System

Abstract

An object of the system according to the embodiment is to propose an optimal menu in accordance with the user's mood or physical condition.SOLUTION: A system according to an embodiment includes an input unit, a determination unit, a food input unit, a generation unit, and a display unit. The input unit inputs personal data. The determination unit determines the mood or physical condition of the user based on the personal data input by the input unit. The food material input unit inputs food materials in the house. The generation unit generates a menu based on the information obtained by the determination unit and the food input unit. The display unit displays the menu generated by the generation 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, it is difficult to automatically suggest a menu that matches the user's mood and physical condition, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu that matches the user's mood and physical condition. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a determination unit, an ingredient input unit, a generation unit, and a display unit. The input unit inputs personal data. The determination unit determines the user's mood or physical condition based on the personal data input by the input unit. The ingredient input unit inputs ingredients available at home. The generation unit generates a menu based on information obtained by the determination unit and the ingredient input unit. The display unit displays the menu generated by the generation unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu suggestion system according to an embodiment of the present invention utilizes a generation AI to propose optimal menus based on a user's personal data and household ingredients. The menu suggestion system allows users to input personal data, and the generation AI proposes menus tailored to the user's mood and physical condition. Furthermore, when a user inputs ingredients available at home, the system proposes menus that effectively utilize those ingredients. For example, even if a user is busy and doesn't have time to plan a menu, the menu suggestion system automatically proposes optimal menus, allowing the user to enjoy meals with peace of mind. Furthermore, the system proposes menus that take nutritional balance into consideration, helping users maintain a healthy diet. Furthermore, the system learns from user feedback and can propose more accurate menus. For example, if a user dislikes a proposed menu, the user can input the reason and the reason will be reflected in future suggestions. This allows for customization to suit the user's preferences. The menu suggestion system considers the user's preferences and nutritional balance and proposes optimal menus that effectively utilize household ingredients, thereby reducing the effort required for daily meal selection and supporting a healthy diet.

[0029] A menu suggestion system according to an embodiment includes an input unit, a determination unit, an ingredient input unit, a generation unit, and a display unit. The input unit allows a user to input personal data. Examples of personal data include, but are not limited to, age, gender, health status, and past meal history. The input unit can input the personal data using methods such as text input, voice input, and image input. The determination unit determines the user's mood or physical condition based on the personal data input by the input unit. The determination unit determines the user's mood or physical condition based on information such as survey results, vital signs, and self-reported information. The determination unit can also determine the user's mood or physical condition by detailed analysis of the user's diary or survey content. The ingredient input unit allows a user to input ingredients available at home. The ingredient input unit can input ingredients using methods such as barcode scanning, voice input, and text input. The generation unit generates a menu based on the information obtained by the determination unit and the ingredient input unit. The generation unit uses a generation AI to generate a menu that takes into account the user's mood, physical condition, and ingredients available at home. The generation unit generates a menu that takes into account, for example, nutritional balance. The generation unit can also receive and learn from user feedback and reflect this in subsequent suggestions. The display unit displays the menu generated by the generation unit. The display unit can display the menu using methods such as text display, graphical display, and audio guidance. This allows the menu suggestion system according to the embodiment to suggest optimal menus based on the user's personal data and ingredients available at home. For example, the output unit displays the suggested menu to the user via a web application or a mobile application. If the user desires feedback on paper, the results are printed using a printer. Sending the results via email provides quick feedback by sending them directly to the user. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI.

[0030] The generation unit can receive and learn from user feedback and reflect it in subsequent suggestions. The generation unit collects feedback, for example, by the user inputting ratings and comments on the suggested menu. The generation unit can also collect feedback based on the user's usage history. For example, the generation unit improves the next menu based on feedback the user provided on menus in the past. The generation unit can also reflect the user's feedback and generate a menu that suits their preferences. Furthermore, the generation unit can analyze the user's feedback and optimize the method for generating the menu. In this way, by reflecting the user's feedback, more accurate menu suggestions are possible.

[0031] The generation unit can generate a menu that takes into consideration nutritional balance. For example, the generation unit generates a menu that takes into consideration the balance of calories, vitamins, and minerals. The generation unit can generate an optimal menu based on the user's health condition and nutritional balance. For example, the generation unit generates a healthy menu that takes into consideration the user's nutritional balance. The generation unit can also comprehensively analyze the user's health condition and nutritional balance to generate an optimal menu. In this way, a healthy diet can be supported by generating a menu that takes into consideration nutritional balance.

[0032] The ingredient input unit can input ingredients using barcode scanning or voice input. The ingredient input unit can input ingredients using, for example, barcode scanning. Barcode scanning includes, for example, JAN codes and two-dimensional codes (e.g., QR Code (registered trademark)), but is not limited to these examples. The ingredient input unit can also input ingredients using voice input. Voice input includes, for example, languages ​​such as Japanese and English, but is not limited to these examples. For example, the ingredient input unit can scan a barcode using a smartphone camera to input ingredient information. The ingredient input unit can also use voice recognition technology to convert ingredient information dictated by the user into text data. This makes it easy to input ingredients using barcode scanning or voice input.

[0033] The determination unit can determine the mood and physical condition of the user based on the user's diary and questionnaires. The determination unit, for example, analyzes the contents of the user's diary to determine the mood and physical condition. The diary may include, but is not limited to, the user's emotions, physical condition, and activity details. The determination unit can also determine the mood and physical condition based on the user's responses to a questionnaire. The questionnaire may include, but is not limited to, questions about the user's mood, physical condition, and food preferences. Furthermore, the determination unit can comprehensively analyze the contents of the user's diary and questionnaire to determine the mood and physical condition. This allows the user's mood and physical condition to be determined based on the user's diary and questionnaires, thereby suggesting a more appropriate menu.

[0034] The display unit can display the suggested menu to the user. The display unit displays the suggested menu to the user by, for example, a text display, a graphical display, or an audio guide. The display unit provides a highly visible display method so that the user can easily check the suggested menu. For example, the display unit displays the menu through a web application or a mobile application. The display unit can also print the results using a printer if the user desires feedback in paper form. Furthermore, the display unit can also send the results directly to the user by email. In this way, by displaying the suggested menu to the user, the user can easily check the menu.

