system

Abstract

The system according to this embodiment aims to efficiently suggest menus and generate shopping lists by utilizing special offer information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a video generation unit, and a hearing unit. The collection unit collects special sale information. The analysis unit analyzes the special sale information collected by the collection unit and proposes a menu. The generation unit generates a shopping list based on the menu proposed by the analysis unit. The video generation unit introduces cooking methods in a short video based on the shopping list generated by the generation unit. The hearing unit conducts hearings on satisfaction after the meal and incorporates this into the next menu proposal.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to efficiently propose a menu by utilizing special sale information and generate a shopping list, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently propose a menu by utilizing special sale information and generate a shopping list.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a video generation unit, and a hearing unit. The collection unit collects special sale information. The analysis unit analyzes the special sale information collected by the collection unit and proposes a menu. The generation unit generates a shopping list based on the menu proposed by the analysis unit. The video generation unit introduces cooking methods in a short video based on the shopping list generated by the generation unit. The hearing unit conducts interviews about satisfaction after the meal and incorporates this into the next menu proposal. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently suggest menus and generate shopping lists by utilizing special offer information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An application service according to an embodiment of the present invention is a system in which a generating AI automatically reads special sale information from nearby supermarkets and suggests recommended menus and recipes based on that sale data. When the user confirms a menu, the generating AI automatically generates a shopping list of which supermarkets to buy which ingredients. Furthermore, it introduces cooking methods with short videos, and after the meal, it asks the user about their satisfaction with the meal and incorporates that feedback into the next menu suggestion. For example, the user selects the supermarket they will go to from a map. Next, the generating AI reads the special sale information data of the selected supermarket and suggests menus. When the user selects from the suggested menus, the generating AI automatically generates a shopping list. For example, based on the special sale information of supermarkets A and B, a list is created of which supermarkets to buy which ingredients. Next, the generating AI introduces cooking methods with short videos. The user can proceed with cooking while watching these videos. After the meal, it asks the user about their satisfaction with the meal and incorporates the results into the next menu suggestion. This allows the user to save the trouble of thinking about daily menus and reduce the effort of choosing inexpensive items at the supermarket. This means the app service will be aimed at people who plan their daily meals, and will be offered particularly to households that cook at home frequently. The revenue model will be a monthly subscription app costing 100 yen, with an assumed subscription rate of 1% of the target population. This is expected to generate approximately 600 million yen in annual revenue.

[0029] The application service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a video generation unit, and a hearing unit. The collection unit collects sale information. For example, the collection unit can automatically collect sale information from nearby supermarkets via the internet. The collection unit can also collect sale information from supermarkets selected by the user. For example, if a user selects a supermarket from the map within the app, the sale information for that supermarket is collected. The analysis unit analyzes the sale information collected by the collection unit and proposes a menu. For example, the analysis unit uses a generation AI to analyze the collected sale information and propose the most suitable menu for the user. The generation AI generates a menu based on the sale information, taking into account nutritional balance and the user's preferences. The generation unit generates a shopping list based on the menu proposed by the analysis unit. For example, the generation unit uses a generation AI to generate a shopping list of which supermarkets to buy which ingredients from based on the proposed menu. The generation AI generates the optimal shopping list based on the sale information. The video generation unit introduces cooking methods in a short video based on the shopping list generated by the generation unit. The video generation unit, for example, uses AI to generate short videos of cooking methods based on the generated shopping list. The short videos clearly explain the steps so that users can easily cook. The interviewing unit interviews users about their satisfaction after the meal and incorporates this into the next menu suggestion. The interviewing unit, for example, uses AI to interview users about their satisfaction after the meal and incorporates the results into the next menu suggestion. As a result, the app service according to this embodiment can consistently perform everything from menu suggestions based on special offer information to generating shopping lists, introducing cooking methods, and interviewing users about their satisfaction.

[0030] The data collection unit collects sale information. For example, the data collection unit can automatically collect sale information from nearby supermarkets from the internet. Specifically, it uses web scraping technology to extract data from the official websites of supermarkets and portal sites that list sale information. This allows for the acquisition of the latest sale information in real time. The data collection unit can also collect sale information from supermarkets selected by the user. For example, if a user selects a supermarket from the map in the app, sale information for that supermarket will be collected. In this case, the user's location information can be used to automatically list nearby supermarkets, making it easy for the user to select one. Furthermore, the data collection unit has a function that prioritizes displaying supermarkets that the user frequently uses, based on the user's past selection and purchase history. This allows users to collect sale information without effort and plan their shopping efficiently. The collected sale information is stored in a database and managed so that the analysis and generation units can access it. The data collection unit is required to update the data regularly and always provide the latest information. This allows the data collection unit to provide users with reliable sale information and improve the overall convenience of the app service.

[0031] The analysis unit analyzes the sale information collected by the collection unit and proposes menus. For example, the analysis unit uses a generation AI to analyze the collected sale information and propose the most suitable menu for the user. The generation AI generates menus based on the sale information, taking into account nutritional balance and the user's preferences. Specifically, the generation AI receives the collected sale information as input data and generates the optimal menu while referring to the user's past meal history and preference data. The generation AI uses natural language processing technology to analyze the text data of the sale information and extract information such as the type of ingredients, price, and nutritional value. Furthermore, the generation AI selects appropriate ingredients while considering the user's health condition and allergy information. For example, if the user is on a diet, it will propose a low-calorie, nutritionally balanced menu. The generation AI can also consider external factors such as season, weather, and events, and can propose menus appropriate for the season or for special occasions. This allows the analysis unit to provide personalized menu suggestions to users and improve their satisfaction with meals. In addition, the analysis unit displays the nutritional value and calorie information of the proposed menus to support users in maintaining a healthy diet. This allows the analytics unit to contribute to user health management and enhance the overall value of the app service.

[0032] The generation unit generates a shopping list based on the menu suggested by the analysis unit. For example, using a generation AI, the generation unit generates a shopping list specifying which supermarkets to buy which ingredients from based on the suggested menu. The generation AI generates the optimal shopping list based on sale information. Specifically, the generation AI analyzes the ingredient list of the suggested menu and calculates the required amount of each ingredient. Next, it refers to the collected sale information and identifies the supermarket where the ingredients can be purchased at the best price. The generation AI also considers the user's location information and past purchase history to suggest the most convenient shopping route for the user. For example, if it is necessary to visit multiple supermarkets, it calculates an efficient route to minimize travel time. The generation unit can also generate a shopping list tailored to the user's preferences if they have a preference for a particular brand or organic ingredients. The generated shopping list can be viewed within the app and can also be sent to the user via email or message. Furthermore, the generation unit has the function to update the contents of the shopping list in real time and modify the list according to changes in sale information and inventory status. In this way, the generation unit can always provide the user with the latest and most optimal shopping list, improving shopping efficiency and satisfaction.

[0033] The video generation unit introduces cooking methods in short videos based on the shopping list it generates. For example, the video generation unit uses AI to generate short videos of cooking methods based on the generated shopping list. Specifically, the AI ​​analyzes the cooking procedure for the suggested menu and generates videos that clearly explain each step. The AI ​​uses natural language processing technology to analyze the text data of the cooking procedure and combines appropriate images and audio to create the video. For example, it explains cooking points in detail, such as how to cut vegetables, the amount of seasonings, and the heat level. Furthermore, the video generation unit can prioritize introducing easy recipes for beginners and recipes that can be made in a short time, taking into account the user's cooking skills and time constraints. The generated short videos can be viewed within the app and also shared via social media and messaging apps. In addition, the video generation unit can improve the video content based on user feedback, providing clearer and more practical videos. For example, if a user leaves a question or comment about a specific cooking procedure, that feedback will be incorporated to improve the next video. This allows the video generation unit to provide users with high-quality cooking videos, improving the enjoyment and convenience of cooking.

