system

Abstract

The system according to this embodiment aims to analyze consumer opinions and efficiently propose menus and manage costs. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a cost management unit. The collection unit collects consumer opinions. The analysis unit analyzes the opinions collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The cost management unit provides upgrade and reduction measures based on the cost of ingredients and the supply chain, based on the content 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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there are problems that it is difficult to quickly grasp consumers' preferences and market trends and reflect them in the menu, and it is also difficult to perform efficient cost management.

[0005] The system according to the embodiment aims to analyze consumers' opinions and efficiently perform menu proposal and cost management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a cost management unit. The data collection unit collects consumer opinions. The analysis unit analyzes the opinions collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The cost management unit provides upgrade and reduction measures based on the cost of ingredients and the supply chain, based on the proposals made by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze consumer opinions and efficiently perform menu suggestions and cost management. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). <00​The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The menu optimization system according to an embodiment of the present invention is a system that uses generative AI to analyze consumer feedback and market trend information to optimize a restaurant's menu composition and cost management. The menu optimization system uses generative AI to analyze consumer opinions from review sites and extract the characteristics of menus that have high customer satisfaction and ratings. Next, the generative AI analyzes photos posted on social media and identifies elements that enhance visual appeal, such as "Instagrammable" or "cute." Based on this information, the AI ​​makes suggestions considering the balance between menus and provides upgrade or reduction measures based on the cost of ingredients and supply chains. For example, if a menu item's popularity declines at a particular time, the system suggests fine-tuning visual elements or ingredients to regain consumer interest. This mechanism allows restaurant owners to respond quickly to changes in consumer preferences and the market, enabling them to maintain their competitiveness. As a result, they can expect not only increased sales but also higher customer satisfaction. Furthermore, efficient cost management helps optimize costs while providing high-quality service. For example, the menu optimization system collects text data posted on review sites, and the AI ​​analyzes it to extract the characteristics of menus that have high customer satisfaction and ratings. For example, it can identify why certain menu items receive high ratings and pinpoint the taste and visual characteristics that consumers prefer. Next, the menu optimization system analyzes photos posted on social media. The AI ​​identifies elements that enhance visual appeal, such as "Instagrammable" or "cute," from the photos. For example, it can identify that colorful presentation and creative designs are elements that consumers like. Based on this information, the AI ​​makes suggestions that consider the balance between menu items. Specifically, it optimizes the menu composition and ingredient selection based on consumer preferences and market trends. For example, if a particular menu item loses popularity, it can suggest minor adjustments to its visual elements or ingredients to regain consumer interest. Furthermore, the AI ​​provides upgrade or reduction measures based on ingredient costs and supply chains. For example, if the price of a particular ingredient rises, it can optimize costs by suggesting alternative ingredients.This system allows restaurant owners to respond quickly to consumer preferences and market changes, enabling them to maintain their competitiveness. As a result, they can expect not only increased sales but also higher customer satisfaction. Furthermore, efficient cost management helps optimize costs while providing high-quality service. In this way, the menu optimization system enables restaurants to respond quickly to consumer preferences and market changes, maintaining their competitiveness.

[0029] The menu optimization system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a cost management unit. The collection unit collects consumer opinions. The collection unit collects consumer opinions from, for example, review sites. The collection unit can efficiently collect consumer opinions by collecting text data posted on review sites. The collection unit can also collect data from specific review sites using APIs, for example. The analysis unit analyzes the opinions collected by the collection unit. The analysis unit analyzes the collected opinions using, for example, text mining techniques. The analysis unit can extract customer satisfaction and characteristics of highly-rated menus from the collected opinions. The analysis unit can also analyze the sentiment of the collected opinions using, for example, sentiment analysis techniques. The analysis unit can also analyze the trends of the collected opinions using, for example, statistical analysis techniques. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit makes, for example, menu improvement proposals based on the analysis results. The proposal unit can also make, for example, new product proposals. The proposal department can, for example, make suggestions to optimize menu composition and ingredient selection. The cost management department provides upgrade or reduction measures based on ingredient costs and supply chains, based on the suggestions made by the proposal department. The cost management department can, for example, provide cost reduction measures based on the suggestions. The cost management department can, for example, optimize the supply chain. The cost management department can, for example, propose changes to ingredients. As a result, the menu optimization system according to the embodiment can efficiently collect, analyze, propose, and manage costs based on consumer feedback.

