, the reception unit can suggest related input items based on recipes shared by the user on social media. In addition, the reception unit can suggest related input items based on health information that the user follows on social media. In this way, the reception unit can provide input items based on the user's social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity to AI and cause the AI ​​to execute the suggestion of related input items.

[0077] The generation unit can estimate the user's emotion and adjust the menu expression method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the menu expression method based on the estimated user's emotion. For example, when the user is relaxed, a menu that proceeds at a leisurely pace is generated. Also, when the user is in a hurry, a menu that can be made easily and quickly can be generated. Also, when the user is excited, a menu that includes a visually stimulating effect can be generated. In this way, the generation unit can provide a menu expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the menu expression method.

[0078] The generating unit can adjust the nutritional balance based on the user's health condition when generating a menu. The generating unit adjusts the nutritional balance based on the user's health condition when generating a menu. For example, when the user is on a diet, a low-calorie, nutritionally balanced menu is generated. Also, when the user needs a specific nutrient, a menu including the nutrient can be generated. Also, when the user has an allergy, a menu not including an allergenic ingredient can be generated. In this way, the generating unit can provide a nutritionally balanced menu according to the user's health condition. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's health data into AI and cause AI to adjust the nutritional balance.

[0079] The generation unit can adjust the cooking time according to the user's lifestyle when generating a menu. The generation unit, for example, adjusts the cooking time according to the user's lifestyle when generating a menu. For example, if the user is busy, a menu that can be cooked in a short time is generated. Also, if the user has time, an elaborate menu can be generated. Also, if the user cooks during a specific time period, a menu that matches that time period can be generated. In this way, the generation unit can provide a menu with a cooking time that matches the user's lifestyle. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle data into AI and cause AI to adjust the cooking time.

[0080] The generation unit can estimate the user's emotion and adjust the level of detail of the menu based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the level of detail of the menu based on the estimated user's emotion. For example, when the user is relaxed, a menu including detailed explanations is generated. Also, when the user is in a hurry, a menu including concise explanations can be generated. Also, when the user is excited, a menu with visually stimulating effects can be generated. In this way, the generation unit can provide a level of detail of the menu according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the level of detail of the menu.

[0081] The generation unit can suggest a menu that matches the user's preferences by referring to the user's past meal history when generating a menu. The generation unit can suggest a menu that matches the user's preferences by referring to the user's past meal history when generating a menu. For example, the generation unit can suggest a similar menu based on dishes that the user liked to eat in the past. Also, based on ingredients that the user avoided in the past, it can suggest a menu that does not include those ingredients. Also, it can suggest a menu that matches a specific season from the user's past meal history. In this way, the generation unit can provide a menu that matches the user's preferences based on the user's past meal history. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past meal history into AI and cause the AI ​​to suggest a menu that matches the user's preferences.

[0082] The generation unit can generate a safe menu by considering the user's allergy information when generating a menu. The generation unit generates a safe menu by considering the user's allergy information, for example, when generating a menu. For example, if the user is allergic to a specific ingredient, a menu that does not include that ingredient is generated. In addition, if the user has multiple allergies, a menu that takes all of them into consideration can be generated. In addition, if the user discovers a new allergy, the menu can be generated by immediately reflecting that information. In this way, the generation unit can provide a safe menu based on the user's allergy information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's allergy information to AI and cause the AI ​​to generate a safe menu.

[0083] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the super information based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and adjusts the timing of acquiring the super information based on the estimated user's emotion. For example, when the user is relaxed, the super information is acquired periodically. Also, when the user is in a hurry, the super information can be acquired immediately when necessary. Also, when the user is stressed, the frequency of acquiring the super information can be reduced. In this way, the acquisition unit can provide the timing of acquiring the super information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring the super information.

[0084] The acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method when acquiring supermarket information. For example, the acquisition unit analyzes the user's past purchase history and selects an optimal acquisition method when acquiring supermarket information. For example, related supermarket information is acquired based on products frequently purchased by the user in the past. Also, product information to be purchased in a specific time period can be preferentially acquired from the user's past purchase history. Also, the acquisition unit can analyze the user's past purchase pattern and acquire optimal supermarket information. In this way, the acquisition unit can provide optimal supermarket information based on the user's past purchase history. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past purchase history into AI and cause AI to select an optimal acquisition method.