[0034] The feedback department gathers feedback on customer satisfaction after meals and incorporates it into future menu suggestions. For example, the feedback department uses AI to gather feedback on post-meal satisfaction and reflects the results in future menu suggestions. Specifically, the AI ​​automatically sends users a post-meal questionnaire asking about their satisfaction level and areas for improvement. The questionnaire consists of text input and multiple-choice questions, designed for easy user response. The AI ​​analyzes the collected questionnaire data to understand user preferences and satisfaction trends. For example, if a particular ingredient or cooking method receives high ratings, this is reflected in future menu suggestions. Also, if a user dislikes a specific ingredient, the menu is adjusted to take this information into account. Furthermore, the feedback department can also collect feedback tailored to the user's health status and meal goals (e.g., weight loss, muscle building) and incorporate this into future menu suggestions. This allows the feedback department to provide personalized menu suggestions to users and continuously improve their meal satisfaction. Additionally, the feedback department can store the collected feedback data in a database for long-term trend analysis and identification of areas for improvement. This allows the hearing department to respond flexibly to user needs, improving the overall quality of the app service and user satisfaction.

[0035] The data collection unit can collect sale information for supermarkets selected by the user. For example, when a user selects a supermarket from the app's map, the data collection unit automatically collects sale information for that supermarket from the internet. By collecting sale information for supermarkets selected by the user, the data collection unit can provide information that meets the user's needs. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the sale information of the supermarket selected by the user into the AI ​​and have the AI ​​perform the collection of sale information.

[0036] The analysis unit can analyze the collected sale information and propose the most suitable menu to the user. For example, the analysis unit uses a generation AI to analyze the collected sale information and propose the most suitable menu to the user. The generation AI generates menus based on the sale information, taking into account nutritional balance and the user's preferences. By analyzing the collected sale information, the analysis unit can propose the most suitable menu to the user. Some or all of the above processing in the analysis unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the analysis unit can input the collected sale information into the generation AI and have the generation AI execute menu proposals.

[0037] The generation unit can generate a shopping list of which supermarkets to buy which ingredients from based on the proposed menu. For example, the generation unit uses a generation AI to generate a shopping list of which supermarkets to buy which ingredients from based on the proposed menu. The generation AI generates an optimal shopping list based on sale information. By generating a shopping list based on the proposed menu, the generation unit enables efficient shopping. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the proposed menu into the generation AI and have the generation AI generate the shopping list.

[0038] The video generation unit can introduce cooking methods in short videos based on the generated shopping list. The video generation unit, for example, uses AI to generate short videos of cooking methods based on the generated shopping list. The short videos clearly explain the steps so that users can easily cook. By introducing cooking methods in short videos based on the generated shopping list, the video generation unit enables users to easily cook. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input the generated shopping list into AI and have the AI ​​generate short videos of cooking methods.

[0039] The interviewing department can gather information on customer satisfaction after a meal and incorporate the results into future menu suggestions. For example, the interviewing department can use AI to gather information on customer satisfaction after a meal and incorporate the results into future menu suggestions. The interviewing department can gather information on customer satisfaction after a meal and incorporate it into future menu suggestions. Some or all of the above-described processes in the interviewing department may be performed using AI or not. For example, the interviewing department can input customer satisfaction after a meal into the AI ​​and have the AI ​​incorporate it into future menu suggestions.

[0040] The data collection unit can analyze the user's past purchase history and select the optimal method for collecting sale information. For example, the data collection unit may prioritize collecting products that the user has frequently purchased in the past. The data collection unit may collect sale information from the user's purchase history for specific days of the week and time slots. The data collection unit analyzes the user's purchasing patterns and proposes the optimal method for collecting sale information. This allows the optimal method for collecting sale information to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into AI and have the AI ​​select the method for collecting sale information.

[0041] The collection unit can filter sale information based on the user's current food inventory. For example, the collection unit can obtain the user's refrigerator inventory information and collect only the necessary ingredients. The collection unit can collect non-duplicate sale information, taking into account the ingredients the user already owns. The collection unit can then suggest the most suitable sale information based on the user's inventory status. This allows for efficient shopping by filtering sale information based on the user's current food inventory status. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's inventory information into the AI ​​and have the AI ​​perform the filtering of sale information.

[0042] The data collection unit can prioritize collecting highly relevant sale information by considering the user's geographical location when gathering sale information. For example, the data collection unit prioritizes collecting sale information from supermarkets closest to the user's current location. The data collection unit collects sale information from supermarkets along the user's commute route. The data collection unit prioritizes collecting sale information from supermarkets near the user's home. This allows for the priority collection of highly relevant sale information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the sale information collection.

[0043] The data collection unit can collect relevant data by analyzing the user's social media activity when collecting sale information. For example, the data collection unit collects sale information related to products mentioned by the user on social media. The data collection unit collects sale information shared by the user's followers. The data collection unit collects sale information related to posts that the user has "liked". In this way, relevant sale information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​perform the collection of sale information.

[0044] The analysis unit can adjust the level of detail in menu suggestions based on the importance of the special offer information during analysis. For example, the analysis unit can provide detailed menu suggestions based on highly important special offer information. The analysis unit can provide concise menu suggestions based on less important special offer information. The analysis unit can provide balanced menu suggestions based on moderately important special offer information. By adjusting the level of detail in menu suggestions based on the importance of the special offer information, it becomes possible to provide the optimal suggestion for the user. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the special offer information into the generation AI and have the generation AI perform the adjustment of the level of detail in menu suggestions.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the special offer information during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes freshness to special offer information on fresh foods. For special offer information on processed foods, the analysis unit applies an analysis algorithm that emphasizes shelf life. For special offer information on beverages, the analysis unit applies an analysis algorithm that emphasizes seasonality. By applying different analysis algorithms depending on the category of the special offer information, it becomes possible to make more accurate menu suggestions. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category of the special offer information into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of menu suggestions based on the timing of sale information collection during analysis. For example, the analysis unit prioritizes menu suggestions based on the latest sale information. The analysis unit sets a lower priority based on older sale information. The analysis unit provides balanced menu suggestions based on moderate sale information. By determining the priority of menu suggestions based on the timing of sale information collection, it becomes possible to provide the optimal suggestions for the user. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the timing of sale information collection into the generation AI and have the generation AI perform the determination of the menu suggestion priority.

[0047] The analysis unit can adjust the order of menu suggestions based on the relevance of the special offer information during analysis. For example, the analysis unit prioritizes menu suggestions based on highly relevant special offer information. The analysis unit sets a lower priority based on less relevant special offer information. The analysis unit provides balanced menu suggestions based on moderately relevant special offer information. By adjusting the order of menu suggestions based on the relevance of the special offer information, it becomes possible to provide the optimal suggestion for the user. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the relevance of the special offer information into a generation AI and have the generation AI perform the adjustment of the order of menu suggestions.