[0030] The data collection unit collects consumer opinions. Specifically, the data collection unit collects consumer opinions from review sites. Review sites contain ratings and impressions posted by consumers about menus they have actually used, and by collecting this text data, consumer opinions can be collected efficiently. The data collection unit can also collect data from specific review sites using APIs. By using APIs, the data collection unit can automatically retrieve the latest reviews and store them in the database. Furthermore, the data collection unit can collect data from other sources such as social media and surveys. For example, it can collect consumer posts and comments on social media to collect a wide range of consumer opinions. It is also possible to conduct surveys to directly collect specific opinions and requests from consumers. In this way, the data collection unit can collect consumer opinions from diverse sources and build a comprehensive database. The collected data is stored as text data and used for analysis by the analysis unit. The data collection unit can flexibly set the frequency and scope of data collection, and it is also possible to focus on collecting consumer opinions in specific periods or regions. In this way, the data collection unit can collect consumer opinions efficiently and effectively and improve the overall performance of the system.

[0031] The analysis unit analyzes the opinions collected by the collection unit. Specifically, the analysis unit uses text mining technology to analyze the collected opinions. By using text mining technology, useful information can be extracted from the large amount of collected text data. For example, it can extract customer satisfaction and the characteristics of highly-rated menu items. The analysis unit can also use sentiment analysis technology to analyze the emotions of the collected opinions. By using sentiment analysis technology, it is possible to determine whether consumer opinions are positive or negative and to quantitatively understand the evaluation of menu items. Furthermore, the analysis unit can also use statistical analysis technology to analyze the trends in the collected opinions. By using statistical analysis technology, it is possible to identify elements and trends that are particularly frequently mentioned in consumer opinions and use this to improve menu items. By combining these technologies, the analysis unit can analyze the collected opinions from multiple angles and accurately understand consumer needs and desires. The analysis results are used by the proposal unit to propose menu improvements and new products. In this way, the analysis unit can analyze the collected data quickly and accurately and improve the overall performance of the system.

[0032] The Proposal Department makes proposals based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department makes menu improvement proposals based on the analysis results. For example, it can propose improvements to existing menus based on the characteristics of menus with high customer satisfaction. It can also propose new products based on the analysis results. For example, it can identify new needs and trends from consumer opinions and propose the development of new products accordingly. Furthermore, the Proposal Department can also propose optimizing menu composition and ingredient selection. For example, based on consumer opinions, it can propose increasing menu variations or adding menus using specific ingredients. When making these proposals, the Proposal Department considers not only consumer opinions but also cost information and supply chain information provided by the Cost Management Department. This allows the Proposal Department to make cost-effective proposals that meet consumer needs. The Proposal Department's proposals are reflected in actual menu improvements and new product development, contributing to increased consumer satisfaction and sales. In this way, the Proposal Department can make concrete proposals based on analysis results and improve the overall performance of the system.

[0033] The Cost Management Department provides upgrade and reduction measures based on the cost of ingredients and the supply chain, based on the proposals made by the Proposal Department. Specifically, the Cost Management Department provides cost reduction measures based on the proposals. For example, it can propose optimizing the cost of ingredients used in proposed menu improvements. The Cost Management Department can also optimize the supply chain. For example, it can propose reviewing ingredient suppliers and building a more cost-effective supply chain. Furthermore, the Cost Management Department can also propose changes to ingredients. For example, if ingredients of the same quality can be procured at a lower cost, it can propose switching to those ingredients. When making these proposals, the Cost Management Department also considers consumer opinions and analysis results provided by the Proposal Department. This allows the Cost Management Department to make cost-effective proposals while meeting consumer needs. The Cost Management Department's proposals are reflected in actual menu improvements and new product development, contributing to cost reduction and increased profits. In this way, the Cost Management Department can work in conjunction with the Proposal Department to improve the overall performance of the system.