[0035] The input unit can analyze the user's past personal data input history and select the optimal input method. For example, if the user has frequently used voice input in the past, the input unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the input unit can preferentially suggest text input. Furthermore, if the user has frequently used image input in the past, the input unit can preferentially suggest image input. In this way, by analyzing the past input history, the optimal input method can be provided to the user.

[0036] The input unit can filter personal data based on the user's current living situation and health condition when inputting the data. For example, if the user is in good health, the input unit can prompt the user to input detailed personal data. If the user is in poor health, the input unit can also prompt the user to input simplified personal data. Furthermore, if the user is busy, the input unit can also prompt the user to input the minimum amount of personal data necessary. This allows for more appropriate data input by filtering data according to the user's living situation and health condition.

[0037] When inputting personal data, the input unit can select an input means according to the user's input method. For example, if the user desires voice input, the input unit can provide voice input with priority. Also, if the user desires text input, the input unit can provide text input with priority. Furthermore, if the user desires image input, the input unit can provide image input with priority. This allows the most appropriate means to be selected according to the user's input method, thereby making data input more efficient.

[0038] When inputting personal data, the input unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the input unit prioritizes inputting data related to the home. Furthermore, when the user is at work, the input unit can also prioritize inputting data related to the work place. Furthermore, when the user is traveling, the input unit can also prioritize inputting data related to the travel destination. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0039] When personal data is input, the input unit can analyze the user's social media activity and input related data. The input unit can input related data based on, for example, information shared by the user on social media. The input unit can also analyze the user's social media activity and input related data. Furthermore, the input unit can also input related data by referring to the activity of the user's friends on social media. In this way, related data can be input efficiently by analyzing social media activity.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting personal data. For example, if the user has preferred voice input in the past, the input unit can provide voice input with priority. Also, if the user has preferred text input in the past, the input unit can provide text input with priority. Furthermore, if the user has preferred image input in the past, the input unit can provide image input with priority. In this way, by reflecting past feedback, the optimal input method can be provided for the user.

[0041] When making a judgment, the judgment unit can judge the mood and physical condition by analyzing in detail the contents of the user's diary and questionnaires. The judgment unit, for example, analyzes the contents written in the user's diary to judge the mood and physical condition. The judgment unit can also judge the mood and physical condition based on the contents of the user's answers to questionnaires. Furthermore, the judgment unit can comprehensively analyze the contents of the user's diary and questionnaires to judge the mood and physical condition. In this way, by analyzing the contents of the diary and questionnaires in detail, the accuracy of judging the mood and physical condition is improved.

[0042] The judgment unit can improve the accuracy of judging the mood and physical condition by referring to the user's past health data when making a judgment. The judgment unit judges the mood and physical condition, for example, based on the user's past health data. The judgment unit can also predict the user's current mood and physical condition by referring to the user's past health data. Furthermore, the judgment unit can analyze the user's past health data to improve the accuracy of judging the mood and physical condition. As a result, by referring to the past health data, the accuracy of judging the mood and physical condition is improved.

[0043] The determination unit can determine the mood and physical condition of the user by taking into consideration the user's lifestyle and activity level. The determination unit, for example, determines the mood and physical condition based on the user's lifestyle. The determination unit can also determine the mood and physical condition based on the user's activity level. Furthermore, the determination unit can comprehensively analyze the user's lifestyle and activity level to determine the mood and physical condition. In this way, by taking into consideration the lifestyle and activity level, the accuracy of determining the mood and physical condition is improved.

[0044] When making a judgment, the judgment unit can judge the mood and physical condition by taking into account the user's geographical location information. For example, when the user is at home, the judgment unit judges the mood and physical condition based on data related to the home. Furthermore, when the user is at work, the judgment unit can also judge the mood and physical condition based on data related to the workplace. Furthermore, when the user is traveling, the judgment unit can also judge the mood and physical condition based on data related to the travel destination. In this way, by taking into account the geographical location information, the accuracy of judging the mood and physical condition is improved.

[0045] At the time of judgment, the judgment unit can analyze the user's social media activity to judge the mood and physical condition. The judgment unit can judge the mood and physical condition based on, for example, information shared by the user on social media. The judgment unit can also analyze the user's social media activity to judge the mood and physical condition. Furthermore, the judgment unit can also judge the mood and physical condition with reference to the activity of the user's friends on social media. In this way, the accuracy of judging mood and physical condition can be improved by analyzing social media activity.

[0046] At the time of judgment, the judgment unit can judge the mood and physical condition based on the user's occupation and lifestyle. The judgment unit judges the mood and physical condition based on, for example, the user's occupation. The judgment unit can also judge the mood and physical condition based on the user's lifestyle. Furthermore, the judgment unit can comprehensively analyze the user's occupation and lifestyle to judge the mood and physical condition. In this way, by taking the occupation and lifestyle into consideration, the accuracy of judging the mood and physical condition is improved.

[0047] When inputting ingredients, the ingredient input unit can analyze the user's past ingredient input history and select the optimal input method. For example, if the user has frequently used barcode scanning in the past, the ingredient input unit can preferentially suggest barcode scanning. Furthermore, if the user has frequently used voice input in the past, the ingredient input unit can also preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the ingredient input unit can also preferentially suggest text input. In this way, by analyzing the past input history, the optimal input method can be provided to the user.

[0048] The ingredient input unit can perform filtering based on the user's current ingredient inventory when inputting ingredients. For example, the ingredient input unit filters the ingredients to be input based on the ingredients the user has. The ingredient input unit can also filter the ingredients to be input by excluding ingredients that the user does not have. Furthermore, the ingredient input unit can also input optimal ingredients based on the user's ingredient inventory. This allows for efficient ingredient input by filtering based on the ingredient inventory.

[0049] When inputting ingredients, the ingredient input unit can select the optimal input means depending on the user's input method (barcode scanning, voice input, etc.). For example, if the user desires barcode scanning, the ingredient input unit can prioritize barcode scanning. Also, if the user desires voice input, the ingredient input unit can prioritize voice input. Furthermore, if the user desires text input, the ingredient input unit can prioritize text input. This allows the efficient input of ingredients by selecting the optimal means depending on the user's input method.

