system

Abstract

The system according to this embodiment aims to efficiently suggest the optimal recipe in response to rising prices. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a presentation unit. The collection unit collects price information. The analysis unit analyzes the price information collected by the collection unit. The proposal unit proposes an optimal recipe based on the information obtained by the analysis unit. The presentation unit presents the recipe proposed by the proposal unit.

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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, with the rise in prices, there is a problem that it is time-consuming and difficult to efficiently collect and analyze information for finding an optimal recipe.

[0005] The system according to the embodiment is intended to efficiently propose an optimal recipe in response to the rise in prices.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a presentation unit. The collection unit collects price information. The analysis unit analyzes the price information collected by the collection unit. The proposal unit proposes an optimal recipe based on the information obtained by the analysis unit. The presentation unit presents the recipe proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond to price increases and efficiently suggest the optimal recipe. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) The home cooking suggestion system according to an embodiment of the present invention aims to bring happiness to families by allowing them to enjoy the most suitable home-cooked meals. The home cooking suggestion system proposes optimal recipes through an AI agent, aiming to encourage saving even a single yen amidst rising prices. The home cooking suggestion system analyzes the latest methods of sourcing ingredients in the vicinity of the home and surrounding areas. The system optimizes purchasing methods by including information not only from major supermarkets but also from retail stores. As a result, the home cooking suggestion system is also useful for household budgeting. The home cooking suggestion system proposes recipes considering the preferences and health of the family. The system makes suggestions on a weekly or monthly basis. For example, if a family member has an allergy to a particular ingredient, the system will propose a recipe that avoids that ingredient. The system also proposes nutritionally balanced recipes, taking health into consideration. Through this mechanism, the home cooking suggestion system allows families to enjoy the most suitable home-cooked meals and to be mindful of saving money even amid rising prices. Furthermore, since the home cooking suggestion system is also useful for household budgeting, managing household finances becomes easier. In this way, the home cooking suggestion system allows families to enjoy the most suitable home-cooked meals and bring happiness to their families.

[0029] The home cooking suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a presentation unit. The collection unit collects price information. For example, the collection unit can collect price information from nearby supermarkets and retail stores. The collection unit can also collect price information using AI. The analysis unit analyzes the price information collected by the collection unit. For example, the analysis unit can suggest the optimal purchasing method based on the collected price information. The analysis unit can also analyze price information using AI. The suggestion unit suggests the optimal recipe based on the information obtained by the analysis unit. For example, the suggestion unit can suggest recipes considering family preferences and health. The suggestion unit can also suggest recipes using AI. The presentation unit presents the recipes suggested by the suggestion unit. For example, the presentation unit can present the suggested recipes to the user on a weekly or monthly basis. The presentation unit can also present recipes using AI. As a result, the home cooking suggestion system according to this embodiment can efficiently collect, analyze, suggest, and present price information.

[0030] The data collection unit collects price information. For example, the data collection unit can collect price information from nearby supermarkets and retail stores. Specifically, the data collection unit obtains price information from each store's website and online advertisements via the internet. It can also collect price information from store digital signage and electronic flyers. Furthermore, the data collection unit can also collect price information using AI. The AI ​​automatically collects price information using web scraping technology and stores it in a database. For example, the AI ​​analyzes web pages based on specific keywords or product names and extracts price information. The AI ​​can also regularly update the collected price information to maintain the latest information. This allows the data collection unit to efficiently collect a wide range of price information and always provide the latest information. In addition, the data collection unit can prioritize collecting price information from nearby stores based on the user's location information. This allows users to find the best deals near their home or workplace. The data collection unit can centrally manage this price information and provide it efficiently in cooperation with other departments.

[0031] The analysis department analyzes price information collected by the data collection department. For example, the analysis department can suggest the optimal purchasing method based on the collected price information. Specifically, the analysis department compares the collected price information and creates a list of the cheapest stores and products. Also, if a particular product is sold at multiple stores, it can suggest the most advantageous time to purchase, taking into account price fluctuations and discount information. The analysis department can also analyze price information using AI. The AI ​​uses machine learning algorithms to analyze price trends and patterns and predict future price fluctuations. For example, the AI ​​can predict when a particular product will be discounted based on past price data and notify the user. The AI ​​can also suggest the optimal purchasing method individually, taking into account the user's purchase history and preferences. In this way, the analysis department can provide users with the most economical purchasing method and contribute to saving money on household expenses. Furthermore, based on the collected price information, the analysis department can analyze price differences by region and price fluctuations by season, and provide useful information to users.