[0034] The data collection unit can collect consumer opinions from review sites. For example, the data collection unit can collect text data posted on review sites. By collecting opinions from review sites, the data collection unit can efficiently obtain consumer feedback. The data collection unit can also collect data from specific review sites using APIs, for example. This allows for efficient acquisition of consumer feedback through the collection of opinions from review sites. Review sites include, but are not limited to, specific site names or methods of data collection using APIs. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input text data posted on review sites into a generative AI, which can then analyze the text data to collect consumer opinions.

[0035] The analysis unit can analyze the collected opinions and extract characteristics of menus that have high customer satisfaction and ratings. The analysis unit can analyze the collected opinions using, for example, text mining technology. The analysis unit can extract characteristics of menus that have high customer satisfaction and ratings from the collected opinions. The analysis unit can also analyze the emotions of the collected opinions using, for example, sentiment analysis technology. The analysis unit can also analyze the trends of the collected opinions using, for example, statistical analysis technology. This helps in improving the menu by extracting characteristics of menus that have high customer satisfaction and ratings. Customer satisfaction includes, for example, scoring systems and survey results. Characteristics of highly-rated menus include, for example, taste, appearance, price, etc. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected opinions into a generative AI, which can analyze the opinions and extract characteristics of menus that have high customer satisfaction and ratings.

[0036] The collection unit can collect photos posted on social media. For example, the collection unit collects photos posted on social media. By collecting photos from social media, the collection unit can identify elements that enhance visual appeal. The collection unit can also collect data from specific social media using APIs, for example. This allows the collection unit to identify elements that enhance visual appeal by collecting photos from social media. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input photos posted on social media into a generative AI, which can analyze the photos to identify elements that enhance visual appeal.

[0037] The analysis unit can analyze the collected photographs and identify elements that enhance visual appeal, such as "Instagrammable" or "cute." The analysis unit can analyze the collected photographs using, for example, image analysis technology. The analysis unit can identify elements that enhance visual appeal, such as "Instagrammable" or "cute," from the collected photographs. The analysis unit can also analyze the colors of the collected photographs using, for example, color analysis technology. The analysis unit can also analyze the composition of the collected photographs using, for example, composition analysis technology. By doing so, the visual appeal of the menu is improved by identifying elements that enhance visual appeal. Elements that enhance visual appeal include, but are not limited to, color, composition, and decoration. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected photographs into a generative AI, which can analyze the photographs and identify elements that enhance visual appeal.

[0038] The proposal unit can make suggestions that take into account the balance between menu items based on the analysis results. For example, the proposal unit can make suggestions to improve menu items based on the analysis results. The proposal unit can also make suggestions for new products, for example. The proposal unit can also make suggestions to optimize menu composition and ingredient selection, for example. This makes it possible to optimize menu composition by making suggestions that take into account the balance between menu items. The balance between menu items includes, but is not limited to, nutritional balance and price balance. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the analysis results into a generative AI, and the generative AI can make suggestions that take into account the balance between menu items.

[0039] The cost management department can provide upgrade or reduction measures based on the proposed content, including the cost of ingredients and the supply chain. The cost management department can, for example, provide cost reduction measures based on the proposed content. The cost management department can also, for example, optimize the supply chain. The cost management department can also, for example, propose changes to ingredients. This enables cost optimization by providing upgrade or reduction measures based on the cost of ingredients and the supply chain. Upgrade and reduction measures include, but are not limited to, changes to ingredients and improvements to cooking methods. Some or all of the above processes in the cost management department may be performed, for example, using a generative AI, or not using a generative AI. For example, the cost management department can input the proposed content into a generative AI, and the generative AI can provide upgrade or reduction measures based on the cost of ingredients and the supply chain.

[0040] The data collection unit can analyze the user's past review history during data collection and select the optimal collection method. For example, the data collection unit may prioritize collecting opinions on menus that the user has previously given high ratings to. The data collection unit may also collect detailed opinions on menus that the user has previously given low ratings to. The data collection unit may also concentrate data collection during times when the user frequently posted reviews in the past. This allows the optimal collection method to be selected by analyzing the user's past review history. The optimal collection method includes, but is not limited to, the timing and means of data collection. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit may input the user's past review history into a generative AI, which can then select the optimal collection method.