[0048] The generation unit can analyze the user's past purchasing behavior during generation to select the optimal shopping list generation method. For example, the generation unit may prioritize including items that the user has frequently purchased in the past in the list. The generation unit may also include items that the user purchases on specific days of the week or at specific times, based on the user's purchase history. The generation unit analyzes the user's purchasing patterns and generates the optimal shopping list. In this way, the optimal shopping list can be generated by analyzing the user's past purchasing behavior. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past purchasing behavior into a generation AI and have the generation AI select the shopping list generation method.

[0049] The generation unit can customize the contents of the shopping list based on the user's current lifestyle during generation. For example, if the user is busy, the generation unit will include ingredients that are easy to cook in the list. If the user prioritizes health, the generation unit will include nutritionally balanced ingredients in the list. If the user is avoiding certain ingredients, the generation unit will exclude those ingredients from the list. In this way, by customizing the shopping list based on the user's current lifestyle, the generation unit can provide the user with the most suitable list. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current lifestyle into a generation AI and have the generation AI perform the customization of the shopping list.

[0050] The generation unit can generate an optimal shopping list by considering the user's geographical location information during the generation process. For example, the generation unit can generate a shopping list based on sale information from the supermarket closest to the user's current location. The generation unit can generate a shopping list based on sale information from supermarkets along the user's commute route. The generation unit can generate a shopping list based on sale information from supermarkets near the user's home. In this way, an optimal shopping list can be generated by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of the shopping list.

[0051] The generation unit can analyze the user's social media activity during generation to suggest the contents of a shopping list. For example, the generation unit can include products mentioned by the user on social media in the list. The generation unit can include products shared by the user's followers in the list. The generation unit can include products related to posts that the user has "liked" in the list. In this way, by analyzing the user's social media activity, it can suggest the optimal shopping list. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI suggest the contents of a shopping list.

[0052] The video generation unit can refer to the user's past cooking history when generating a video to introduce the optimal cooking method. For example, the video generation unit can introduce similar procedures based on cooking methods the user has successfully used in the past. The video generation unit can introduce new procedures while avoiding cooking methods the user has failed at in the past. The video generation unit can suggest the optimal cooking method from the user's past cooking history. In this way, the optimal cooking method can be introduced by referring to the user's past cooking history. Some or all of the above processes in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's past cooking history into AI and have the AI ​​perform the introduction of cooking methods.

[0053] The video generation unit can adjust the difficulty level of the cooking method based on the user's current cooking skill level when generating a video. For example, if the user is a beginner, the video generation unit will introduce an easy cooking method. If the user is an intermediate cook, the video generation unit will introduce a slightly more difficult cooking method. If the user is an advanced cook, the video generation unit will introduce a challenging cooking method. In this way, by adjusting the difficulty level of the cooking method based on the user's current cooking skill level, the system can provide the user with the most suitable cooking method. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's cooking skill data into the AI ​​and have the AI ​​perform the adjustment of the cooking method difficulty level.

[0054] The video generation unit can select the optimal video format when generating a video, taking into account the user's device information. For example, if the user is using a smartphone, the video generation unit will provide a portrait video format. If the user is using a tablet, the video generation unit will provide a video format optimized for a large screen. If the user is using a personal computer, the video generation unit will provide a landscape video format. In this way, the optimal video format can be provided by taking into account the user's device information. Some or all of the above processing in the video generation unit may be performed using AI, or it may be performed without using AI. For example, the video generation unit can input the user's device information into the AI ​​and have the AI ​​perform the selection of the video format.

[0055] The video generation unit can analyze the user's social media activity and introduce relevant cooking methods when generating videos. For example, the video generation unit can introduce cooking methods related to dishes mentioned by the user on social media. The video generation unit can introduce cooking methods related to dishes shared by the user's followers. The video generation unit can introduce cooking methods related to posts that the user has "liked". In this way, relevant cooking methods can be introduced by analyzing the user's social media activity. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's social media data into AI and have the AI ​​perform the introduction of cooking methods.

[0056] The interviewing unit can select the optimal interviewing method by referring to the user's past satisfaction data during the interview. For example, the interviewing unit asks questions related to menus that the user has previously given high ratings to. The interviewing unit avoids asking questions about menus that the user has previously given low ratings to. Based on the user's past satisfaction data, the interviewing unit proposes the optimal interviewing method. In this way, the optimal interviewing method can be selected by referring to the user's past satisfaction data. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's past satisfaction data into AI and have the AI ​​perform the selection of the interviewing method.

[0057] The interviewing unit can customize the content of the satisfaction interview based on the user's current lifestyle. For example, if the user is busy, the interviewing unit will ask concise questions. If the user is relaxed, the interviewing unit will ask detailed questions. If the user is avoiding a particular food, the interviewing unit will avoid questions about that food. By customizing the content of the satisfaction interview based on the user's current lifestyle, it becomes possible to conduct an optimal interview for the user. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's current lifestyle into the AI ​​and have the AI ​​customize the content of the satisfaction interview.

[0058] The interviewing unit can select the optimal interviewing method by considering the user's geographical location during the interview. For example, if the user is at home, the interviewing unit conducts the interview in a relaxed environment. If the user is out, the interviewing unit conducts a concise and quick interview. If the user is in a specific location, the interviewing unit asks questions related to that location. In this way, the optimal interviewing method can be selected by considering the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the selection of the interviewing method.

[0059] The interviewing unit can analyze the user's social media activity during the interview and propose content for the satisfaction interview. For example, the interviewing unit can ask questions related to menu items mentioned by the user on social media. The interviewing unit can ask questions related to menu items shared by the user's followers. The interviewing unit can ask questions related to posts that the user has "liked". By analyzing the user's social media activity in this way, the interviewing unit can propose the most suitable content for the satisfaction interview. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's social media data into AI and have the AI ​​propose content for the satisfaction interview.

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

[0061] The data collection unit can analyze the user's past purchase history and select the optimal method for collecting sale information. For example, the data collection unit may prioritize collecting products that the user has frequently purchased in the past. The data collection unit may collect sale information from the user's purchase history for specific days of the week and time slots. The data collection unit analyzes the user's purchasing patterns and proposes the optimal method for collecting sale information. This allows the optimal method for collecting sale information to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into AI and have the AI ​​select the method for collecting sale information.

[0062] The analysis unit can apply different analysis algorithms depending on the category of the special offer information during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes freshness to special offer information on fresh foods. For special offer information on processed foods, the analysis unit applies an analysis algorithm that emphasizes shelf life. For special offer information on beverages, the analysis unit applies an analysis algorithm that emphasizes seasonality. By applying different analysis algorithms depending on the category of the special offer information, it becomes possible to make more accurate menu suggestions. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category of the special offer information into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0063] The generation unit can analyze the user's past purchasing behavior during generation to select the optimal shopping list generation method. For example, the generation unit may prioritize including items that the user has frequently purchased in the past in the list. The generation unit may also include items that the user purchases on specific days of the week or at specific times, based on the user's purchase history. The generation unit analyzes the user's purchasing patterns and generates the optimal shopping list. In this way, the optimal shopping list can be generated by analyzing the user's past purchasing behavior. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past purchasing behavior into a generation AI and have the generation AI select the shopping list generation method.