[0032] The suggestion department proposes optimal recipes based on information obtained by the analysis department. For example, the suggestion department can suggest recipes considering family preferences and health. Specifically, the suggestion department selects the optimal recipe considering the user's food preferences, allergy information, and nutritional balance. The suggestion department can also suggest recipes using AI. The AI ​​generates individually customized recipes based on the user's past cooking history and ratings. For example, the AI ​​analyzes recipes that the user has highly rated in the past and dishes that they frequently make, and suggests new recipes based on that. The AI ​​can also suggest cost-effective recipes considering collected price information. This allows the suggestion department to provide users with economical and healthy meals. Furthermore, the suggestion department can suggest recipes that utilize seasonal ingredients and ingredients that are in season. This allows users to enjoy nutritious meals while appreciating the changing seasons. The suggestion department can flexibly suggest recipes according to the user's lifestyle and dietary preferences, improving the enjoyment and convenience of home cooking.

[0033] The presentation unit displays recipes suggested by the suggestion unit. For example, the presentation unit can present suggested recipes to the user on a weekly or monthly basis. Specifically, the presentation unit displays recipes in a calendar format according to the user's schedule and meal plans. It can also provide a list of necessary ingredients and information on where to purchase them based on the recipe selected by the user. The presentation unit can also present recipes using AI. The AI ​​suggests and notifies the user of recipes at the optimal time based on the user's preferences and past selection history. For example, if the user has a habit of cooking on a particular day of the week, the AI ​​will suggest recipes tailored to that day. The AI ​​can also collect user feedback and continuously improve the suggestions. This allows the presentation unit to provide the user with the most suitable recipes in a timely manner and support their meal planning. Furthermore, the presentation unit can provide detailed instructions and cooking tips for recipes using videos and images. This allows the user to understand the recipes in a visually easy-to-understand way and proceed with cooking smoothly. The presentation unit can enhance user convenience and improve the enjoyment and efficiency of home cooking.

[0034] The data collection unit can collect price information from nearby supermarkets and retail stores. For example, it can collect price information from nearby supermarkets and retail stores. It can also collect price information from stores within walking distance or from specific chain stores. The data collection unit can also use AI to collect price information. This allows the data collection unit to obtain more accurate price information by collecting price information from nearby supermarkets and retail stores.

[0035] The analysis department can suggest the optimal purchasing method based on the collected price information. For example, the analysis department can suggest optimal purchasing methods such as bulk buying or taking advantage of sale days. The analysis department can also analyze price information using AI. As a result, the analysis department can suggest the optimal purchasing method, enabling users to shop more efficiently.

[0036] The suggestion function can propose recipes that take into account the family's preferences and health. For example, the suggestion function can propose recipes that consider the family's preferences and health. The suggestion function can also propose recipes that take into account allergy information and nutritional balance. The suggestion function can even use AI to propose recipes. This allows the suggestion function to provide meals that satisfy the whole family by suggesting recipes that take into account the family's preferences and health.

[0037] The presentation unit can present suggested recipes to the user on a weekly or monthly basis. For example, the presentation unit can present suggested recipes to the user on a weekly or monthly basis. The presentation unit can present recipes every Monday, or present a batch of recipes at the beginning of the month. The presentation unit can also present recipes using AI. This allows the presentation unit to present recipes on a weekly or monthly basis, enabling users to prepare meals in a planned manner.

[0038] The data collection unit can analyze the user's past purchase history and select the optimal collection method during data collection. For example, the data collection unit can prioritize collecting ingredients that the user has frequently purchased in the past. The data collection unit can analyze the user's past purchase history to determine purchasing trends on specific days of the week or times of day, and collect data at those times. Based on the user's past purchase history, the data collection unit can prioritize collecting price information from specific stores. In this way, the data collection unit can select the optimal collection method by analyzing the user's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without using AI.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, if the user is health-conscious, the data collection unit can prioritize collecting price information for organic ingredients. If the user is budget-conscious, the data collection unit can prioritize collecting discount and special offer information. If the user is interested in a particular ingredient, the data collection unit can prioritize collecting price information for that ingredient. In this way, the data collection unit can collect more relevant price information by filtering based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without using AI.

[0040] The data collection unit can prioritize collecting highly relevant price information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize collecting price information from supermarkets and retail stores near the user's residence. The data collection unit can prioritize collecting price information from areas the user frequently visits. The data collection unit can prioritize collecting price information from stores along the user's commute route. In this way, the data collection unit can collect more relevant price 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, for example, or without using AI.