[0085] The acquisition unit can perform filtering based on the user's current living situation and field of interest when acquiring supermarket information. For example, the acquisition unit performs filtering based on the user's current living situation and field of interest when acquiring supermarket information. For example, if the user is interested in health, health-related product information is preferentially acquired. In addition, if the user is interested in a specific ingredient, product information related to that ingredient can also be acquired. In addition, if the user leads a busy life, product information that can be easily cooked can also be acquired preferentially. In this way, the acquisition unit can provide supermarket information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and field of interest into AI and cause AI to perform filtering.

[0086] The acquisition unit can estimate the user's emotion and determine the priority of the super information to be acquired based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and determines the priority of the super information to be acquired based on the estimated user's emotion. For example, when the user is relaxed, detailed super information is acquired preferentially. Also, when the user is in a hurry, the minimum necessary super information can be acquired preferentially. Also, when the user is stressed, important super information can be acquired preferentially. In this way, the acquisition unit can provide the priority of the super information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the super information.

[0087] The acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring the super information. For example, the acquisition unit preferentially acquires highly relevant information in consideration of the geographical location information of the user when acquiring the super information. For example, if the user lives in a specific area, super information related to the area is preferentially acquired. In addition, if the user is traveling, super information related to the travel destination can be acquired. In addition, if the user is planning to move, super information related to the new address can be acquired. In this way, the acquisition unit can provide super information based on the geographical location information of the user. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the user to AI and cause the AI ​​to acquire highly relevant information.

[0088] The acquisition unit can analyze the social media activity of the user and acquire related information when acquiring supermarket information. For example, the acquisition unit can analyze the social media activity of the user and acquire related information when acquiring supermarket information. For example, related supermarket information can be acquired based on products that the user frequently mentions on social media. Also, related product information can be acquired based on recipes that the user shares on social media. Also, related product information can be acquired based on health information that the user follows on social media. In this way, the acquisition unit can provide supermarket information based on the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity to AI and cause the AI ​​to acquire related information.

[0089] The providing unit can estimate the user's emotion and adjust the display method of the shopping list based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the shopping list based on the estimated user's emotion. For example, when the user is relaxed, a detailed shopping list is displayed. Also, when the user is in a hurry, a concise shopping list can be displayed. Also, when the user is stressed, a shopping list that is visually easy to read can be displayed. In this way, the providing unit can provide a display method of the shopping list according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the shopping list.

[0090] The providing unit can generate an optimal list by referring to the user's past purchase history when providing a shopping list. For example, the providing unit generates an optimal list by referring to the user's past purchase history when providing a shopping list. For example, the shopping list is generated based on products frequently purchased by the user in the past. Products to be purchased during a specific time period can also be included in the list from the user's past purchase history. The optimal shopping list can also be generated by analyzing the user's past purchase patterns. In this way, the providing unit can provide an optimal shopping list based on the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history into AI and cause AI to generate an optimal list.

[0091] The providing unit can customize the shopping list based on the user's current living situation when providing the shopping list. For example, the providing unit customizes the list based on the user's current living situation when providing the shopping list. For example, if the user is interested in health, health-related products can be included in the list. Also, if the user leads a busy life, products that can be easily cooked can be included in the list. Also, if the user is interested in a specific ingredient, the ingredient can be included in the list. In this way, the providing unit can provide a shopping list according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI and cause AI to customize the list.

[0092] The providing unit can estimate the user's emotion and determine the priority order of the shopping list based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority order of the shopping list based on the estimated user's emotion. For example, when the user is relaxed, a detailed shopping list can be preferentially displayed. Also, when the user is in a hurry, a shopping list with the minimum necessary items can be preferentially displayed. Also, when the user is stressed, important products can be preferentially included in the list. This allows the providing unit to provide the priority order of the shopping list according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of the shopping list.