[0064] The video generation unit can refer to the user's past cooking history when generating a video to introduce the optimal cooking method. For example, the video generation unit can introduce similar procedures based on cooking methods the user has successfully used in the past. The video generation unit can introduce new procedures while avoiding cooking methods the user has failed at in the past. The video generation unit can suggest the optimal cooking method from the user's past cooking history. In this way, the optimal cooking method can be introduced by referring to the user's past cooking history. Some or all of the above processes in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's past cooking history into AI and have the AI ​​perform the introduction of cooking methods.

[0065] The interviewing unit can select the optimal interviewing method by referring to the user's past satisfaction data during the interview. For example, the interviewing unit asks questions related to menus that the user has previously given high ratings to. The interviewing unit avoids asking questions about menus that the user has previously given low ratings to. Based on the user's past satisfaction data, the interviewing unit proposes the optimal interviewing method. In this way, the optimal interviewing method can be selected by referring to the user's past satisfaction data. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's past satisfaction data into AI and have the AI ​​perform the selection of the interviewing method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects sale information. For example, it can automatically collect sale information from nearby supermarkets via the internet. It can also collect sale information from supermarkets selected by the user. When a user selects a supermarket from the in-app map, the sale information for that supermarket is collected. Step 2: The analysis unit analyzes the special offer information collected by the collection unit and proposes menus. For example, it uses a generation AI to analyze the collected special offer information and propose the most suitable menu for the user. Based on the special offer information, the generation AI generates menus considering nutritional balance and the user's preferences. Step 3: The generation unit generates a shopping list based on the menu suggested by the analysis unit. For example, using the generation AI, it generates a shopping list of which supermarkets to buy which ingredients to purchase based on the suggested menu. The generation AI generates the optimal shopping list based on sale information. Step 4: The video generation unit creates short videos demonstrating cooking methods based on the shopping list generated by the generation unit. For example, AI is used to generate short videos demonstrating cooking methods based on the generated shopping list. The short videos clearly explain the steps so that users can easily perform the cooking. Step 5: The interviewing department gathers feedback on customer satisfaction after the meal and incorporates it into future menu suggestions. For example, AI can be used to gather feedback on customer satisfaction after the meal and incorporate the results into future menu suggestions.

[0068] (Example of form 2) An application service according to an embodiment of the present invention is a system in which a generating AI automatically reads special sale information from nearby supermarkets and suggests recommended menus and recipes based on that sale data. When the user confirms a menu, the generating AI automatically generates a shopping list of which supermarkets to buy which ingredients. Furthermore, it introduces cooking methods with short videos, and after the meal, it asks the user about their satisfaction with the meal and incorporates that feedback into the next menu suggestion. For example, the user selects the supermarket they will go to from a map. Next, the generating AI reads the special sale information data of the selected supermarket and suggests menus. When the user selects from the suggested menus, the generating AI automatically generates a shopping list. For example, based on the special sale information of supermarkets A and B, a list is created of which supermarkets to buy which ingredients. Next, the generating AI introduces cooking methods with short videos. The user can proceed with cooking while watching these videos. After the meal, it asks the user about their satisfaction with the meal and incorporates the results into the next menu suggestion. This allows the user to save the trouble of thinking about daily menus and reduce the effort of choosing inexpensive items at the supermarket. This means the app service will be aimed at people who plan their daily meals, and will be offered particularly to households that cook at home frequently. The revenue model will be a monthly subscription app costing 100 yen, with an assumed subscription rate of 1% of the target population. This is expected to generate approximately 600 million yen in annual revenue.

[0069] The application service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a video generation unit, and a hearing unit. The collection unit collects sale information. For example, the collection unit can automatically collect sale information from nearby supermarkets via the internet. The collection unit can also collect sale information from supermarkets selected by the user. For example, if a user selects a supermarket from the map within the app, the sale information for that supermarket is collected. The analysis unit analyzes the sale information collected by the collection unit and proposes a menu. For example, the analysis unit uses a generation AI to analyze the collected sale information and propose the most suitable menu for the user. The generation AI generates a menu based on the sale information, taking into account nutritional balance and the user's preferences. The generation unit generates a shopping list based on the menu proposed by the analysis unit. For example, the generation unit uses a generation AI to generate a shopping list of which supermarkets to buy which ingredients from based on the proposed menu. The generation AI generates the optimal shopping list based on the sale information. The video generation unit introduces cooking methods in a short video based on the shopping list generated by the generation unit. The video generation unit, for example, uses AI to generate short videos of cooking methods based on the generated shopping list. The short videos clearly explain the steps so that users can easily cook. The interviewing unit interviews users about their satisfaction after the meal and incorporates this into the next menu suggestion. The interviewing unit, for example, uses AI to interview users about their satisfaction after the meal and incorporates the results into the next menu suggestion. As a result, the app service according to this embodiment can consistently perform everything from menu suggestions based on special offer information to generating shopping lists, introducing cooking methods, and interviewing users about their satisfaction.

[0070] The data collection unit collects sale information. For example, the data collection unit can automatically collect sale information from nearby supermarkets from the internet. Specifically, it uses web scraping technology to extract data from the official websites of supermarkets and portal sites that list sale information. This allows for the acquisition of the latest sale information in real time. The data collection unit can also collect sale information from supermarkets selected by the user. For example, if a user selects a supermarket from the map in the app, sale information for that supermarket will be collected. In this case, the user's location information can be used to automatically list nearby supermarkets, making it easy for the user to select one. Furthermore, the data collection unit has a function that prioritizes displaying supermarkets that the user frequently uses, based on the user's past selection and purchase history. This allows users to collect sale information without effort and plan their shopping efficiently. The collected sale information is stored in a database and managed so that the analysis and generation units can access it. The data collection unit is required to update the data regularly and always provide the latest information. This allows the data collection unit to provide users with reliable sale information and improve the overall convenience of the app service.

[0071] The analysis unit analyzes the sale information collected by the collection unit and proposes menus. For example, the analysis unit uses a generation AI to analyze the collected sale information and propose the most suitable menu for the user. The generation AI generates menus based on the sale information, taking into account nutritional balance and the user's preferences. Specifically, the generation AI receives the collected sale information as input data and generates the optimal menu while referring to the user's past meal history and preference data. The generation AI uses natural language processing technology to analyze the text data of the sale information and extract information such as the type of ingredients, price, and nutritional value. Furthermore, the generation AI selects appropriate ingredients while considering the user's health condition and allergy information. For example, if the user is on a diet, it will propose a low-calorie, nutritionally balanced menu. The generation AI can also consider external factors such as season, weather, and events, and can propose menus appropriate for the season or for special occasions. This allows the analysis unit to provide personalized menu suggestions to users and improve their satisfaction with meals. In addition, the analysis unit displays the nutritional value and calorie information of the proposed menus to support users in maintaining a healthy diet. This allows the analytics unit to contribute to user health management and enhance the overall value of the app service.

[0072] The generation unit generates a shopping list based on the menu suggested by the analysis unit. For example, using a generation AI, the generation unit generates a shopping list specifying which supermarkets to buy which ingredients from based on the suggested menu. The generation AI generates the optimal shopping list based on sale information. Specifically, the generation AI analyzes the ingredient list of the suggested menu and calculates the required amount of each ingredient. Next, it refers to the collected sale information and identifies the supermarket where the ingredients can be purchased at the best price. The generation AI also considers the user's location information and past purchase history to suggest the most convenient shopping route for the user. For example, if it is necessary to visit multiple supermarkets, it calculates an efficient route to minimize travel time. The generation unit can also generate a shopping list tailored to the user's preferences if they have a preference for a particular brand or organic ingredients. The generated shopping list can be viewed within the app and can also be sent to the user via email or message. Furthermore, the generation unit has the function to update the contents of the shopping list in real time and modify the list according to changes in sale information and inventory status. In this way, the generation unit can always provide the user with the latest and most optimal shopping list, improving shopping efficiency and satisfaction.