[0050] When inputting ingredients, the ingredient input unit can prioritize inputting highly relevant ingredients by taking into account the user's geographical location information. For example, when the user is at home, the ingredient input unit prioritizes inputting ingredients related to the user's home. Also, when the user is at work, the ingredient input unit can prioritize inputting ingredients related to the user's workplace. Furthermore, when the user is traveling, the ingredient input unit can prioritize inputting ingredients related to the user's travel destination. In this way, highly relevant ingredients can be prioritized by taking into account the geographical location information.

[0051] When inputting ingredients, the ingredient input unit can analyze the user's social media activity and input related ingredients. The ingredient input unit can input related ingredients based on, for example, information shared by the user on social media. The ingredient input unit can also analyze the user's social media activity and input related ingredients. Furthermore, the ingredient input unit can also input related ingredients by referring to the activity of the user's friends on social media. In this way, related ingredients can be input efficiently by analyzing social media activity.

[0052] The ingredient input unit can customize the input method by reflecting the user's past feedback when inputting ingredients. For example, if the user has previously preferred barcode scanning, the ingredient input unit can prioritize barcode scanning. Furthermore, if the user has previously preferred voice input, the ingredient input unit can also prioritize voice input. Furthermore, if the user has previously preferred text input, the ingredient input unit can also prioritize text input. This allows the optimal input method to be provided to the user by reflecting past feedback.

[0053] When generating a menu, the generation unit can generate an optimal menu by referring to the user's past meal history. The generation unit generates an optimal menu, for example, based on the user's past meal history. The generation unit can also generate a nutritionally balanced menu by referring to the user's past meal history. Furthermore, the generation unit can analyze the user's past meal history and generate a menu that suits the user's preferences. In this way, by referring to the past meal history, an optimal menu can be generated for the user.

[0054] When generating a menu, the generation unit can generate the menu taking into consideration the user's current health condition and nutritional balance. The generation unit generates an optimal menu based on, for example, the user's current health condition. The generation unit can also generate a healthy menu taking into consideration the user's nutritional balance. Furthermore, the generation unit can also generate an optimal menu by comprehensively analyzing the user's health condition and nutritional balance. In this way, a healthy menu can be generated by taking into consideration the health condition and nutritional balance.

[0055] The generation unit can improve the menu generation method by reflecting user feedback when generating a menu. For example, the generation unit improves the next menu based on feedback from the user regarding menus provided in the past. The generation unit can also reflect user feedback to generate a menu that suits the user's preferences. Furthermore, the generation unit can analyze user feedback and optimize the menu generation method. In this way, the feedback can be reflected to generate a menu that is optimal for the user.

[0056] When generating a menu, the generation unit can generate an optimal menu by taking into account the user's geographical location information. For example, when the user is at home, the generation unit generates a menu using ingredients related to the user's home. Furthermore, when the user is at work, the generation unit can also generate a menu using ingredients related to the user's workplace. Furthermore, when the user is traveling, the generation unit can also generate a menu using ingredients related to the user's travel destination. In this way, by taking into account the geographical location information, an optimal menu can be generated for the user.

[0057] When generating a menu, the generation unit can analyze the user's social media activity to generate the menu. The generation unit generates a related menu based on, for example, information shared by the user on social media. The generation unit can also analyze the user's social media activity to generate a related menu. Furthermore, the generation unit can also generate a related menu by referring to the activity of the user's friends on social media. In this way, the analysis of social media activity can generate an optimal menu for the user.

[0058] When generating a menu, the generation unit can customize the menu generation method by reflecting the user's past feedback. For example, the generation unit customizes the next menu based on feedback the user has provided on menus in the past. The generation unit can also generate a menu that matches the user's preferences by reflecting the user's feedback. Furthermore, the generation unit can analyze the user's feedback and optimize the menu generation method. In this way, the optimal menu for the user can be generated by reflecting the user's past feedback.

[0059] When displaying a menu, the display unit can select the optimal display method by referring to the user's past operation history. The display unit provides the optimal display method, for example, based on the user's preferred display method in the past. The display unit can also provide a highly visible display method by referring to the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide a display method that suits the user's preferences. In this way, the optimal display method can be provided to the user by referring to the past operation history.

[0060] The display unit can customize the display content according to the user's current task when displaying a menu. For example, if the user is cooking, the display unit can prioritize displaying cooking steps. Also, if the user is shopping, the display unit can prioritize displaying a list of necessary ingredients. Furthermore, if the user is eating, the display unit can prioritize displaying nutritional information. In this way, by customizing the display content according to the current task, it is possible to provide the user with the most appropriate information.

[0061] The display unit can improve the display method by reflecting user feedback when displaying a menu. For example, the display unit can improve the next display method based on feedback from the user regarding the display method provided in the past. The display unit can also reflect the user's feedback and provide a display method that suits the user's preferences. Furthermore, the display unit can analyze the user's feedback and optimize the display method. In this way, the display unit can provide the optimal display method for the user by reflecting the feedback.

[0062] When displaying a menu, the display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, the optimal display method can be provided to the user by taking into account the device information.

[0063] When displaying a menu, the display unit can make the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the display unit can provide the display content in that language. This allows the display content to be optimal for the user by supporting multiple languages ​​according to the language setting.

[0064] When displaying a menu, the display unit can customize the display content based on the user's occupation and lifestyle. For example, if the user has a busy occupation, the display unit can prioritize displaying menus with short cooking times. Furthermore, if the user has a health-conscious lifestyle, the display unit can also prioritize displaying nutritionally balanced menus. Furthermore, if the user is outdoorsy, the display unit can also prioritize displaying menus that use ingredients that are easy to carry. In this way, by customizing the display content based on the user's occupation and lifestyle, it is possible to provide the user with the most suitable information.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The input unit can automatically complete the input of ingredients based on the user's past meal history. For example, it can prioritize and suggest ingredients that the user has frequently used in the past. The input unit can also exclude ingredients that the user has avoided in the past. Furthermore, the input unit can analyze the user's past meal history and suggest ingredients according to the season or event. This makes it possible to efficiently input ingredients by utilizing the user's past meal history.

[0067] The display unit can monitor the user's device usage status and select the optimal display timing. For example, if the user frequently uses a smartphone, it can provide a display optimized for the smartphone. If the user uses a tablet, it can also provide a display tailored to a larger screen. Furthermore, if the user uses a desktop, it can also provide a display including detailed information. This allows the optimal display timing to be selected according to the device usage status, enabling a display with high visibility for the user.