[0041] The data collection unit can analyze the user's social media activity and collect relevant price information during the collection process. For example, the data collection unit can collect price information of ingredients that the user has shown interest in on social media. The data collection unit can collect price information of ingredients that have been featured by influencers that the user follows. The data collection unit can collect price information of ingredients that are trending in cooking communities that the user participates in. In this way, the data collection unit can collect relevant price information 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, for example, or without using AI.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the price information during the analysis. For example, the analysis unit can perform a detailed analysis on important price information, and a concise analysis on less important price information. The analysis unit can adjust the level of detail of its analysis in stages according to the importance of the price information. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of its analysis based on the importance of the price information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The analysis unit can apply different analysis algorithms depending on the category of price information during analysis. For example, the analysis unit can apply an analysis algorithm that takes into account freshness and expiration date to price information of fresh food. For price information of processed food, the analysis unit can apply an analysis algorithm that takes into account shelf life and nutritional value. For price information of seasonings, the analysis unit can apply an analysis algorithm that takes into account frequency of use and storage method. In this way, the analysis unit can obtain more accurate analysis results by applying different analysis algorithms depending on the category of price information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can determine the priority of its analysis based on when the price information was collected. For example, the analysis unit may prioritize the analysis of the most recent price information. The analysis unit may lower the priority of older price information for analysis. The analysis unit can adjust the priority of its analysis in stages according to when the price information was collected. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of its analysis based on when the price information was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can adjust the order of analysis based on the relevance of price information during the analysis. For example, the analysis unit can prioritize the analysis of price information of high user interest. The analysis unit can postpone the analysis of price information of low user interest. The analysis unit can adjust the order of analysis in stages according to the relevance of price information. In this way, the analysis unit can prioritize the analysis of information that is important to the user by adjusting the order of analysis based on the relevance of price information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The suggestion function can adjust the level of detail in its suggestions based on the importance of the recipes. For example, it can provide detailed suggestions for important recipes and concise suggestions for less important recipes. The suggestion function can adjust the level of detail in its suggestions in stages according to the importance of the recipes. This allows the suggestion function to provide efficient suggestions by adjusting the level of detail based on the importance of the recipes. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI.

[0047] The suggestion unit can apply different suggestion algorithms depending on the recipe category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm that considers nutritional balance to main dish recipes. For side dish recipes, it can apply a suggestion algorithm that considers cooking time. For dessert recipes, it can apply a suggestion algorithm that considers calories. This allows the suggestion unit to provide more appropriate recipe suggestions by applying the optimal suggestion algorithm according to the recipe category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0048] The proposal department can determine the priority of proposals based on when the recipes are submitted. For example, the proposal department will prioritize the most recent recipes. The proposal department can lower the priority of older recipes. The proposal department can adjust the priority of proposals in stages according to when the recipes are submitted. This allows the proposal department to prioritize the most recent recipes by determining the priority of proposals based on when they are submitted. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the recipes when making suggestions. For example, the suggestion unit can prioritize suggesting recipes that are of high interest to the user. The suggestion unit can postpone suggesting recipes that are of low interest to the user. The suggestion unit can adjust the order of suggestions in stages according to the relevance of the recipes. In this way, the suggestion unit can prioritize suggesting recipes that are important to the user by adjusting the order of suggestions based on the relevance of the recipes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0050] The presentation unit can select the optimal presentation method by referring to the user's past recipe viewing history when presenting recipes. For example, the presentation unit can prioritize presenting recipes that the user has frequently viewed in the past. The presentation unit can analyze the user's past viewing history to determine viewing trends at specific times and present recipes at those times. Based on the user's past viewing history, the presentation unit can prioritize presenting recipes from specific categories. In this way, the presentation unit can select the optimal presentation method by referring to the user's past recipe viewing history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0051] The presentation unit can customize the means of presentation based on the user's current life situation at the time of presentation. For example, if the user is busy, the presentation unit can use a concise and highly visible presentation method. If the user is relaxed, the presentation unit can use a presentation method that includes detailed information. If the user tends to view the content at a particular time of day, the presentation unit can use a presentation method that is optimal for that time of day. In this way, the presentation unit can provide more appropriate presentations by customizing the means of presentation based on the user's life situation. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI.

[0052] The presentation unit can select the optimal presentation method when presenting information, taking into account the user's geographical location. For example, the presentation unit can present recipes based on price information from supermarkets and retail stores near the user's home. The presentation unit can present recipes using ingredients from areas the user frequently visits. The presentation unit can present recipes using ingredients from stores along the user's commute route. In this way, the presentation unit can present more relevant information by taking into account the user's geographical location. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0053] The presentation unit can analyze the user's social media activity and suggest presentation methods at the time of presentation. For example, the presentation unit can present recipes using ingredients that the user has shown interest in on social media. The presentation unit can present recipes introduced by influencers that the user follows. The presentation unit can present recipes that are trending in cooking communities that the user participates in. In this way, the presentation unit can present highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The home cooking suggestion system can also be equipped with a storage unit that monitors the storage status of the user's ingredients. For example, the storage unit monitors the expiration dates and inventory levels of ingredients in the refrigerator and suggests consuming them at the appropriate time. The storage unit can also suggest storage methods to prevent ingredient spoilage. This allows the home cooking suggestion system to reduce food waste and achieve efficient ingredient management.

[0056] A home cooking suggestion system can be equipped with a preference learning unit that learns the user's dietary preferences. For example, the preference learning unit analyzes data on recipes and ingredients previously selected by the user and suggests recipes that match the user's preferences. The preference learning unit can also improve its suggestions based on user feedback. This allows the home cooking suggestion system to provide more personalized recipe suggestions.