[0073] The video generation unit introduces cooking methods in short videos based on the shopping list it generates. For example, the video generation unit uses AI to generate short videos of cooking methods based on the generated shopping list. Specifically, the AI ​​analyzes the cooking procedure for the suggested menu and generates videos that clearly explain each step. The AI ​​uses natural language processing technology to analyze the text data of the cooking procedure and combines appropriate images and audio to create the video. For example, it explains cooking points in detail, such as how to cut vegetables, the amount of seasonings, and the heat level. Furthermore, the video generation unit can prioritize introducing easy recipes for beginners and recipes that can be made in a short time, taking into account the user's cooking skills and time constraints. The generated short videos can be viewed within the app and also shared via social media and messaging apps. In addition, the video generation unit can improve the video content based on user feedback, providing clearer and more practical videos. For example, if a user leaves a question or comment about a specific cooking procedure, that feedback will be incorporated to improve the next video. This allows the video generation unit to provide users with high-quality cooking videos, improving the enjoyment and convenience of cooking.

[0074] The feedback department gathers feedback on customer satisfaction after meals and incorporates it into future menu suggestions. For example, the feedback department uses AI to gather feedback on post-meal satisfaction and reflects the results in future menu suggestions. Specifically, the AI ​​automatically sends users a post-meal questionnaire asking about their satisfaction level and areas for improvement. The questionnaire consists of text input and multiple-choice questions, designed for easy user response. The AI ​​analyzes the collected questionnaire data to understand user preferences and satisfaction trends. For example, if a particular ingredient or cooking method receives high ratings, this is reflected in future menu suggestions. Also, if a user dislikes a specific ingredient, the menu is adjusted to take this information into account. Furthermore, the feedback department can also collect feedback tailored to the user's health status and meal goals (e.g., weight loss, muscle building) and incorporate this into future menu suggestions. This allows the feedback department to provide personalized menu suggestions to users and continuously improve their meal satisfaction. Additionally, the feedback department can store the collected feedback data in a database for long-term trend analysis and identification of areas for improvement. This allows the hearing department to respond flexibly to user needs, improving the overall quality of the app service and user satisfaction.

[0075] The data collection unit can collect sale information for supermarkets selected by the user. For example, when a user selects a supermarket from the app's map, the data collection unit automatically collects sale information for that supermarket from the internet. By collecting sale information for supermarkets selected by the user, the data collection unit can provide information that meets the user's needs. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the sale information of the supermarket selected by the user into the AI ​​and have the AI ​​perform the collection of sale information.

[0076] The analysis unit can analyze the collected sale information and propose the most suitable menu to the user. For example, the analysis unit uses a generation AI to analyze the collected sale information and propose the most suitable menu to the user. The generation AI generates menus based on the sale information, taking into account nutritional balance and the user's preferences. By analyzing the collected sale information, the analysis unit can propose the most suitable menu to the user. Some or all of the above processing in the analysis unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the analysis unit can input the collected sale information into the generation AI and have the generation AI execute menu proposals.

[0077] The generation unit can generate a shopping list of which supermarkets to buy which ingredients from based on the proposed menu. For example, the generation unit uses a generation AI to generate a shopping list of which supermarkets to buy which ingredients from based on the proposed menu. The generation AI generates an optimal shopping list based on sale information. By generating a shopping list based on the proposed menu, the generation unit enables efficient shopping. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the proposed menu into the generation AI and have the generation AI generate the shopping list.

[0078] The video generation unit can introduce cooking methods in short videos based on the generated shopping list. The video generation unit, for example, uses AI to generate short videos of cooking methods based on the generated shopping list. The short videos clearly explain the steps so that users can easily cook. By introducing cooking methods in short videos based on the generated shopping list, the video generation unit enables users to easily cook. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input the generated shopping list into AI and have the AI ​​generate short videos of cooking methods.

[0079] The interviewing department can gather information on customer satisfaction after a meal and incorporate the results into future menu suggestions. For example, the interviewing department can use AI to gather information on customer satisfaction after a meal and incorporate the results into future menu suggestions. The interviewing department can gather information on customer satisfaction after a meal and incorporate it into future menu suggestions. Some or all of the above-described processes in the interviewing department may be performed using AI or not. For example, the interviewing department can input customer satisfaction after a meal into the AI ​​and have the AI ​​incorporate it into future menu suggestions.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of collecting sale information based on the estimated emotions. For example, if the user is stressed, the data collection unit will delay collecting sale information and begin collecting it when the user is relaxed. If the user is excited, the data collection unit will collect sale information quickly and begin making suggestions immediately. If the user is tired, the data collection unit will postpone collecting sale information until the next day, allowing the user to rest. By adjusting the timing of sale information collection based on the user's emotions, information can be provided at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of sale information collection.

[0081] The data collection unit can analyze the user's past purchase history and select the optimal method for collecting sale information. For example, the data collection unit may prioritize collecting products that the user has frequently purchased in the past. The data collection unit may collect sale information from the user's purchase history for specific days of the week and time slots. The data collection unit analyzes the user's purchasing patterns and proposes the optimal method for collecting sale information. This allows the optimal method for collecting sale information to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into AI and have the AI ​​select the method for collecting sale information.

[0082] The collection unit can filter sale information based on the user's current food inventory. For example, the collection unit can obtain the user's refrigerator inventory information and collect only the necessary ingredients. The collection unit can collect non-duplicate sale information, taking into account the ingredients the user already owns. The collection unit can then suggest the most suitable sale information based on the user's inventory status. This allows for efficient shopping by filtering sale information based on the user's current food inventory status. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's inventory information into the AI ​​and have the AI ​​perform the filtering of sale information.

[0083] The data collection unit can estimate the user's emotions and determine the priority of sale information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will set a low priority for sale information and collect it slowly. If the user is in a hurry, the data collection unit will set a high priority for sale information and collect it quickly. If the user is excited, the data collection unit will set a medium priority for sale information and collect it in a balanced manner. In this way, by determining the priority of sale information based on the user's emotions, the data collection unit can provide the user with the most suitable information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the determination of the priority of sale information.

[0084] The data collection unit can prioritize collecting highly relevant sale information by considering the user's geographical location when gathering sale information. For example, the data collection unit prioritizes collecting sale information from supermarkets closest to the user's current location. The data collection unit collects sale information from supermarkets along the user's commute route. The data collection unit prioritizes collecting sale information from supermarkets near the user's home. This allows for the priority collection of highly relevant sale information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the sale information collection.

[0085] The data collection unit can collect relevant data by analyzing the user's social media activity when collecting sale information. For example, the data collection unit collects sale information related to products mentioned by the user on social media. The data collection unit collects sale information shared by the user's followers. The data collection unit collects sale information related to posts that the user has "liked". In this way, relevant sale information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​perform the collection of sale information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of menu suggestions based on the estimated emotions. For example, if the user is relaxed, the analysis unit will provide menu suggestions with detailed explanations. If the user is in a hurry, the analysis unit will provide concise and to-the-point menu suggestions. If the user is excited, the analysis unit will provide visually appealing menu suggestions. By adjusting the presentation of menu suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of menu suggestions.