[0068] The determination unit can predict patterns of mood and physical condition fluctuations based on the user's past health data. For example, if the user has tended to get sick during a particular season in the past, it can suggest a menu that matches that season. Also, if the user has tended to feel stressed before a particular event in the past, it can suggest a relaxing menu that matches that event. Furthermore, it can analyze the user's past health data and suggest menus that support long-term health management. In this way, by utilizing past health data, it is possible to predict patterns of mood and physical condition fluctuations and provide more appropriate menus.

[0069] The input unit can suggest regional ingredients and dishes based on the user's geographical location information. For example, if the user is in a specific region, ingredients that are easily available in that region can be suggested. If the user is traveling, dishes using local specialties of the destination can be suggested. Furthermore, if the user is overseas, traditional dishes of that country can be suggested. In this way, by utilizing geographical location information, regional ingredients and dishes can be suggested, providing the user with a new dining experience.

[0070] The generation unit can optimize ingredient combinations based on the user's past eating history. For example, it can prioritize and suggest ingredient combinations that the user has previously preferred. It can also exclude ingredient combinations that the user has previously avoided. It can also analyze the user's past eating history and suggest new ingredient combinations. In this way, by utilizing the user's past eating history, it is possible to optimize ingredient combinations and provide the user with a new eating experience.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The user inputs personal data into the input unit. The personal data includes age, gender, health status, and past dietary history. The input unit can input personal data by text input, voice input, image input, or other methods. Step 2: The determination unit determines the user's mood or physical condition based on the personal data input by the input unit. The determination unit determines the user's mood or physical condition based on information such as questionnaire results, vital signs, and self-reporting. The determination unit can also determine the user's mood or physical condition by analyzing the details of the user's diary and questionnaires. Step 3: The ingredient input unit allows the user to input ingredients at home using methods such as barcode scanning, voice input, and text input. Step 4: The generation unit generates a menu based on the information obtained by the judgment unit and the ingredient input unit. Using generation AI, the generation unit generates a menu that takes into account the user's mood, physical condition, and ingredients available at home. The generation unit generates a menu that takes nutritional balance into consideration, receives user feedback, learns from it, and can reflect this in future suggestions. Step 5: The display unit displays the menu generated by the generation unit. The display unit can display the menu using methods such as text display, graphical display, and audio guidance. For example, the suggested menu is displayed to the user through a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0073] (Example 2) A menu suggestion system according to an embodiment of the present invention utilizes a generation AI to propose optimal menus based on a user's personal data and household ingredients. The menu suggestion system allows users to input personal data, and the generation AI proposes menus tailored to the user's mood and physical condition. Furthermore, when a user inputs ingredients available at home, the system proposes menus that effectively utilize those ingredients. For example, even if a user is busy and doesn't have time to plan a menu, the menu suggestion system automatically proposes optimal menus, allowing the user to enjoy meals with peace of mind. Furthermore, the system proposes menus that take nutritional balance into consideration, helping users maintain a healthy diet. Furthermore, the system learns from user feedback and can propose more accurate menus. For example, if a user dislikes a proposed menu, the user can input the reason and the reason will be reflected in future suggestions. This allows for customization to suit the user's preferences. The menu suggestion system considers the user's preferences and nutritional balance and proposes optimal menus that effectively utilize household ingredients, thereby reducing the effort required for daily meal selection and supporting a healthy diet.

[0074] A menu suggestion system according to an embodiment includes an input unit, a determination unit, an ingredient input unit, a generation unit, and a display unit. The input unit allows a user to input personal data. Examples of personal data include, but are not limited to, age, gender, health status, and past meal history. The input unit can input the personal data using methods such as text input, voice input, and image input. The determination unit determines the user's mood or physical condition based on the personal data input by the input unit. The determination unit determines the user's mood or physical condition based on information such as survey results, vital signs, and self-reported information. The determination unit can also determine the user's mood or physical condition by detailed analysis of the user's diary or survey content. The ingredient input unit allows a user to input ingredients available at home. The ingredient input unit can input ingredients using methods such as barcode scanning, voice input, and text input. The generation unit generates a menu based on the information obtained by the determination unit and the ingredient input unit. The generation unit uses a generation AI to generate a menu that takes into account the user's mood, physical condition, and ingredients available at home. The generation unit generates a menu that takes into account, for example, nutritional balance. The generation unit can also receive and learn from user feedback and reflect this in subsequent suggestions. The display unit displays the menu generated by the generation unit. The display unit can display the menu using methods such as text display, graphical display, and audio guidance. This allows the menu suggestion system according to the embodiment to suggest optimal menus based on the user's personal data and ingredients available at home. For example, the output unit displays the suggested menu to the user via a web application or a mobile application. If the user desires feedback on paper, the results are printed using a printer. Sending the results via email provides quick feedback by sending them directly to the user. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The generation unit can receive and learn from user feedback and reflect it in subsequent suggestions. The generation unit collects feedback, for example, by the user inputting ratings and comments on the suggested menu. The generation unit can also collect feedback based on the user's usage history. For example, the generation unit improves the next menu based on feedback the user provided on menus in the past. The generation unit can also reflect the user's feedback and generate a menu that suits their preferences. Furthermore, the generation unit can analyze the user's feedback and optimize the method for generating the menu. In this way, by reflecting the user's feedback, more accurate menu suggestions are possible.

[0076] The generation unit can generate a menu that takes into consideration nutritional balance. For example, the generation unit generates a menu that takes into consideration the balance of calories, vitamins, and minerals. The generation unit can generate an optimal menu based on the user's health condition and nutritional balance. For example, the generation unit generates a healthy menu that takes into consideration the user's nutritional balance. The generation unit can also comprehensively analyze the user's health condition and nutritional balance to generate an optimal menu. In this way, a healthy diet can be supported by generating a menu that takes into consideration nutritional balance.

[0077] The ingredient input unit can input ingredients using barcode scanning or voice input. The ingredient input unit can input ingredients using, for example, barcode scanning. Barcode scanning includes, but is not limited to, JAN codes, QR codes, etc. The ingredient input unit can also input ingredients using voice input. Voice input includes, but is not limited to, languages ​​such as Japanese and English. For example, the ingredient input unit can scan a barcode using a smartphone camera to input ingredient information. The ingredient input unit can also use voice recognition technology to convert ingredient information dictated by the user into text data. This simplifies the input of ingredients by using barcode scanning or voice input.