[0041] The data collection unit can prioritize collecting opinions from specific time periods or event periods. For example, it can prioritize collecting opinions posted during specific time periods such as lunchtime or dinnertime. The data collection unit can also prioritize collecting opinions posted during event periods such as Christmas or Valentine's Day. The data collection unit can also prioritize collecting opinions posted when a new menu is announced. This allows for the efficient collection of important opinions by prioritizing opinions from specific time periods or event periods. Specific time periods or event periods include, but are not limited to, peak times or sales periods. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input opinions from specific time periods or event periods into a generative AI, which can then determine the priority of the opinions.

[0042] The data collection unit can prioritize collecting highly relevant opinions by considering the user's geographical location information during the collection process. For example, if the user is near a restaurant, the data collection unit will prioritize collecting opinions related to that restaurant. For example, if the user is in a specific region, the data collection unit can also prioritize collecting opinions related to that region. For example, if the user is traveling, the data collection unit can also prioritize collecting opinions related to their travel destination. This allows for the efficient collection of highly relevant opinions by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and regional tags. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize collecting highly relevant opinions.

[0043] The data collection unit can analyze a user's social media activity and collect relevant opinions during the collection process. For example, if a user uses a specific hashtag, the data collection unit can prioritize collecting opinions related to that hashtag. The data collection unit can also prioritize collecting opinions related to a specific group or community if the user belongs to one. The data collection unit can also prioritize collecting opinions related to an event if the user participates in one. This allows for the efficient collection of relevant opinions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posting frequency and follower count. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input the user's social media activity into a generative AI, which can then collect relevant opinions.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions during the analysis. For example, the analysis unit can perform a detailed analysis on highly rated opinions. The analysis unit can also perform a detailed analysis on low-rated opinions. For example, the analysis unit can analyze neutral opinions in comparison with other opinions. This allows important opinions to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the opinions. The importance of an opinion includes, but is not limited to, the content of the opinion and the influence of the submitter. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of opinions into a generative AI, and the generative AI can adjust the level of detail of the analysis based on the importance of the opinions.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the opinion during analysis. For example, the analysis unit can apply a specific analysis algorithm to opinions about taste. The analysis unit can also apply a specific analysis algorithm to opinions about appearance. The analysis unit can also apply a specific analysis algorithm to opinions about service. By applying different analysis algorithms depending on the category of the opinion, more accurate analysis becomes possible. The categories of opinions include, for example, positive, negative, and neutral, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of opinions into a generative AI, and the generative AI can apply different analysis algorithms depending on the category of the opinion.

[0046] The analysis unit can determine the priority of analysis based on when the opinions were submitted. For example, the analysis unit may prioritize the analysis of the most recent opinions. The analysis unit may also prioritize the analysis of opinions submitted during a specific event period. The analysis unit may also prioritize the analysis of current opinions while referring to past opinions. This allows for the prioritization of the analysis of the most recent opinions by determining the priority of analysis based on when the opinions were submitted. The submission period of opinions includes, but is not limited to, the submission date and time or the event period. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit can input the submission period of opinions into a generating AI, and the generating AI can determine the priority of analysis based on the submission period of the opinions.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the opinions during the analysis. For example, the analysis unit may prioritize the analysis of highly rated opinions. The analysis unit may also prioritize the analysis of low-rated opinions. The analysis unit may also analyze neutral opinions in comparison with other opinions. This allows important opinions to be prioritized by adjusting the order of analysis based on the relevance of the opinions. The relevance of opinions includes, but is not limited to, similarity of content or relationship between submitters. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of opinions into a generative AI, which can then adjust the order of analysis based on the relevance of the opinions.

[0048] The suggestion department can adjust the level of detail in its suggestions based on the importance of the menu items. For example, the suggestion department can provide detailed suggestions for popular menu items. It can also provide detailed suggestions for new menu items. It can also provide detailed suggestions for seasonal menu items. By adjusting the level of detail in suggestions based on the importance of the menu items, it becomes possible to provide detailed suggestions for important menu items. Menu importance includes, but is not limited to, sales data and customer ratings. Some or all of the above processing in the suggestion department may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion department can input the importance of the menu items into a generative AI, which can then adjust the level of detail in its suggestions based on the importance of the menu items.