[0093] The providing unit can generate an optimal list in consideration of the geographical location information of the user when providing a shopping list. The providing unit generates an optimal list in consideration of the geographical location information of the user when providing a shopping list, for example. For example, if the user lives in a specific area, products related to the area can be included in the list. Also, if the user is traveling, products related to the travel destination can be included in the list. Also, if the user is planning to move, products related to the new address can be included in the list. In this way, the providing unit can provide a shopping list based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to generate an optimal list.

[0094] The providing unit can analyze the social media activity of the user and propose the list when providing the shopping list. For example, the providing unit can analyze the social media activity of the user and propose the list when providing the shopping list. For example, the providing unit can include products that the user frequently mentions on social media in the list. Also, based on recipes shared by the user on social media, related products can be included in the list. Also, based on health information that the user follows on social media, related products can be included in the list. In this way, the providing unit can provide a shopping list based on the social media activity of the user. Part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity into AI and cause the AI ​​to execute the list suggestion.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] When accepting data of a user, the reception unit can refer to the user's past meal history and customize input items based on the dishes the user liked to eat in the past and the ingredients the user avoided. For example, the reception unit can preferentially display dishes that the user frequently ate in the past and suggest input items that do not include ingredients the user avoided. In addition, the reception unit can display input items that are suitable for a specific season based on the user's past meal history. This allows the reception unit to provide optimal input items based on the user's past meal history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past meal history to AI and have the AI ​​execute the suggestion of optimal input items.

[0097] When analyzing the user's data, the generation unit can analyze the user's social media activity and generate a related menu. For example, a menu including ingredients frequently mentioned by the user on social media is generated. Also, a related menu can be generated based on recipes shared by the user on social media. Also, a related menu can be generated based on health information followed by the user on social media. In this way, the generation unit can provide an optimal menu based on the user's social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into AI and cause the AI ​​to generate an optimal menu.

[0098] When displaying a shopping list on a user's terminal device, the providing unit can display highly relevant information preferentially, taking into consideration the user's geographical location information. For example, if a user lives in a specific area, products related to that area can be included in the list. Also, if a user is traveling, products related to the travel destination can be included in the list. Also, if a user is planning to move, products related to the new address can be included in the list. This allows the providing unit to provide a shopping list based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to AI and cause AI to generate an optimal list.

[0099] When acquiring supermarket information, the acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method. For example, related supermarket information can be acquired based on products that the user frequently purchased in the past. Also, it is possible to preferentially acquire product information to be purchased in a specific time period from the user's past purchase history. Also, it is possible to analyze the user's past purchase patterns and acquire optimal supermarket information. In this way, the acquisition unit can provide optimal supermarket information based on the user's past purchase history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the user's past purchase history into AI and cause the AI ​​to select the optimal acquisition method.

[0100] The reception unit can estimate the user's emotion and adjust the data input interface based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Also, when the user is in a hurry, voice input can be prioritized to enable data to be input quickly. In this way, the reception unit can provide an interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The generation unit can estimate the user's emotion and adjust the way of expressing the menu based on the estimated emotion of the user. For example, when the user is relaxed, a menu that proceeds at a leisurely pace can be generated. Also, when the user is in a hurry, a menu that can be made easily and quickly can be generated. Also, when the user is excited, a menu that includes a visually stimulating effect can be generated. In this way, the generation unit can provide a way of expressing the menu according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the menu.

[0102] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the super information based on the estimated emotion of the user. For example, when the user is relaxed, the super information is acquired periodically. When the user is in a hurry, the super information can be acquired immediately when necessary. When the user is stressed, the frequency of acquiring the super information can be reduced. This allows the acquisition unit to provide the timing of acquiring the super information according to the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring the super information.

[0103] The providing unit can estimate the user's emotion and adjust the display method of the shopping list based on the estimated user's emotion. For example, when the user is relaxed, a detailed shopping list can be displayed. Also, when the user is in a hurry, a concise shopping list can be displayed. Also, when the user is stressed, a shopping list that is visually easy to read can be displayed. This allows the providing unit to provide a display method of the shopping list according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the shopping list.