[0078] The determination unit can determine the mood and physical condition of the user based on the user's diary and questionnaires. The determination unit, for example, analyzes the contents of the user's diary to determine the mood and physical condition. The diary may include, but is not limited to, the user's emotions, physical condition, and activity details. The determination unit can also determine the mood and physical condition based on the user's responses to a questionnaire. The questionnaire may include, but is not limited to, questions about the user's mood, physical condition, and food preferences. Furthermore, the determination unit can comprehensively analyze the contents of the user's diary and questionnaire to determine the mood and physical condition. This allows the user's mood and physical condition to be determined based on the user's diary and questionnaires, thereby suggesting a more appropriate menu.

[0079] The display unit can display the suggested menu to the user. The display unit displays the suggested menu to the user by, for example, a text display, a graphical display, or an audio guide. The display unit provides a highly visible display method so that the user can easily check the suggested menu. For example, the display unit displays the menu through a web application or a mobile application. The display unit can also print the results using a printer if the user desires feedback in paper form. Furthermore, the display unit can also send the results directly to the user by email. In this way, by displaying the suggested menu to the user, the user can easily check the menu.

[0080] The input unit can estimate the user's emotions and adjust the timing of personal data input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit prompts the user to input personal data during a time when the user is able to relax. Furthermore, if the user is relaxed, the input unit can also prompt the user to input personal data at that time. Furthermore, if the user is busy, the input unit can also prompt the user to input personal data during a time when the user is free. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. This allows data to be input at a more appropriate time by adjusting the timing of personal data input according to the user's emotions.

[0081] The input unit can analyze the user's past personal data input history and select the optimal input method. For example, if the user has frequently used voice input in the past, the input unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the input unit can preferentially suggest text input. Furthermore, if the user has frequently used image input in the past, the input unit can preferentially suggest image input. In this way, by analyzing the past input history, the optimal input method can be provided to the user.

[0082] The input unit can filter personal data based on the user's current living situation and health condition when inputting the data. For example, if the user is in good health, the input unit can prompt the user to input detailed personal data. If the user is in poor health, the input unit can also prompt the user to input simplified personal data. Furthermore, if the user is busy, the input unit can also prompt the user to input the minimum amount of personal data necessary. This allows for more appropriate data input by filtering data according to the user's living situation and health condition.

[0083] When inputting personal data, the input unit can select an input means according to the user's input method. For example, if the user desires voice input, the input unit can provide voice input with priority. Also, if the user desires text input, the input unit can provide text input with priority. Furthermore, if the user desires image input, the input unit can provide image input with priority. This allows the most appropriate means to be selected according to the user's input method, thereby making data input more efficient.

[0084] The input unit can estimate the user's emotions and determine the priority of personal data to be input based on the estimated user emotions. For example, when the user is feeling stressed, the input unit prioritizes input of only important data. Furthermore, when the user is relaxed, the input unit can also prioritize input of detailed data. Furthermore, when the user is busy, the input unit can prioritize input of the minimum necessary data. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important data can be input preferentially by determining the priority of data according to the user's emotions.

[0085] When inputting personal data, the input unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the input unit prioritizes inputting data related to the home. Furthermore, when the user is at work, the input unit can also prioritize inputting data related to the work place. Furthermore, when the user is traveling, the input unit can also prioritize inputting data related to the travel destination. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0086] When personal data is input, the input unit can analyze the user's social media activity and input related data. The input unit can input related data based on, for example, information shared by the user on social media. The input unit can also analyze the user's social media activity and input related data. Furthermore, the input unit can also input related data by referring to the activity of the user's friends on social media. In this way, related data can be input efficiently by analyzing social media activity.

[0087] The input unit can customize the input method by reflecting the user's past feedback when inputting personal data. For example, if the user has preferred voice input in the past, the input unit can provide voice input with priority. Also, if the user has preferred text input in the past, the input unit can provide text input with priority. Furthermore, if the user has preferred image input in the past, the input unit can provide image input with priority. In this way, by reflecting past feedback, the optimal input method can be provided for the user.

[0088] The judgment unit can estimate the user's emotions and adjust the judgment criteria for mood and physical condition based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit can suggest a relaxing menu. Furthermore, if the user is relaxed, the judgment unit can also suggest a nutritionally balanced menu. Furthermore, if the user is tired, the judgment unit can also suggest an easy-to-prepare menu. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the judgment criteria based on the user's emotions, more appropriate menus can be suggested.

[0089] When making a judgment, the judgment unit can judge the mood and physical condition by analyzing in detail the contents of the user's diary and questionnaires. The judgment unit, for example, analyzes the contents written in the user's diary to judge the mood and physical condition. The judgment unit can also judge the mood and physical condition based on the contents of the user's answers to questionnaires. Furthermore, the judgment unit can comprehensively analyze the contents of the user's diary and questionnaires to judge the mood and physical condition. In this way, by analyzing the contents of the diary and questionnaires in detail, the accuracy of judging the mood and physical condition is improved.

[0090] The judgment unit can improve the accuracy of judging the mood and physical condition by referring to the user's past health data when making a judgment. The judgment unit judges the mood and physical condition, for example, based on the user's past health data. The judgment unit can also predict the user's current mood and physical condition by referring to the user's past health data. Furthermore, the judgment unit can analyze the user's past health data to improve the accuracy of judging the mood and physical condition. As a result, by referring to the past health data, the accuracy of judging the mood and physical condition is improved.

[0091] The determination unit can determine the mood and physical condition of the user by taking into consideration the user's lifestyle and activity level. The determination unit, for example, determines the mood and physical condition based on the user's lifestyle. The determination unit can also determine the mood and physical condition based on the user's activity level. Furthermore, the determination unit can comprehensively analyze the user's lifestyle and activity level to determine the mood and physical condition. In this way, by taking into consideration the lifestyle and activity level, the accuracy of determining the mood and physical condition is improved.

[0092] The determination unit can estimate the user's emotions and adjust the display method of the mood and physical condition based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit can provide a simple, highly visible display method. If the user is relaxed, the determination unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the determination unit can also provide a display method that focuses on the main points. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the display method based on the user's emotions, a highly visible display can be achieved.