[0049] The suggestion unit can apply different suggestion algorithms depending on the menu category when making suggestions. For example, the suggestion unit can apply a specific suggestion algorithm to a dessert menu. The suggestion unit can also apply a specific suggestion algorithm to a main dish menu. The suggestion unit can also apply a specific suggestion algorithm to a drink menu menu. By applying different suggestion algorithms depending on the menu category, more appropriate suggestions become possible. Menu categories include, but are not limited to, appetizers, main dishes, and desserts. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the menu category into a generative AI, which can then apply a different suggestion algorithm depending on the menu category.

[0050] The proposal department can determine the priority of proposals based on the submission timing of the menus. For example, the proposal department may prioritize proposals for the latest menus. The proposal department may also prioritize proposals for seasonal menus. The proposal department may also prioritize proposals for current menus, while referring to past menus. This allows for prioritizing proposals for the latest menus by determining the priority of proposals based on the submission timing of the menus. The submission timing of the menus includes, but is not limited to, the submission date and time or the event period. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the menu submission timing into a generative AI, which can then determine the priority of proposals based on the submission timing.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the menu items. For example, the suggestion unit may prioritize suggesting popular menu items. The suggestion unit may also prioritize suggesting new menu items. The suggestion unit may also prioritize suggesting seasonal menu items. By adjusting the order of suggestions based on the relevance of the menu items, it becomes possible to prioritize suggesting important menu items. Menu relevance includes, but is not limited to, similarity of content and customer preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the menu relevance into a generative AI, which can then adjust the order of suggestions based on the relevance.

[0052] The cost management department can monitor fluctuations in ingredient prices in real time during cost management and select the optimal cost management method. For example, if ingredient prices rise sharply, the cost management department can suggest alternative ingredients. For example, if ingredient prices fall, the cost management department can suggest more expensive ingredients to improve quality. For example, if ingredient prices are stable, the cost management department can suggest ways to maintain quality while keeping costs down. This enables optimal cost management by monitoring ingredient price fluctuations in real time. Ingredient price fluctuations include, but are not limited to, real-time data and price prediction models. Some or all of the above processes in the cost management department may be performed using, for example, generative AI, or not. For example, the cost management department can input ingredient price fluctuations into a generative AI, which can then select the optimal cost management method based on the price fluctuations.

[0053] The cost management department can customize its cost management methods when managing costs, taking into account the stability of the supply chain. For example, if the supply chain is stable, the cost management department can propose methods to maintain quality while keeping costs down. If the supply chain is unstable, the cost management department can also propose alternative suppliers. If the supply chain is fluctuating, the cost management department can also propose diversification of suppliers. This allows for more appropriate cost management by considering the stability of the supply chain. Supply chain stability includes, but is not limited to, the reliability of suppliers and fluctuations in supply quantities. Some or all of the above processes in the cost management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the cost management department can input the stability of the supply chain into a generative AI, and the generative AI can customize the cost management methods based on that stability.

[0054] The cost management department can select the optimal cost management method when managing costs, taking into account the geographical distribution of ingredients. For example, the cost management department can reduce transportation costs by prioritizing the use of local ingredients. For example, when using imported ingredients, the cost management department can also select the optimal supplier, taking into account transportation costs. For example, the cost management department can propose the optimal cost management method by taking into account the geographical distribution of ingredients each season. This makes it possible to manage costs more appropriately by taking into account the geographical distribution of ingredients. The geographical distribution of ingredients includes, but is not limited to, information on the place of origin and transportation costs. Some or all of the above processes in the cost management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the cost management department can input the geographical distribution of ingredients into a generative AI, and the generative AI can select the optimal cost management method based on the geographical distribution.