[0057] A home cooking suggestion system can include a health management unit that monitors the user's health status. This unit can collect data such as the user's weight, blood pressure, and blood sugar levels, and suggest recipes tailored to their health condition. The health management unit can also suggest recipes using ingredients rich in specific nutrients. In this way, the home cooking suggestion system can contribute to maintaining the user's health.

[0058] The home cooking suggestion system can include a timing management unit that manages the user's meal timing. For example, the timing management unit records the user's meal times and frequency and suggests recipes at appropriate times. The timing management unit can also adjust meal preparation times to match the user's schedule. This allows the home cooking suggestion system to provide meal suggestions tailored to the user's lifestyle.

[0059] A home cooking suggestion system can include a satisfaction evaluation unit that assesses the user's satisfaction with the meal. For example, the satisfaction evaluation unit evaluates the user's satisfaction with the recipe based on feedback provided after the meal. Based on this user feedback, the satisfaction evaluation unit can identify areas for improvement in the recipe and incorporate these improvements into future suggestions. This allows the home cooking suggestion system to improve user satisfaction.

[0060] A home cooking suggestion system can include a nutrition management unit that manages the nutritional balance of the user's meals. For example, the nutrition management unit records the user's meals and analyzes their nutritional balance. If a specific nutrient is deficient, the nutrition management unit can suggest recipes to supplement that nutrient. In this way, the home cooking suggestion system can contribute to maintaining the user's health.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects price information. For example, it can collect price information from nearby supermarkets and retail stores. The data collection unit can also use AI to collect price information. Step 2: The analysis unit analyzes the price information collected by the data collection unit. For example, it can suggest the optimal purchasing method based on the collected price information. The analysis unit can also use AI to analyze the price information. Step 3: The suggestion department proposes the optimal recipe based on the information obtained by the analysis department. For example, it can suggest recipes that take into account family preferences and health. The suggestion department can also use AI to suggest recipes. Step 4: The presentation unit presents the recipes suggested by the suggestion unit. For example, the suggested recipes can be presented to the user on a weekly or monthly basis. The presentation unit can also use AI to present recipes.

[0063] (Example of form 2) The home cooking suggestion system according to an embodiment of the present invention aims to bring happiness to families by allowing them to enjoy the most suitable home-cooked meals. The home cooking suggestion system proposes optimal recipes through an AI agent, aiming to encourage saving even a single yen amidst rising prices. The home cooking suggestion system analyzes the latest methods of sourcing ingredients in the vicinity of the home and surrounding areas. The system optimizes purchasing methods by including information not only from major supermarkets but also from retail stores. As a result, the home cooking suggestion system is also useful for household budgeting. The home cooking suggestion system proposes recipes considering the preferences and health of the family. The system makes suggestions on a weekly or monthly basis. For example, if a family member has an allergy to a particular ingredient, the system will propose a recipe that avoids that ingredient. The system also proposes nutritionally balanced recipes, taking health into consideration. Through this mechanism, the home cooking suggestion system allows families to enjoy the most suitable home-cooked meals and to be mindful of saving money even amid rising prices. Furthermore, since the home cooking suggestion system is also useful for household budgeting, managing household finances becomes easier. In this way, the home cooking suggestion system allows families to enjoy the most suitable home-cooked meals and bring happiness to their families.

[0064] The home cooking suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a presentation unit. The collection unit collects price information. For example, the collection unit can collect price information from nearby supermarkets and retail stores. The collection unit can also collect price information using AI. The analysis unit analyzes the price information collected by the collection unit. For example, the analysis unit can suggest the optimal purchasing method based on the collected price information. The analysis unit can also analyze price information using AI. The suggestion unit suggests the optimal recipe based on the information obtained by the analysis unit. For example, the suggestion unit can suggest recipes considering family preferences and health. The suggestion unit can also suggest recipes using AI. The presentation unit presents the recipes suggested by the suggestion unit. For example, the presentation unit can present the suggested recipes to the user on a weekly or monthly basis. The presentation unit can also present recipes using AI. As a result, the home cooking suggestion system according to this embodiment can efficiently collect, analyze, suggest, and present price information.

[0065] The data collection unit collects price information. For example, the data collection unit can collect price information from nearby supermarkets and retail stores. Specifically, the data collection unit obtains price information from each store's website and online advertisements via the internet. It can also collect price information from store digital signage and electronic flyers. Furthermore, the data collection unit can also collect price information using AI. The AI ​​automatically collects price information using web scraping technology and stores it in a database. For example, the AI ​​analyzes web pages based on specific keywords or product names and extracts price information. The AI ​​can also regularly update the collected price information to maintain the latest information. This allows the data collection unit to efficiently collect a wide range of price information and always provide the latest information. In addition, the data collection unit can prioritize collecting price information from nearby stores based on the user's location information. This allows users to find the best deals near their home or workplace. The data collection unit can centrally manage this price information and provide it efficiently in cooperation with other departments.