[0093] When making a judgment, the judgment unit can judge the mood and physical condition by taking into account the user's geographical location information. For example, when the user is at home, the judgment unit judges the mood and physical condition based on data related to the home. Furthermore, when the user is at work, the judgment unit can also judge the mood and physical condition based on data related to the workplace. Furthermore, when the user is traveling, the judgment unit can also judge the mood and physical condition based on data related to the travel destination. In this way, by taking into account the geographical location information, the accuracy of judging the mood and physical condition is improved.

[0094] At the time of judgment, the judgment unit can analyze the user's social media activity to judge the mood and physical condition. The judgment unit can judge the mood and physical condition based on, for example, information shared by the user on social media. The judgment unit can also analyze the user's social media activity to judge the mood and physical condition. Furthermore, the judgment unit can also judge the mood and physical condition with reference to the activity of the user's friends on social media. In this way, the accuracy of judging mood and physical condition can be improved by analyzing social media activity.

[0095] At the time of judgment, the judgment unit can judge the mood and physical condition based on the user's occupation and lifestyle. The judgment unit judges the mood and physical condition based on, for example, the user's occupation. The judgment unit can also judge the mood and physical condition based on the user's lifestyle. Furthermore, the judgment unit can comprehensively analyze the user's occupation and lifestyle to judge the mood and physical condition. In this way, by taking the occupation and lifestyle into consideration, the accuracy of judging the mood and physical condition is improved.

[0096] The ingredient input unit can estimate the user's emotions and adjust the timing of ingredient input based on the estimated user emotions. For example, if the user is feeling stressed, the ingredient input unit can prompt the user to input ingredients at a time when the user is able to relax. Furthermore, if the user is relaxed, the ingredient input unit can also prompt the user to input ingredients at that time. Furthermore, if the user is busy, the ingredient input unit can also prompt the user to input ingredients at a time when the user is not busy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the timing of ingredient input to be adjusted according to the user's emotions, allowing ingredients to be input at a more appropriate time.

[0097] When inputting ingredients, the ingredient input unit can analyze the user's past ingredient input history and select the optimal input method. For example, if the user has frequently used barcode scanning in the past, the ingredient input unit can preferentially suggest barcode scanning. Furthermore, if the user has frequently used voice input in the past, the ingredient input unit can also preferentially suggest voice input. Furthermore, if the user has frequently used text input in the past, the ingredient input unit can also preferentially suggest text input. In this way, by analyzing the past input history, the optimal input method can be provided to the user.

[0098] The ingredient input unit can perform filtering based on the user's current ingredient inventory when inputting ingredients. For example, the ingredient input unit filters the ingredients to be input based on the ingredients the user has. The ingredient input unit can also filter the ingredients to be input by excluding ingredients that the user does not have. Furthermore, the ingredient input unit can also input optimal ingredients based on the user's ingredient inventory. This allows for efficient ingredient input by filtering based on the ingredient inventory.

[0099] When inputting ingredients, the ingredient input unit can select the optimal input means depending on the user's input method (barcode scanning, voice input, etc.). For example, if the user desires barcode scanning, the ingredient input unit can prioritize barcode scanning. Also, if the user desires voice input, the ingredient input unit can prioritize voice input. Furthermore, if the user desires text input, the ingredient input unit can prioritize text input. This allows the efficient input of ingredients by selecting the optimal means depending on the user's input method.

[0100] The ingredient input unit can estimate the user's emotions and determine the priority of ingredients to be input based on the estimated user's emotions. For example, if the user is feeling stressed, the ingredient input unit can prioritize inputting ingredients that are easy to cook. Furthermore, if the user is relaxed, the ingredient input unit can also prioritize inputting ingredients that are highly nutritious. Furthermore, if the user is busy, the ingredient input unit can also prioritize inputting ingredients that require a short cooking time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. In this way, by determining the priority of ingredients according to the user's emotions, important ingredients can be input preferentially.

[0101] When inputting ingredients, the ingredient input unit can prioritize inputting highly relevant ingredients by taking into account the user's geographical location information. For example, when the user is at home, the ingredient input unit prioritizes inputting ingredients related to the user's home. Also, when the user is at work, the ingredient input unit can prioritize inputting ingredients related to the user's workplace. Furthermore, when the user is traveling, the ingredient input unit can prioritize inputting ingredients related to the user's travel destination. In this way, highly relevant ingredients can be prioritized by taking into account the geographical location information.

[0102] When inputting ingredients, the ingredient input unit can analyze the user's social media activity and input related ingredients. The ingredient input unit can input related ingredients based on, for example, information shared by the user on social media. The ingredient input unit can also analyze the user's social media activity and input related ingredients. Furthermore, the ingredient input unit can also input related ingredients by referring to the activity of the user's friends on social media. In this way, related ingredients can be input efficiently by analyzing social media activity.

[0103] The ingredient input unit can customize the input method by reflecting the user's past feedback when inputting ingredients. For example, if the user has previously preferred barcode scanning, the ingredient input unit can prioritize barcode scanning. Furthermore, if the user has previously preferred voice input, the ingredient input unit can also prioritize voice input. Furthermore, if the user has previously preferred text input, the ingredient input unit can also prioritize text input. This allows the optimal input method to be provided to the user by reflecting past feedback.

[0104] The generation unit can estimate the user's emotions and adjust the menu generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a relaxing menu. Furthermore, if the user is relaxed, the generation unit can also generate a nutritionally balanced menu. Furthermore, if the user is tired, the generation unit can also generate an easy-to-make menu. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the generation of a more appropriate menu by adjusting the menu generation method according to the user's emotions.

[0105] When generating a menu, the generation unit can generate an optimal menu by referring to the user's past meal history. The generation unit generates an optimal menu, for example, based on the user's past meal history. The generation unit can also generate a nutritionally balanced menu by referring to the user's past meal history. Furthermore, the generation unit can analyze the user's past meal history and generate a menu that suits the user's preferences. In this way, by referring to the past meal history, an optimal menu can be generated for the user.