[0104] When providing a shopping list, the providing unit can generate an optimal list by referring to the user's past purchase history. For example, the shopping list is generated based on products that the user frequently purchased in the past. Also, products to be purchased during a specific time period can be included in the list from the user's past purchase history. Also, the providing unit can analyze the user's past purchase patterns and generate an optimal shopping list. In this way, the providing unit can provide an optimal shopping list based on the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history into AI and cause AI to generate an optimal list.

[0105] When providing the shopping list, the providing unit can customize the list based on the user's current living situation. For example, if the user is interested in health, health-related products can be included in the list. Also, if the user leads a busy life, products that can be easily cooked can be included in the list. Also, if the user is interested in a particular ingredient, the ingredient can be included in the list. In this way, the providing unit can provide a shopping list according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into AI and have the AI ​​customize the list.

[0106] The process flow of the second embodiment will be briefly described below.

[0107] Step 1: The reception unit receives user data. The user data includes, for example, gender, address, family structure, height and weight, likes and dislikes, ideal monthly food expenses, eating habits, etc. The reception unit stores the data entered by the user in a database and sends it to the generation unit for analysis. Step 2: The generation unit uses generation AI to analyze the data received by the reception unit and generate original menus tailored to the individual's health condition and lifestyle. The generation AI uses neural networks and deep learning technology to analyze the user's data and generate optimal menus. The generation unit generates nutritionally balanced menus based on the user's health condition and easy-to-prepare menus that fit the user's lifestyle. Step 3: The acquisition unit acquires supermarket information. The supermarket information includes store locations, business hours, inventory information, etc. The acquisition unit collects supermarket information via the Internet and stores it in a database. Step 4: The providing unit provides the user with a shopping list based on the menu generated by the generating unit and the information acquired by the acquiring unit. The providing unit displays the shopping list on the user's terminal device. The shopping list includes necessary ingredients, quantities, purchasing locations, etc.

[0108] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0109] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0110] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0111] For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives data input by the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's data using a generation AI to generate an original menu. For example, the acquisition unit acquires supermarket information from the Internet via the communication I / F 44 of the smart device 14. For example, the provision unit is realized by the output device 40 of the smart device 14, and displays a shopping list on the user's terminal device. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0115] 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.

[0116] 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 the voice according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0118] 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 performed in a secure state.

[0119] Fig. 4 shows an example of 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.

[0120] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0125] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0126] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and receives data input by the user through voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's data using a generation AI to generate an original menu. For example, the acquisition unit acquires supermarket information from the Internet via the communication I / F 44 of the smart glasses 214. For example, the provision unit is realized by the speaker 240 of the smart glasses 214, and provides the user with a shopping list by voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] 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.

[0136] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 voice indicating a user input for 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and receives data input by voice from the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's data using a generation AI to generate an original menu. For example, the acquisition unit acquires supermarket information from the Internet via the communication I / F 44 of the headset terminal 314. For example, the provision unit is realized by the display 343 of the headset terminal 314, and displays a shopping list on the user's terminal device. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0150] 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 performed in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0152] 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.

[0153] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0156] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] 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 a voice indicating a user input for 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.

[0158] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0160] For example, the reception unit is realized by the microphone 238 of the robot 414, and receives data input by voice from the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's data using a generation AI to generate an original menu. For example, the acquisition unit acquires supermarket information from the Internet via the communication I / F 44 of the robot 414. For example, the provision unit is realized by the speaker 240 of the robot 414, and provides a shopping list to the user by voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0162] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0164] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0166] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0168] 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.

[0169] 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 Universal Serial Bus (USB) 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.

[0170] In addition, 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 upon request from the data processing device 12.

[0171] 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.