[0066] The analysis department analyzes price information collected by the data collection department. For example, the analysis department can suggest the optimal purchasing method based on the collected price information. Specifically, the analysis department compares the collected price information and creates a list of the cheapest stores and products. Also, if a particular product is sold at multiple stores, it can suggest the most advantageous time to purchase, taking into account price fluctuations and discount information. The analysis department can also analyze price information using AI. The AI ​​uses machine learning algorithms to analyze price trends and patterns and predict future price fluctuations. For example, the AI ​​can predict when a particular product will be discounted based on past price data and notify the user. The AI ​​can also suggest the optimal purchasing method individually, taking into account the user's purchase history and preferences. In this way, the analysis department can provide users with the most economical purchasing method and contribute to saving money on household expenses. Furthermore, based on the collected price information, the analysis department can analyze price differences by region and price fluctuations by season, and provide useful information to users.

[0067] The suggestion department proposes optimal recipes based on information obtained by the analysis department. For example, the suggestion department can suggest recipes considering family preferences and health. Specifically, the suggestion department selects the optimal recipe considering the user's food preferences, allergy information, and nutritional balance. The suggestion department can also suggest recipes using AI. The AI ​​generates individually customized recipes based on the user's past cooking history and ratings. For example, the AI ​​analyzes recipes that the user has highly rated in the past and dishes that they frequently make, and suggests new recipes based on that. The AI ​​can also suggest cost-effective recipes considering collected price information. This allows the suggestion department to provide users with economical and healthy meals. Furthermore, the suggestion department can suggest recipes that utilize seasonal ingredients and ingredients that are in season. This allows users to enjoy nutritious meals while appreciating the changing seasons. The suggestion department can flexibly suggest recipes according to the user's lifestyle and dietary preferences, improving the enjoyment and convenience of home cooking.

[0068] The presentation unit displays recipes suggested by the suggestion unit. For example, the presentation unit can present suggested recipes to the user on a weekly or monthly basis. Specifically, the presentation unit displays recipes in a calendar format according to the user's schedule and meal plans. It can also provide a list of necessary ingredients and information on where to purchase them based on the recipe selected by the user. The presentation unit can also present recipes using AI. The AI ​​suggests and notifies the user of recipes at the optimal time based on the user's preferences and past selection history. For example, if the user has a habit of cooking on a particular day of the week, the AI ​​will suggest recipes tailored to that day. The AI ​​can also collect user feedback and continuously improve the suggestions. This allows the presentation unit to provide the user with the most suitable recipes in a timely manner and support their meal planning. Furthermore, the presentation unit can provide detailed instructions and cooking tips for recipes using videos and images. This allows the user to understand the recipes in a visually easy-to-understand way and proceed with cooking smoothly. The presentation unit can enhance user convenience and improve the enjoyment and efficiency of home cooking.

[0069] The data collection unit can collect price information from nearby supermarkets and retail stores. For example, it can collect price information from nearby supermarkets and retail stores. It can also collect price information from stores within walking distance or from specific chain stores. The data collection unit can also use AI to collect price information. This allows the data collection unit to obtain more accurate price information by collecting price information from nearby supermarkets and retail stores.

[0070] The analysis department can suggest the optimal purchasing method based on the collected price information. For example, the analysis department can suggest optimal purchasing methods such as bulk buying or taking advantage of sale days. The analysis department can also analyze price information using AI. As a result, the analysis department can suggest the optimal purchasing method, enabling users to shop more efficiently.

[0071] The suggestion function can propose recipes that take into account the family's preferences and health. For example, the suggestion function can propose recipes that consider the family's preferences and health. The suggestion function can also propose recipes that take into account allergy information and nutritional balance. The suggestion function can even use AI to propose recipes. This allows the suggestion function to provide meals that satisfy the whole family by suggesting recipes that take into account the family's preferences and health.

[0072] The presentation unit can present suggested recipes to the user on a weekly or monthly basis. For example, the presentation unit can present suggested recipes to the user on a weekly or monthly basis. The presentation unit can present recipes every Monday, or present a batch of recipes at the beginning of the month. The presentation unit can also present recipes using AI. This allows the presentation unit to present recipes on a weekly or monthly basis, enabling users to prepare meals in a planned manner.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of price information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. If the user is in a hurry, the data collection unit can speed up the collection timing to quickly collect price information. If the user is relaxed, the data collection unit can collect price information at the normal timing. In this way, the data collection unit can reduce user stress by adjusting the collection timing according to 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.

[0074] The data collection unit can analyze the user's past purchase history and select the optimal collection method during data collection. For example, the data collection unit can prioritize collecting ingredients that the user has frequently purchased in the past. The data collection unit can analyze the user's past purchase history to determine purchasing trends on specific days of the week or times of day, and collect data at those times. Based on the user's past purchase history, the data collection unit can prioritize collecting price information from specific stores. In this way, the data collection unit can select the optimal collection method by analyzing the user's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without using AI.