[0106] When generating a menu, the generation unit can generate the menu taking into consideration the user's current health condition and nutritional balance. The generation unit generates an optimal menu based on, for example, the user's current health condition. The generation unit can also generate a healthy menu taking into consideration the user's nutritional balance. Furthermore, the generation unit can also generate an optimal menu by comprehensively analyzing the user's health condition and nutritional balance. In this way, a healthy menu can be generated by taking into consideration the health condition and nutritional balance.

[0107] The generation unit can improve the menu generation method by reflecting user feedback when generating a menu. For example, the generation unit improves the next menu based on feedback from the user regarding menus provided in the past. The generation unit can also reflect user feedback to generate a menu that suits the user's preferences. Furthermore, the generation unit can analyze user feedback and optimize the menu generation method. In this way, the feedback can be reflected to generate a menu that is optimal for the user.

[0108] The generation unit can estimate the user's emotions and prioritize menu items based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize suggesting relaxing menu items. Furthermore, if the user is relaxed, the generation unit can prioritize suggesting nutritionally balanced menu items. Furthermore, if the user is tired, the generation unit can prioritize suggesting easy-to-prepare menu items. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important menu items can be prioritized by prioritizing menu items according to the user's emotions.

[0109] When generating a menu, the generation unit can generate an optimal menu by taking into account the user's geographical location information. For example, when the user is at home, the generation unit generates a menu using ingredients related to the user's home. Furthermore, when the user is at work, the generation unit can also generate a menu using ingredients related to the user's workplace. Furthermore, when the user is traveling, the generation unit can also generate a menu using ingredients related to the user's travel destination. In this way, by taking into account the geographical location information, an optimal menu can be generated for the user.

[0110] When generating a menu, the generation unit can analyze the user's social media activity to generate the menu. The generation unit generates a related menu based on, for example, information shared by the user on social media. The generation unit can also analyze the user's social media activity to generate a related menu. Furthermore, the generation unit can also generate a related menu by referring to the activity of the user's friends on social media. In this way, the analysis of social media activity can generate an optimal menu for the user.

[0111] When generating a menu, the generation unit can customize the menu generation method by reflecting the user's past feedback. For example, the generation unit customizes the next menu based on feedback the user has provided on menus in the past. The generation unit can also generate a menu that matches the user's preferences by reflecting the user's feedback. Furthermore, the generation unit can analyze the user's feedback and optimize the menu generation method. In this way, the optimal menu for the user can be generated by reflecting the user's past feedback.

[0112] The display unit can estimate the user's emotions and adjust the menu display method based on the estimated user emotions. For example, when the user is feeling stressed, the display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that focuses on the main points. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by adjusting the display method based on the user's emotions, a highly visible display can be achieved.

[0113] When displaying a menu, the display unit can select the optimal display method by referring to the user's past operation history. The display unit provides the optimal display method, for example, based on the user's preferred display method in the past. The display unit can also provide a highly visible display method by referring to the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide a display method that suits the user's preferences. In this way, the optimal display method can be provided to the user by referring to the past operation history.

[0114] The display unit can customize the display content according to the user's current task when displaying a menu. For example, if the user is cooking, the display unit can prioritize displaying cooking steps. Also, if the user is shopping, the display unit can prioritize displaying a list of necessary ingredients. Furthermore, if the user is eating, the display unit can prioritize displaying nutritional information. In this way, by customizing the display content according to the current task, it is possible to provide the user with the most appropriate information.

[0115] The display unit can improve the display method by reflecting user feedback when displaying a menu. For example, the display unit can improve the next display method based on feedback from the user regarding the display method provided in the past. The display unit can also reflect the user's feedback and provide a display method that suits the user's preferences. Furthermore, the display unit can analyze the user's feedback and optimize the display method. In this way, the display unit can provide the optimal display method for the user by reflecting the feedback.

[0116] The display unit can estimate the user's emotions and adjust the display order of menu items based on the estimated user emotions. For example, if the user is feeling stressed, the display unit can prioritize displaying easy-to-prepare menu items. Furthermore, if the user is relaxed, the display unit can prioritize displaying nutritionally balanced menu items. Furthermore, if the user is tired, the display unit can prioritize displaying menu items with short cooking times. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, important menu items can be prioritized by adjusting the display order based on the user's emotions.

[0117] When displaying a menu, the display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, the optimal display method can be provided to the user by taking into account the device information.

[0118] When displaying a menu, the display unit can make the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the display unit can provide the display content in that language. This allows the display content to be optimal for the user by supporting multiple languages ​​according to the language setting.

[0119] When displaying a menu, the display unit can customize the display content based on the user's occupation and lifestyle. For example, if the user has a busy occupation, the display unit can prioritize displaying menus with short cooking times. Furthermore, if the user has a health-conscious lifestyle, the display unit can also prioritize displaying nutritionally balanced menus. Furthermore, if the user is outdoorsy, the display unit can also prioritize displaying menus that use ingredients that are easy to carry. In this way, by customizing the display content based on the user's occupation and lifestyle, it is possible to provide the user with the most suitable information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, determination unit, ingredient input unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input the user's personal data using the reception device 38 of the smart device 14. The determination unit determines the user's mood and physical condition using the specific processing unit 290 of the data processing device 12. The ingredient input unit can input ingredients available at home using the camera 42 of the smart device 14. The generation unit generates a menu using a generation AI by the specific processing unit 290 of the data processing device 12. The display unit can display the generated menu using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, determination unit, ingredient input unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input the user's personal data using the microphone 238 of the smart glasses 214. The determination unit determines the user's mood and physical condition using the specific processing unit 290 of the data processing device 12. The ingredient input unit can input ingredients available at home using the camera 42 of the smart glasses 214. The generation unit generates a menu using generation AI by the specific processing unit 290 of the data processing device 12. The display unit can provide audio guidance of the generated menu using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, determination unit, ingredient input unit, generation unit, and display unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can input the user's personal data using the microphone 238 of the headset terminal 314. The determination unit determines the user's mood and physical condition using the specific processing unit 290 of the data processing device 12. The ingredient input unit can input ingredients available at home using the camera 42 of the headset terminal 314. The generation unit generates a menu using generation AI by the specific processing unit 290 of the data processing device 12. The display unit can display the generated menu using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, determination unit, ingredient input unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input the user's personal data using the microphone 238 of the robot 414. The determination unit determines the user's mood and physical condition using the specific processing unit 290 of the data processing device 12. The ingredient input unit can input ingredients available at home using the camera 42 of the robot 414. The generation unit generates a menu using a generation AI by the specific processing unit 290 of the data processing device 12. The display unit can provide audio guidance of the generated menu using the speaker 240 of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The input unit can automatically complete the input of ingredients based on the user's past meal history. For example, it can prioritize and suggest ingredients that the user has frequently used in the past. The input unit can also exclude ingredients that the user has avoided in the past. Furthermore, the input unit can analyze the user's past meal history and suggest ingredients according to the season or event. This makes it possible to efficiently input ingredients by utilizing the user's past meal history.