[0087] The analysis unit can adjust the level of detail in menu suggestions based on the importance of the special offer information during analysis. For example, the analysis unit can provide detailed menu suggestions based on highly important special offer information. The analysis unit can provide concise menu suggestions based on less important special offer information. The analysis unit can provide balanced menu suggestions based on moderately important special offer information. By adjusting the level of detail in menu suggestions based on the importance of the special offer information, it becomes possible to provide the optimal suggestion for the user. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the special offer information into the generation AI and have the generation AI perform the adjustment of the level of detail in menu suggestions.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the special offer information during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes freshness to special offer information on fresh foods. For special offer information on processed foods, the analysis unit applies an analysis algorithm that emphasizes shelf life. For special offer information on beverages, the analysis unit applies an analysis algorithm that emphasizes seasonality. By applying different analysis algorithms depending on the category of the special offer information, it becomes possible to make more accurate menu suggestions. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category of the special offer information into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of menu suggestions based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide short, concise menu suggestions. If the user is relaxed, the analysis unit will provide longer menu suggestions with detailed explanations. If the user is excited, the analysis unit will provide visually stimulating menu suggestions. By adjusting the length of menu suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of menu suggestions.

[0090] The analysis unit can determine the priority of menu suggestions based on the timing of sale information collection during analysis. For example, the analysis unit prioritizes menu suggestions based on the latest sale information. The analysis unit sets a lower priority based on older sale information. The analysis unit provides balanced menu suggestions based on moderate sale information. By determining the priority of menu suggestions based on the timing of sale information collection, it becomes possible to provide the optimal suggestions for the user. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the timing of sale information collection into the generation AI and have the generation AI perform the determination of the menu suggestion priority.

[0091] The analysis unit can adjust the order of menu suggestions based on the relevance of the special offer information during analysis. For example, the analysis unit prioritizes menu suggestions based on highly relevant special offer information. The analysis unit sets a lower priority based on less relevant special offer information. The analysis unit provides balanced menu suggestions based on moderately relevant special offer information. By adjusting the order of menu suggestions based on the relevance of the special offer information, it becomes possible to provide the optimal suggestion for the user. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the relevance of the special offer information into a generation AI and have the generation AI perform the adjustment of the order of menu suggestions.

[0092] The generation unit can estimate the user's emotions and adjust the shopping list generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a detailed shopping list. If the user is in a hurry, the generation unit generates a concise and to-the-point shopping list. If the user is excited, the generation unit generates a visually appealing shopping list. In this way, by adjusting the shopping list generation method based on the user's emotions, the system can provide the user with the most suitable list. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the shopping list generation method.

[0093] The generation unit can analyze the user's past purchasing behavior during generation to select the optimal shopping list generation method. For example, the generation unit may prioritize including items that the user has frequently purchased in the past in the list. The generation unit may also include items that the user purchases on specific days of the week or at specific times, based on the user's purchase history. The generation unit analyzes the user's purchasing patterns and generates the optimal shopping list. In this way, the optimal shopping list can be generated by analyzing the user's past purchasing behavior. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past purchasing behavior into a generation AI and have the generation AI select the shopping list generation method.

[0094] The generation unit can customize the contents of the shopping list based on the user's current lifestyle during generation. For example, if the user is busy, the generation unit will include ingredients that are easy to cook in the list. If the user prioritizes health, the generation unit will include nutritionally balanced ingredients in the list. If the user is avoiding certain ingredients, the generation unit will exclude those ingredients from the list. In this way, by customizing the shopping list based on the user's current lifestyle, the generation unit can provide the user with the most suitable list. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current lifestyle into a generation AI and have the generation AI perform the customization of the shopping list.

[0095] The generation unit can estimate the user's emotions and determine the priority of the shopping list based on the estimated emotions. For example, if the user is relaxed, the generation unit will set a low priority and proceed with shopping slowly. If the user is in a hurry, the generation unit will set a high priority and proceed with shopping quickly. If the user is excited, the generation unit will set a medium priority and proceed with shopping in a balanced manner. In this way, by determining the priority of the shopping list based on the user's emotions, the system can provide the user with the optimal list. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of the shopping list.

[0096] The generation unit can generate an optimal shopping list by considering the user's geographical location information during the generation process. For example, the generation unit can generate a shopping list based on sale information from the supermarket closest to the user's current location. The generation unit can generate a shopping list based on sale information from supermarkets along the user's commute route. The generation unit can generate a shopping list based on sale information from supermarkets near the user's home. In this way, an optimal shopping list can be generated by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of the shopping list.

[0097] The generation unit can analyze the user's social media activity during generation to suggest the contents of a shopping list. For example, the generation unit can include products mentioned by the user on social media in the list. The generation unit can include products shared by the user's followers in the list. The generation unit can include products related to posts that the user has "liked" in the list. In this way, by analyzing the user's social media activity, it can suggest the optimal shopping list. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI suggest the contents of a shopping list.

[0098] The video generation unit can estimate the user's emotions and adjust the cooking method presentation based on the estimated emotions. For example, if the user is relaxed, the video generation unit provides a video that progresses at a leisurely pace. If the user is in a hurry, the video generation unit provides a short, concise video. If the user is excited, the video generation unit provides a video with visually stimulating effects. In this way, by adjusting the cooking method presentation based on the user's emotions, the optimal cooking method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the cooking method presentation.

[0099] The video generation unit can refer to the user's past cooking history when generating a video to introduce the optimal cooking method. For example, the video generation unit can introduce similar procedures based on cooking methods the user has successfully used in the past. The video generation unit can introduce new procedures while avoiding cooking methods the user has failed at in the past. The video generation unit can suggest the optimal cooking method from the user's past cooking history. In this way, the optimal cooking method can be introduced by referring to the user's past cooking history. Some or all of the above processes in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's past cooking history into AI and have the AI ​​perform the introduction of cooking methods.

[0100] The video generation unit can adjust the difficulty level of the cooking method based on the user's current cooking skill level when generating a video. For example, if the user is a beginner, the video generation unit will introduce an easy cooking method. If the user is an intermediate cook, the video generation unit will introduce a slightly more difficult cooking method. If the user is an advanced cook, the video generation unit will introduce a challenging cooking method. In this way, by adjusting the difficulty level of the cooking method based on the user's current cooking skill level, the system can provide the user with the most suitable cooking method. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's cooking skill data into the AI ​​and have the AI ​​perform the adjustment of the cooking method difficulty level.

[0101] The video generation unit can estimate the user's emotions and adjust the order in which the cooking method is presented based on the estimated emotions. For example, if the user is relaxed, the video generation unit will proceed without changing the order. If the user is in a hurry, the video generation unit will present the important steps first. If the user is excited, the video generation unit will present the visually appealing steps first. In this way, by adjusting the order in which the cooking method is presented based on the user's emotions, the optimal cooking method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into an AI and have the AI ​​adjust the order in which the cooking method is presented.