[0075] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, if the user is health-conscious, the data collection unit can prioritize collecting price information for organic ingredients. If the user is budget-conscious, the data collection unit can prioritize collecting discount and special offer information. If the user is interested in a particular ingredient, the data collection unit can prioritize collecting price information for that ingredient. In this way, the data collection unit can collect more relevant price information by filtering based on the user's lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without using AI.

[0076] The data collection unit can estimate the user's emotions and determine the priority of price information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting price information for ingredients that can help reduce stress. If the user is relaxed, the data collection unit will collect price information with normal priority. If the user is in a hurry, the data collection unit will prioritize collecting price information that can be collected quickly. In this way, the data collection unit can provide information that meets the user's needs by prioritizing price information according to 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.

[0077] The data collection unit can prioritize collecting highly relevant price information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize collecting price information from supermarkets and retail stores near the user's residence. The data collection unit can prioritize collecting price information from areas the user frequently visits. The data collection unit can prioritize collecting price information from stores along the user's commute route. In this way, the data collection unit can collect more relevant price 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, for example, or without using AI.

[0078] The data collection unit can analyze the user's social media activity and collect relevant price information during the collection process. For example, the data collection unit can collect price information of ingredients that the user has shown interest in on social media. The data collection unit can collect price information of ingredients that have been featured by influencers that the user follows. The data collection unit can collect price information of ingredients that are trending in cooking communities that the user participates in. In this way, the data collection unit can collect relevant price information 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, for example, or without using AI.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will use a simple and easy-to-understand presentation. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to 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.

[0080] The analysis unit can adjust the level of detail of its analysis based on the importance of the price information during the analysis. For example, the analysis unit can perform a detailed analysis on important price information, and a concise analysis on less important price information. The analysis unit can adjust the level of detail of its analysis in stages according to the importance of the price information. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of its analysis based on the importance of the price information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0081] The analysis unit can apply different analysis algorithms depending on the category of price information during analysis. For example, the analysis unit can apply an analysis algorithm that takes into account freshness and expiration date to price information of fresh food. For price information of processed food, the analysis unit can apply an analysis algorithm that takes into account shelf life and nutritional value. For price information of seasonings, the analysis unit can apply an analysis algorithm that takes into account frequency of use and storage method. In this way, the analysis unit can obtain more accurate analysis results by applying different analysis algorithms depending on the category of price information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is in a hurry, the analysis unit can provide a brief analysis. In this way, the analysis unit can provide an analysis of an appropriate length for the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can determine the priority of its analysis based on when the price information was collected. For example, the analysis unit may prioritize the analysis of the most recent price information. The analysis unit may lower the priority of older price information for analysis. The analysis unit can adjust the priority of its analysis in stages according to when the price information was collected. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of its analysis based on when the price information was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0084] The analysis unit can adjust the order of analysis based on the relevance of price information during the analysis. For example, the analysis unit can prioritize the analysis of price information of high user interest. The analysis unit can postpone the analysis of price information of low user interest. The analysis unit can adjust the order of analysis in stages according to the relevance of price information. In this way, the analysis unit can prioritize the analysis of information that is important to the user by adjusting the order of analysis based on the relevance of price information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will use a simple and easy-to-understand approach. If the user is relaxed, the suggestion function can provide detailed suggestions. If the user is in a hurry, the suggestion function can provide concise suggestions that get straight to the point. In this way, the suggestion function can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The suggestion function can adjust the level of detail in its suggestions based on the importance of the recipes. For example, it can provide detailed suggestions for important recipes and concise suggestions for less important recipes. The suggestion function can adjust the level of detail in its suggestions in stages according to the importance of the recipes. This allows the suggestion function to provide efficient suggestions by adjusting the level of detail based on the importance of the recipes. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI.

[0087] The suggestion unit can apply different suggestion algorithms depending on the recipe category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm that considers nutritional balance to main dish recipes. For side dish recipes, it can apply a suggestion algorithm that considers cooking time. For dessert recipes, it can apply a suggestion algorithm that considers calories. This allows the suggestion unit to provide more appropriate recipe suggestions by applying the optimal suggestion algorithm according to the recipe category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0088] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion function can provide short, to-the-point suggestions. If the user is relaxed, the suggestion function can provide detailed suggestions. If the user is in a hurry, the suggestion function can provide concise suggestions. In this way, the suggestion function can provide suggestions of an appropriate length for the user by adjusting the length according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The proposal department can determine the priority of proposals based on when the recipes are submitted. For example, the proposal department will prioritize the most recent recipes. The proposal department can lower the priority of older recipes. The proposal department can adjust the priority of proposals in stages according to when the recipes are submitted. This allows the proposal department to prioritize the most recent recipes by determining the priority of proposals based on when they are submitted. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0090] The suggestion unit can adjust the order of suggestions based on the relevance of the recipes when making suggestions. For example, the suggestion unit can prioritize suggesting recipes that are of high interest to the user. The suggestion unit can postpone suggesting recipes that are of low interest to the user. The suggestion unit can adjust the order of suggestions in stages according to the relevance of the recipes. In this way, the suggestion unit can prioritize suggesting recipes that are important to the user by adjusting the order of suggestions based on the relevance of the recipes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0091] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is stressed, the presentation unit will use a simple and easy-to-understand presentation method. If the user is relaxed, the presentation unit can use a presentation method that includes detailed information. If the user is in a hurry, the presentation unit can use a concise presentation method that gets straight to the point. In this way, the presentation unit can make the presentation easy for the user to understand by adjusting the presentation method according to 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.