[0122] The generation unit can estimate the user's emotions and adjust the menu variations based on the estimated user emotions. For example, if the user is feeling stressed, a simple and easy menu can be suggested. Also, if the user is relaxed, a complex and challenging menu can be suggested. Furthermore, if the user is tired, a menu with a short cooking time can be suggested. In this way, by adjusting the menu variations according to the user's emotions, more appropriate menus can be provided.

[0123] The display unit can monitor the user's device usage status and select the optimal display timing. For example, if the user frequently uses a smartphone, it can provide a display optimized for the smartphone. If the user uses a tablet, it can also provide a display tailored to a larger screen. Furthermore, if the user uses a desktop, it can also provide a display including detailed information. This allows the optimal display timing to be selected according to the device usage status, enabling a display with high visibility for the user.

[0124] The ingredient input unit can estimate the user's emotions and customize the ingredient input method based on the estimated user emotions. For example, if the user is stressed, voice input can be given priority. If the user is relaxed, text input can be given priority. Furthermore, if the user is busy, barcode scanning can be given priority. In this way, the ingredient input method can be customized according to the user's emotions, allowing for a more appropriate input method to be provided.

[0125] The determination unit can predict patterns of mood and physical condition fluctuations based on the user's past health data. For example, if the user has tended to get sick during a particular season in the past, it can suggest a menu that matches that season. Also, if the user has tended to feel stressed before a particular event in the past, it can suggest a relaxing menu that matches that event. Furthermore, it can analyze the user's past health data and suggest menus that support long-term health management. In this way, by utilizing past health data, it is possible to predict patterns of mood and physical condition fluctuations and provide more appropriate menus.

[0126] The generation unit can estimate the user's emotions and adjust the menu presentation method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive presentation can be provided. If the user is relaxed, a presentation including detailed explanations and visuals can be provided. Furthermore, if the user is in a hurry, a concise presentation that focuses on the main points can be provided. In this way, by adjusting the menu presentation method according to the user's emotions, a presentation with high visibility can be achieved.

[0127] The input unit can suggest regional ingredients and dishes based on the user's geographical location information. For example, if the user is in a specific region, ingredients that are easily available in that region can be suggested. If the user is traveling, dishes using local specialties of the destination can be suggested. Furthermore, if the user is overseas, traditional dishes of that country can be suggested. In this way, by utilizing geographical location information, regional ingredients and dishes can be suggested, providing the user with a new dining experience.

[0128] The display unit can estimate the user's emotions and customize the menu display content based on the estimated user emotions. For example, if the user is feeling stressed, relaxing colors and designs can be used. If the user is relaxed, detailed information and recipes can be displayed. Furthermore, if the user is in a hurry, a concise display that focuses on the main points can be provided. This allows the display content to be customized according to the user's emotions, resulting in a highly visible display.

[0129] The generation unit can optimize ingredient combinations based on the user's past eating history. For example, it can prioritize and suggest ingredient combinations that the user has previously preferred. It can also exclude ingredient combinations that the user has previously avoided. It can also analyze the user's past eating history and suggest new ingredient combinations. In this way, by utilizing the user's past eating history, it is possible to optimize ingredient combinations and provide the user with a new eating experience.

[0130] The determination unit can estimate the user's emotions and narrow down menu options based on the estimated user's emotions. For example, if the user is feeling stressed, menus that are relaxing can be displayed preferentially. Also, if the user is relaxed, menus with a balanced nutritional profile can be displayed preferentially. Furthermore, if the user is tired, menus with a short cooking time can be displayed preferentially. In this way, by narrowing down menu options according to the user's emotions, more appropriate menus can be provided.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The user inputs personal data into the input unit. The personal data includes age, gender, health status, and past dietary history. The input unit can input personal data by text input, voice input, image input, or other methods. Step 2: The determination unit determines the user's mood or physical condition based on the personal data input by the input unit. The determination unit determines the user's mood or physical condition based on information such as questionnaire results, vital signs, and self-reporting. The determination unit can also determine the user's mood or physical condition by analyzing the details of the user's diary and questionnaires. Step 3: The ingredient input unit allows the user to input ingredients at home using methods such as barcode scanning, voice input, and text input. Step 4: The generation unit generates a menu based on the information obtained by the judgment unit and the ingredient input unit. Using generation AI, the generation unit generates a menu that takes into account the user's mood, physical condition, and ingredients available at home. The generation unit generates a menu that takes nutritional balance into consideration, receives user feedback, learns from it, and can reflect this in future suggestions. Step 5: The display unit displays the menu generated by the generation unit. The display unit can display the menu using methods such as text display, graphical display, and audio guidance. For example, the suggested menu is displayed to the user through a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] 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.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] 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.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] 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.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting personal data; a determination unit that determines the mood or physical condition of the user based on the personal data input by the input unit; an ingredient input section for inputting ingredients available at home; a generation unit that generates a menu based on the information obtained by the determination unit and the ingredient input unit; A display unit that displays the menu generated by the generation unit. A system characterized by:

2. The generation unit Receive and learn from user feedback and incorporate it into future proposals 2. The system of claim 1.

3. The generation unit Generate a menu that takes nutritional balance into consideration 2. The system of claim 1.

4. The ingredient input unit Enter ingredients using barcode scanning or voice input 2. The system of claim 1.

5. The determination unit Determine mood and physical condition based on user diary and surveys 2. The system of claim 1.

6. The display unit Displaying suggested meals to the user 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the timing of personal data input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past personal data input history and select the input method 2. The system of claim 1.

9. The input unit When entering personal data, filtering is performed based on the user's current living situation and health status.

2. The system of claim 1.

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