[0172] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0173] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0174] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0175] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0177] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0179] (Appendix 1) A reception unit that receives user data; A generation unit that analyzes the data received by the reception unit and generates a menu based on the personal health data; An acquisition unit that acquires store information; a providing unit that provides the user with a shopping list based on the menu generated by the generating unit and the information acquired by the acquiring unit. A system characterized by: (Appendix 2) The reception unit is Data including the attributes of the user and the desires of the user is accepted as data of the user. 2. The system of claim 1, (Appendix 3) The generation unit is Generative AI analyzes user data and generates original menus based on personal health data or lifestyle. 2. The system of claim 1, (Appendix 4) The providing unit is Displaying the shopping list on the user's terminal device 2. The system of claim 1, (Appendix 5) The reception unit is To estimate a user's emotion and adjust a data input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past data entry history and suggest appropriate input methods 2. The system of claim 1, (Appendix 7) The reception unit is During data entry, customize the input fields based on the user's current life situation and interests 2. The system of claim 1, (Appendix 8) The reception unit is Infer user emotions and prioritize input data based on the inferred user emotions 2. The system of claim 1, (Appendix 9) The reception unit is During data entry, the system takes into account the user's geographic location and prioritizes the most relevant input fields. 2. The system of claim 1, (Appendix 10) The reception unit is Analyzes your social media activity and suggests relevant entries as you enter data 2. The system of claim 1, (Appendix 11) The generation unit is Estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 12) The generation unit is When generating a menu, adjust the nutritional balance based on the user's health condition 2. The system of claim 1, (Appendix 13) The generation unit is When creating a menu, adjust cooking times according to the user's lifestyle 2. The system of claim 1, (Appendix 14) The generation unit is The system estimates the user's emotions and adjusts the level of detail of the menu based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The generation unit is When generating a menu, the system refers to the user's past meal history and suggests menus that match their preferences. 2. The system of claim 1, (Appendix 16) The generation unit is When generating menus, generate safe menus by taking into account the user's allergy information 2. The system of claim 1, (Appendix 17) The acquisition unit is Estimate the user's emotions and adjust the timing of super information acquisition based on the estimated user's emotions. 2. The system of claim 1, (Appendix 18) The acquisition unit is When obtaining supermarket information, analyze the user's past purchase history and select the appropriate method of obtaining information. 2. The system of claim 1, (Appendix 19) The acquisition unit is When acquiring super information, filtering is performed based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 20) The acquisition unit is Estimate the user's emotions and prioritize the super information to be acquired based on the estimated user's emotions. 2. The system of claim 1, (Appendix 21) The acquisition unit is When retrieving super information, the system prioritizes retrieving information that is highly relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 22) The acquisition unit is When acquiring super information, the user's social media activity is analyzed to acquire relevant information. 2. The system of claim 1, (Appendix 23) The providing unit is Infer user emotions and adjust how the shopping list is displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The providing unit is When providing a shopping list, the system generates an optimal list by referring to the user's past purchase history. 2. The system of claim 1, (Appendix 25) The providing unit is When providing a shopping list, customize the list based on the user's current life situation 2. The system of claim 1, (Appendix 26) The providing unit is Infer user emotions and prioritize shopping lists based on the inferred user emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing a shopping list, generate an optimal list taking into account the user's geographic location information 2. The system of claim 1, (Appendix 28) The providing unit is When providing a shopping list, the app analyzes the user's social media activity to suggest lists. 2. The system of claim 1, [Explanation of symbols]

[0180] 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 unit that receives user data; A generation unit that analyzes the data received by the reception unit and generates a menu based on the personal health data; An acquisition unit that acquires store information; a providing unit that provides the user with a shopping list based on the menu generated by the generating unit and the information acquired by the acquiring unit. A system characterized by:

2. The reception unit is Data including the attributes of the user and the desires of the user is accepted as data of the user.

2. The system of claim 1 .

3. The generation unit is The generation AI analyzes the user's data and generates an original menu based on the user's personal health data or lifestyle.

2. The system of claim 1 .

4. The providing unit is Displaying the shopping list on the user's terminal device 2. The system of claim 1 .

5. The reception unit is Estimating the user's emotion and adjusting a data entry interface based on the estimated user's emotion.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past data entry history and suggest an appropriate entry method 2. The system of claim 1 .

7. The reception unit is During data entry, customizing input items based on the user's current life situation and areas of interest 2. The system of claim 1 .

8. The reception unit is Estimating the user's emotion and prioritizing input data based on the estimated user's emotion.

2. The system of claim 1 .

9. The reception unit is When inputting data, the system takes into consideration the user's geographic location information and prioritizes displaying relevant input items.

2. The system of claim 1 .

10. The reception unit is Upon data entry, analyze the user's social media activity and suggest relevant entries 2. The system of claim 1 .

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