[0092] The presentation unit can select the optimal presentation method by referring to the user's past recipe viewing history when presenting recipes. For example, the presentation unit can prioritize presenting recipes that the user has frequently viewed in the past. The presentation unit can analyze the user's past viewing history to determine viewing trends at specific times and present recipes at those times. Based on the user's past viewing history, the presentation unit can prioritize presenting recipes from specific categories. In this way, the presentation unit can select the optimal presentation method by referring to the user's past recipe viewing history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0093] The presentation unit can customize the means of presentation based on the user's current life situation at the time of presentation. For example, if the user is busy, the presentation unit can use a concise and highly visible presentation method. If the user is relaxed, the presentation unit can use a presentation method that includes detailed information. If the user tends to view the content at a particular time of day, the presentation unit can use a presentation method that is optimal for that time of day. In this way, the presentation unit can provide more appropriate presentations by customizing the means of presentation based on the user's life situation. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI.

[0094] The presentation unit can estimate the user's emotions and determine the priority of presentations based on the estimated emotions. For example, if the user is stressed, the presentation unit will prioritize presenting recipes that help reduce stress. If the user is relaxed, the presentation unit can present recipes with normal priority. If the user is in a hurry, the presentation unit can prioritize presenting recipes that can be presented quickly. In this way, the presentation unit can prioritize presenting information that is important to the user by determining the priority of presentations according to 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.

[0095] The presentation unit can select the optimal presentation method when presenting information, taking into account the user's geographical location. For example, the presentation unit can present recipes based on price information from supermarkets and retail stores near the user's home. The presentation unit can present recipes using ingredients from areas the user frequently visits. The presentation unit can present recipes using ingredients from stores along the user's commute route. In this way, the presentation unit can present more relevant information by taking into account the user's geographical location. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0096] The presentation unit can analyze the user's social media activity and suggest presentation methods at the time of presentation. For example, the presentation unit can present recipes using ingredients that the user has shown interest in on social media. The presentation unit can present recipes introduced by influencers that the user follows. The presentation unit can present recipes that are trending in cooking communities that the user participates in. In this way, the presentation unit can present highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The home cooking suggestion system can also be equipped with a storage unit that monitors the storage status of the user's ingredients. For example, the storage unit monitors the expiration dates and inventory levels of ingredients in the refrigerator and suggests consuming them at the appropriate time. The storage unit can also suggest storage methods to prevent ingredient spoilage. This allows the home cooking suggestion system to reduce food waste and achieve efficient ingredient management.

[0099] A home cooking suggestion system can be equipped with a preference learning unit that learns the user's dietary preferences. For example, the preference learning unit analyzes data on recipes and ingredients previously selected by the user and suggests recipes that match the user's preferences. The preference learning unit can also improve its suggestions based on user feedback. This allows the home cooking suggestion system to provide more personalized recipe suggestions.

[0100] The home cooking suggestion system can estimate the user's emotions and adjust the difficulty of the recipes based on those emotions. For example, if the user is stressed, it can suggest easy and simple recipes. If the user is relaxed, it can suggest recipes that require a little more effort. In this way, the home cooking suggestion system can provide appropriate recipes that match the user's emotions.

[0101] A home cooking suggestion system can include a health management unit that monitors the user's health status. This unit can collect data such as the user's weight, blood pressure, and blood sugar levels, and suggest recipes tailored to their health condition. The health management unit can also suggest recipes using ingredients rich in specific nutrients. In this way, the home cooking suggestion system can contribute to maintaining the user's health.

[0102] The home cooking suggestion system can estimate the user's emotions and adjust the recipe presentation method based on those emotions. For example, if the user is stressed, it can provide a simple and visually easy-to-understand presentation. If the user is relaxed, it can provide a presentation that includes detailed explanations and background information. This allows the home cooking suggestion system to provide appropriate information according to the user's emotions.

[0103] The home cooking suggestion system can include a timing management unit that manages the user's meal timing. For example, the timing management unit records the user's meal times and frequency and suggests recipes at appropriate times. The timing management unit can also adjust meal preparation times to match the user's schedule. This allows the home cooking suggestion system to provide meal suggestions tailored to the user's lifestyle.