[0102] The video generation unit can select the optimal video format when generating a video, taking into account the user's device information. For example, if the user is using a smartphone, the video generation unit will provide a portrait video format. If the user is using a tablet, the video generation unit will provide a video format optimized for a large screen. If the user is using a personal computer, the video generation unit will provide a landscape video format. In this way, the optimal video format can be provided by taking into account the user's device information. Some or all of the above processing in the video generation unit may be performed using AI, or it may be performed without using AI. For example, the video generation unit can input the user's device information into the AI ​​and have the AI ​​perform the selection of the video format.

[0103] The video generation unit can analyze the user's social media activity and introduce relevant cooking methods when generating videos. For example, the video generation unit can introduce cooking methods related to dishes mentioned by the user on social media. The video generation unit can introduce cooking methods related to dishes shared by the user's followers. The video generation unit can introduce cooking methods related to posts that the user has "liked". In this way, relevant cooking methods can be introduced by analyzing the user's social media activity. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's social media data into AI and have the AI ​​perform the introduction of cooking methods.

[0104] The interviewing unit can estimate the user's emotions and adjust the satisfaction interview method based on the estimated emotions. For example, if the user is relaxed, the interviewing unit will conduct an interview that includes detailed questions. If the user is in a hurry, the interviewing unit will conduct a concise and to-the-point interview. If the user is excited, the interviewing unit will conduct a visually engaging interview. By adjusting the satisfaction interview method based on the user's emotions, it becomes possible to conduct an optimal interview for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input user emotion data into AI and have the AI ​​adjust the satisfaction interview method.

[0105] The interviewing unit can select the optimal interviewing method by referring to the user's past satisfaction data during the interview. For example, the interviewing unit asks questions related to menus that the user has previously given high ratings to. The interviewing unit avoids asking questions about menus that the user has previously given low ratings to. Based on the user's past satisfaction data, the interviewing unit proposes the optimal interviewing method. In this way, the optimal interviewing method can be selected by referring to the user's past satisfaction data. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's past satisfaction data into AI and have the AI ​​perform the selection of the interviewing method.

[0106] The interviewing unit can customize the content of the satisfaction interview based on the user's current lifestyle. For example, if the user is busy, the interviewing unit will ask concise questions. If the user is relaxed, the interviewing unit will ask detailed questions. If the user is avoiding a particular food, the interviewing unit will avoid questions about that food. By customizing the content of the satisfaction interview based on the user's current lifestyle, it becomes possible to conduct an optimal interview for the user. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's current lifestyle into the AI ​​and have the AI ​​customize the content of the satisfaction interview.

[0107] The interviewing unit can estimate the user's emotions and determine the priority of satisfaction interviews based on the estimated emotions. For example, if the user is relaxed, the interviewing unit will set a low priority and proceed slowly. If the user is in a hurry, the interviewing unit will set a high priority and proceed quickly. If the user is excited, the interviewing unit will set a medium priority and proceed in a balanced manner. This allows for optimal interviews for the user by determining the priority of satisfaction interviews based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input user emotion data into AI and have the AI ​​determine the priority of satisfaction interviews.

[0108] The interviewing unit can select the optimal interviewing method by considering the user's geographical location during the interview. For example, if the user is at home, the interviewing unit conducts the interview in a relaxed environment. If the user is out, the interviewing unit conducts a concise and quick interview. If the user is in a specific location, the interviewing unit asks questions related to that location. In this way, the optimal interviewing method can be selected by considering the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the selection of the interviewing method.

[0109] The interviewing unit can analyze the user's social media activity during the interview and propose content for the satisfaction interview. For example, the interviewing unit can ask questions related to menu items mentioned by the user on social media. The interviewing unit can ask questions related to menu items shared by the user's followers. The interviewing unit can ask questions related to posts that the user has "liked". By analyzing the user's social media activity in this way, the interviewing unit can propose the most suitable content for the satisfaction interview. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's social media data into AI and have the AI ​​propose content for the satisfaction interview.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can analyze the user's past purchase history and select the optimal method for collecting sale information. For example, the data collection unit may prioritize collecting products that the user has frequently purchased in the past. The data collection unit may collect sale information from the user's purchase history for specific days of the week and time slots. The data collection unit analyzes the user's purchasing patterns and proposes the optimal method for collecting sale information. This allows the optimal method for collecting sale information to be selected by analyzing the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past purchase history into AI and have the AI ​​select the method for collecting sale information.

[0112] The analysis unit can apply different analysis algorithms depending on the category of the special offer information during analysis. For example, the analysis unit applies an analysis algorithm that emphasizes freshness to special offer information on fresh foods. For special offer information on processed foods, the analysis unit applies an analysis algorithm that emphasizes shelf life. For special offer information on beverages, the analysis unit applies an analysis algorithm that emphasizes seasonality. By applying different analysis algorithms depending on the category of the special offer information, it becomes possible to make more accurate menu suggestions. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category of the special offer information into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0113] The generation unit can analyze the user's past purchasing behavior during generation to select the optimal shopping list generation method. For example, the generation unit may prioritize including items that the user has frequently purchased in the past in the list. The generation unit may also include items that the user purchases on specific days of the week or at specific times, based on the user's purchase history. The generation unit analyzes the user's purchasing patterns and generates the optimal shopping list. In this way, the optimal shopping list can be generated by analyzing the user's past purchasing behavior. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past purchasing behavior into a generation AI and have the generation AI select the shopping list generation method.

[0114] The video generation unit can refer to the user's past cooking history when generating a video to introduce the optimal cooking method. For example, the video generation unit can introduce similar procedures based on cooking methods the user has successfully used in the past. The video generation unit can introduce new procedures while avoiding cooking methods the user has failed at in the past. The video generation unit can suggest the optimal cooking method from the user's past cooking history. In this way, the optimal cooking method can be introduced by referring to the user's past cooking history. Some or all of the above processes in the video generation unit may be performed using AI or not. For example, the video generation unit can input the user's past cooking history into AI and have the AI ​​perform the introduction of cooking methods.

[0115] The interviewing unit can select the optimal interviewing method by referring to the user's past satisfaction data during the interview. For example, the interviewing unit asks questions related to menus that the user has previously given high ratings to. The interviewing unit avoids asking questions about menus that the user has previously given low ratings to. Based on the user's past satisfaction data, the interviewing unit proposes the optimal interviewing method. In this way, the optimal interviewing method can be selected by referring to the user's past satisfaction data. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the user's past satisfaction data into AI and have the AI ​​perform the selection of the interviewing method.

[0116] The data collection unit can estimate the user's emotions and adjust the timing of collecting sale information based on the estimated emotions. For example, if the user is stressed, the data collection unit will delay collecting sale information and begin collecting it when the user is relaxed. If the user is excited, the data collection unit will collect sale information quickly and begin making suggestions immediately. If the user is tired, the data collection unit will postpone collecting sale information until the next day, allowing the user to rest. By adjusting the timing of sale information collection based on the user's emotions, information can be provided at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of sale information collection.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of menu suggestions based on the estimated emotions. For example, if the user is relaxed, the analysis unit will provide menu suggestions with detailed explanations. If the user is in a hurry, the analysis unit will provide concise and to-the-point menu suggestions. If the user is excited, the analysis unit will provide visually appealing menu suggestions. By adjusting the presentation of menu suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of menu suggestions.

[0118] The generation unit can estimate the user's emotions and adjust the shopping list generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a detailed shopping list. If the user is in a hurry, the generation unit generates a concise and to-the-point shopping list. If the user is excited, the generation unit generates a visually appealing shopping list. In this way, by adjusting the shopping list generation method based on the user's emotions, the system can provide the user with the most suitable list. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the shopping list generation method.