[0055] The cost management department can improve the accuracy of cost management by referring to relevant literature on ingredients during cost management. For example, the cost management department can refer to literature on ingredient price fluctuations to propose the optimal cost management method. For example, the cost management department can refer to literature on ingredient quality to propose methods for reducing costs while maintaining quality. For example, the cost management department can refer to literature on ingredient supply chains to propose diversification of suppliers. In this way, the accuracy of cost management is improved by referring to relevant literature on ingredients. Relevant literature on ingredients includes, but is not limited to, academic papers and industry reports. Some or all of the above processes in the cost management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the cost management department can input relevant literature on ingredients into a generative AI, and the generative AI can improve the accuracy of cost management based on the literature.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze collected opinions and extract characteristics of menus that receive high customer satisfaction ratings. For example, it can use text mining techniques to analyze collected opinions. It can also use sentiment analysis techniques to analyze the emotions behind the collected opinions. Furthermore, it can use statistical analysis techniques to analyze trends in the collected opinions. This helps in improving menus by extracting characteristics of menus that receive high customer satisfaction ratings.

[0058] The proposal department can make suggestions that consider the balance between menu items based on the analysis results. For example, it can make suggestions to improve menu items based on the analysis results. It can also propose new products. Furthermore, it can make suggestions to optimize the menu composition and ingredient selection. This makes it possible to optimize the menu composition by making suggestions that consider the balance between menu items.

[0059] The cost management department can provide upgrade or reduction measures based on the proposed content, including cost analysis of ingredients and supply chains. For example, it can provide cost reduction measures based on the proposed content. It can also optimize the supply chain. Furthermore, it can propose changes to ingredients. By providing upgrade or reduction measures based on ingredient costs and supply chains, cost optimization becomes possible.

[0060] The cost management department can monitor fluctuations in ingredient prices in real time during cost management and select the optimal cost management method. For example, if ingredient prices rise sharply, they can propose alternative ingredients. If ingredient prices fall, they can propose more expensive ingredients to improve quality. Furthermore, if ingredient prices are stable, they can propose methods to maintain quality while keeping costs down. In this way, optimal cost management becomes possible by monitoring fluctuations in ingredient prices in real time.

[0061] The cost management department can customize its cost management methods when conducting cost management, taking into account the stability of the supply chain. For example, if the supply chain is stable, it can propose methods to maintain quality while keeping costs down. If the supply chain is unstable, it can propose alternative suppliers. Furthermore, if the supply chain is fluctuating, it can propose diversification of suppliers. By considering the stability of the supply chain, more appropriate cost management becomes possible.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit gathers consumer opinions. For example, it collects consumer opinions from review sites and gathers text data posted on those sites. The data collection unit can also collect data from specific review sites using APIs. Step 2: The analysis unit analyzes the opinions collected by the collection unit. For example, it may use text mining techniques to analyze the collected opinions and extract characteristics of customer satisfaction and highly-rated menu items. It may also use sentiment analysis techniques and statistical analysis techniques to analyze the sentiment and trends of the collected opinions. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. For example, they may make suggestions for menu improvements or new products based on the analysis results. They can also make suggestions to optimize menu composition or ingredient selection. Step 4: The Cost Management Department provides upgrade or reduction measures based on the cost of ingredients and the supply chain, based on the proposals made by the Proposal Department. For example, they may provide cost reduction measures based on the proposals and suggest optimization of the supply chain or changes to ingredients.

[0064] (Example of form 2) The menu optimization system according to an embodiment of the present invention is a system that uses generative AI to analyze consumer feedback and market trend information to optimize a restaurant's menu composition and cost management. The menu optimization system uses generative AI to analyze consumer opinions from review sites and extract the characteristics of menus that have high customer satisfaction and ratings. Next, the generative AI analyzes photos posted on social media and identifies elements that enhance visual appeal, such as "Instagrammable" or "cute." Based on this information, the AI ​​makes suggestions considering the balance between menus and provides upgrade or reduction measures based on the cost of ingredients and supply chains. For example, if a menu item's popularity declines at a particular time, the system suggests fine-tuning visual elements or ingredients to regain consumer interest. This mechanism allows restaurant owners to respond quickly to changes in consumer preferences and the market, enabling them to maintain their competitiveness. As a result, they can expect not only increased sales but also higher customer satisfaction. Furthermore, efficient cost management helps optimize costs while providing high-quality service. For example, the menu optimization system collects text data posted on review sites, and the AI ​​analyzes it to extract the characteristics of menus that have high customer satisfaction and ratings. For example, it can identify why certain menu items receive high ratings and pinpoint the taste and visual characteristics that consumers prefer. Next, the menu optimization system analyzes photos posted on social media. The AI ​​identifies elements that enhance visual appeal, such as "Instagrammable" or "cute," from the photos. For example, it can identify that colorful presentation and creative designs are elements that consumers like. Based on this information, the AI ​​makes suggestions that consider the balance between menu items. Specifically, it optimizes the menu composition and ingredient selection based on consumer preferences and market trends. For example, if a particular menu item loses popularity, it can suggest minor adjustments to its visual elements or ingredients to regain consumer interest. Furthermore, the AI ​​provides upgrade or reduction measures based on ingredient costs and supply chains. For example, if the price of a particular ingredient rises, it can optimize costs by suggesting alternative ingredients.This system allows restaurant owners to respond quickly to consumer preferences and market changes, enabling them to maintain their competitiveness. As a result, they can expect not only increased sales but also higher customer satisfaction. Furthermore, efficient cost management helps optimize costs while providing high-quality service. In this way, the menu optimization system enables restaurants to respond quickly to consumer preferences and market changes, maintaining their competitiveness.