[0104] The home cooking suggestion system can estimate the user's emotions and select ingredients based on those emotions. For example, if the user is feeling stressed, it can suggest a recipe using ingredients that have a relaxing effect. If the user is relaxed, it can suggest a recipe using highly nutritious ingredients. This allows the home cooking suggestion system to select ingredients appropriately according to the user's emotions.

[0105] A home cooking suggestion system can include a satisfaction evaluation unit that assesses the user's satisfaction with the meal. For example, the satisfaction evaluation unit evaluates the user's satisfaction with the recipe based on feedback provided after the meal. Based on this user feedback, the satisfaction evaluation unit can identify areas for improvement in the recipe and incorporate these improvements into future suggestions. This allows the home cooking suggestion system to improve user satisfaction.

[0106] The home cooking suggestion system can estimate the user's emotions and suggest a dining atmosphere based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing music and lighting. If the user is relaxed, it can suggest ideas to create a pleasant atmosphere. In this way, the home cooking suggestion system can support the creation of a dining atmosphere that is tailored to the user's emotions.

[0107] A home cooking suggestion system can include a nutrition management unit that manages the nutritional balance of the user's meals. For example, the nutrition management unit records the user's meals and analyzes their nutritional balance. If a specific nutrient is deficient, the nutrition management unit can suggest recipes to supplement that nutrient. In this way, the home cooking suggestion system can contribute to maintaining the user's health.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects price information. For example, it can collect price information from nearby supermarkets and retail stores. The data collection unit can also use AI to collect price information. Step 2: The analysis unit analyzes the price information collected by the data collection unit. For example, it can suggest the optimal purchasing method based on the collected price information. The analysis unit can also use AI to analyze the price information. Step 3: The suggestion department proposes the optimal recipe based on the information obtained by the analysis department. For example, it can suggest recipes that take into account family preferences and health. The suggestion department can also use AI to suggest recipes. Step 4: The presentation unit presents the recipes suggested by the suggestion unit. For example, the suggested recipes can be presented to the user on a weekly or monthly basis. The presentation unit can also use AI to present recipes.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects price information using the camera 42 and communication I / F 44 of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the optimal purchasing method based on the collected price information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal recipe based on the analysis results. The presentation unit presents the proposed recipe to the user using the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and presentation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects price information using the camera 42 and communication I / F 44 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the optimal purchasing method based on the collected price information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal recipe based on the analysis results. The presentation unit presents the proposed recipe to the user, for example, using the display and speaker of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects price information using the camera 42 and communication I / F 44 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the optimal purchasing method based on the collected price information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal recipe based on the analysis results. The presentation unit presents the proposed recipe to the user using the display and speaker of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and presentation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects price information using the camera 42 and communication I / F 44 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the optimal purchasing method based on the collected price information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal recipe based on the analysis results. The presentation unit presents the proposed recipe to the user using the display or speaker of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A collection unit that collects price information, An analysis unit analyzes the price information collected by the aforementioned collection unit, Based on the information obtained by the analysis unit, the proposal unit proposes the optimal recipe, The system includes a presentation unit that presents the recipe proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather price information from nearby supermarkets and retail stores. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Based on the collected price information, we propose the optimal purchase method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest recipes that take into account your family's preferences and health. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, The suggested recipes are presented to the user on a weekly or monthly basis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate user sentiment and adjust the timing of price information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's past purchase history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates user sentiment and prioritizes the price information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant pricing information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant pricing information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of price information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of price information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the price information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of price information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the recipe. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the recipe category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the recipes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the recipes. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, When presenting recipes, the system will refer to the user's past recipe viewing history to select the most suitable presentation method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting information, the presentation method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, It estimates the user's emotions and determines the priority of presentations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting information, the system selects the optimal presentation method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting the content, we analyze the user's social media activity and suggest methods for presentation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 unit that collects price information, An analysis unit analyzes the price information collected by the aforementioned collection unit, Based on the information obtained by the analysis unit, the proposal unit proposes the optimal recipe, The system includes a presentation unit that presents the recipe proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Gather price information from nearby supermarkets and retail stores. The system according to feature 1.

3. The aforementioned analysis unit is Based on the collected price information, we propose the optimal purchase method. The system according to feature 1.

4. The aforementioned proposal section is, We suggest recipes that take into account your family's preferences and health. The system according to feature 1.

5. The aforementioned display unit is, The suggested recipes are presented to the user on a weekly or monthly basis. The system according to feature 1.

6. The aforementioned collection unit is We estimate user sentiment and adjust the timing of price information collection based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes the user's past purchase history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates user sentiment and prioritizes the price information to collect based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant pricing information, taking into account the user's geographical location. The system according to feature 1.

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