[0119] The video generation unit can estimate the user's emotions and adjust the cooking method presentation based on the estimated emotions. For example, if the user is relaxed, the video generation unit provides a video that progresses at a leisurely pace. If the user is in a hurry, the video generation unit provides a short, concise video. If the user is excited, the video generation unit provides a video with visually stimulating effects. In this way, by adjusting the cooking method presentation based on the user's emotions, the optimal cooking method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the cooking method presentation.

[0120] The interviewing unit can estimate the user's emotions and adjust the satisfaction interview method based on the estimated emotions. For example, if the user is relaxed, the interviewing unit will conduct an interview that includes detailed questions. If the user is in a hurry, the interviewing unit will conduct a concise and to-the-point interview. If the user is excited, the interviewing unit will conduct a visually engaging interview. By adjusting the satisfaction interview method based on the user's emotions, it becomes possible to conduct an optimal interview for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input user emotion data into AI and have the AI ​​adjust the satisfaction interview method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects sale information. For example, it can automatically collect sale information from nearby supermarkets via the internet. It can also collect sale information from supermarkets selected by the user. When a user selects a supermarket from the in-app map, the sale information for that supermarket is collected. Step 2: The analysis unit analyzes the special offer information collected by the collection unit and proposes menus. For example, it uses a generation AI to analyze the collected special offer information and propose the most suitable menu for the user. Based on the special offer information, the generation AI generates menus considering nutritional balance and the user's preferences. Step 3: The generation unit generates a shopping list based on the menu suggested by the analysis unit. For example, using the generation AI, it generates a shopping list of which supermarkets to buy which ingredients to purchase based on the suggested menu. The generation AI generates the optimal shopping list based on sale information. Step 4: The video generation unit creates short videos demonstrating cooking methods based on the shopping list generated by the generation unit. For example, AI is used to generate short videos demonstrating cooking methods based on the generated shopping list. The short videos clearly explain the steps so that users can easily perform the cooking. Step 5: The interviewing department gathers feedback on customer satisfaction after the meal and incorporates it into future menu suggestions. For example, AI can be used to gather feedback on customer satisfaction after the meal and incorporate the results into future menu suggestions.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, video generation unit, and interview unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects special offer information from the internet via the communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected special offer information and propose a menu. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to generate a shopping list based on the proposed menu. The video generation unit is implemented in the control unit 46A of the smart device 14, for example, to introduce cooking methods in a short video based on the generated shopping list. The interview unit is implemented in the control unit 46A of the smart device 14, for example, to interview customers about their satisfaction after the meal and reflect this in the next menu proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, video generation unit, and hearing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects special offer information from the internet via the communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected special offer information and proposes a menu. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates a shopping list based on the proposed menu. The video generation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which introduces cooking methods in a short video based on the generated shopping list. The hearing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which hears about satisfaction after the meal and reflects it in the next menu proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, video generation unit, and hearing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects special offer information from the internet via the communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected special offer information and propose a menu. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to generate a shopping list based on the proposed menu. The video generation unit is implemented in the control unit 46A of the headset terminal 314, for example, to introduce cooking methods in a short video based on the generated shopping list. The hearing unit is implemented in the control unit 46A of the headset terminal 314, for example, to hear about satisfaction after the meal and reflect it in the next menu proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, video generation unit, and hearing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects special offer information from the internet via the communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected special offer information and propose a menu. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to generate a shopping list based on the proposed menu. The video generation unit is implemented in the control unit 46A of the robot 414, for example, to introduce cooking methods in a short video based on the generated shopping list. The hearing unit is implemented in the control unit 46A of the robot 414, for example, to hear about satisfaction after the meal and reflect it in the next menu proposal. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A collection department that collects special sale information, The analysis unit analyzes the special sale information collected by the collection unit and proposes a menu, A generation unit generates a shopping list based on the menu proposed by the analysis unit, A video generation unit that introduces cooking methods in a short video based on the shopping list generated by the generation unit, It includes a department that conducts interviews to gather feedback on customer satisfaction after meals and incorporates this information into future menu suggestions. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects sale information from supermarkets selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected sale information and suggest the most suitable menu for the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the suggested menu, generate a shopping list of which supermarkets to buy which ingredients from. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned video generation unit, Based on the generated shopping list, a short video will show you how to cook. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned hearing section is, We will gather feedback on customer satisfaction after the meal and incorporate the results into future menu suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting special offer information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past purchase history to select the optimal method for collecting special offer information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting sale information, the system filters the results based on the user's current food inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of sale information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sale information, the system prioritizes collecting highly relevant sale information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting sale information, we analyze users' social media activity and collect relevant sale information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the presentation of menu suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail in menu suggestions is adjusted based on the importance of the special offer information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the special offer information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of menu suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of menu suggestions is determined based on when the special offer information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of menu suggestions is adjusted based on the relevance of special offer information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how shopping lists are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system analyzes the user's past purchasing behavior to select the optimal method for generating a shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the contents of the shopping list are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and prioritizes items on the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system takes the user's geographical location into consideration to create an optimal shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system analyzes the user's social media activity to suggest items for their shopping list. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned video generation unit, The system estimates the user's emotions and adjusts the way cooking instructions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned video generation unit, When generating a video, the system will refer to the user's past cooking history to recommend the most suitable cooking method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned video generation unit, When generating a video, the difficulty level of the cooking method is adjusted based on the user's current cooking skill level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned video generation unit, The system estimates the user's emotions and adjusts the order in which cooking methods are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned video generation unit, When generating a video, the system selects the optimal video format by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned video generation unit, When generating videos, we analyze users' social media activity and introduce relevant cooking methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned hearing section is, We estimate the user's emotions and adjust the satisfaction assessment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned hearing section is, During the interview process, we select the most suitable interview method by referring to the user's past satisfaction data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned hearing section is, During the interview, the content of the satisfaction assessment will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned hearing section is, The system estimates user emotions and prioritizes satisfaction interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned hearing section is, During the interview process, the most suitable interview method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned hearing section is, During the interview, we will analyze the user's social media activity and propose content for the satisfaction assessment. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects special sale information, The analysis unit analyzes the special sale information collected by the collection unit and proposes a menu, A generation unit generates a shopping list based on the menu proposed by the analysis unit, A video generation unit that introduces cooking methods in a short video based on the shopping list generated by the generation unit, It includes a department that conducts interviews to gather feedback on customer satisfaction after meals and incorporates this information into future menu suggestions. A system characterized by the following features.

2. The aforementioned collection unit is Collects sale information from supermarkets selected by the user. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected sale information and suggest the most suitable menu for the user. The system according to feature 1.

4. The generating unit is Based on the suggested menu, generate a shopping list of which supermarkets to buy which ingredients from. The system according to feature 1.

5. The aforementioned video generation unit, Based on the generated shopping list, a short video will show you how to cook. The system according to feature 1.

6. The aforementioned hearing section is, We will gather feedback on customer satisfaction after the meal and incorporate the results into future menu suggestions. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting special offer information based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past purchase history to select the optimal method for collecting special offer information. The system according to feature 1.

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