[0065] The menu optimization system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a cost management unit. The collection unit collects consumer opinions. The collection unit collects consumer opinions from, for example, review sites. The collection unit can efficiently collect consumer opinions by collecting text data posted on review sites. The collection unit can also collect data from specific review sites using APIs, for example. The analysis unit analyzes the opinions collected by the collection unit. The analysis unit analyzes the collected opinions using, for example, text mining techniques. The analysis unit can extract customer satisfaction and characteristics of highly-rated menus from the collected opinions. The analysis unit can also analyze the sentiment of the collected opinions using, for example, sentiment analysis techniques. The analysis unit can also analyze the trends of the collected opinions using, for example, statistical analysis techniques. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit makes, for example, menu improvement proposals based on the analysis results. The proposal unit can also make, for example, new product proposals. The proposal department can, for example, make suggestions to optimize menu composition and ingredient selection. The cost management department provides upgrade or reduction measures based on ingredient costs and supply chains, based on the suggestions made by the proposal department. The cost management department can, for example, provide cost reduction measures based on the suggestions. The cost management department can, for example, optimize the supply chain. The cost management department can, for example, propose changes to ingredients. As a result, the menu optimization system according to the embodiment can efficiently collect, analyze, propose, and manage costs based on consumer feedback.

[0066] The data collection unit collects consumer opinions. Specifically, the data collection unit collects consumer opinions from review sites. Review sites contain ratings and impressions posted by consumers about menus they have actually used, and by collecting this text data, consumer opinions can be collected efficiently. The data collection unit can also collect data from specific review sites using APIs. By using APIs, the data collection unit can automatically retrieve the latest reviews and store them in the database. Furthermore, the data collection unit can collect data from other sources such as social media and surveys. For example, it can collect consumer posts and comments on social media to collect a wide range of consumer opinions. It is also possible to conduct surveys to directly collect specific opinions and requests from consumers. In this way, the data collection unit can collect consumer opinions from diverse sources and build a comprehensive database. The collected data is stored as text data and used for analysis by the analysis unit. The data collection unit can flexibly set the frequency and scope of data collection, and it is also possible to focus on collecting consumer opinions in specific periods or regions. In this way, the data collection unit can collect consumer opinions efficiently and effectively and improve the overall performance of the system.

[0067] The analysis unit analyzes the opinions collected by the collection unit. Specifically, the analysis unit uses text mining technology to analyze the collected opinions. By using text mining technology, useful information can be extracted from the large amount of collected text data. For example, it can extract customer satisfaction and the characteristics of highly-rated menu items. The analysis unit can also use sentiment analysis technology to analyze the emotions of the collected opinions. By using sentiment analysis technology, it is possible to determine whether consumer opinions are positive or negative and to quantitatively understand the evaluation of menu items. Furthermore, the analysis unit can also use statistical analysis technology to analyze the trends in the collected opinions. By using statistical analysis technology, it is possible to identify elements and trends that are particularly frequently mentioned in consumer opinions and use this to improve menu items. By combining these technologies, the analysis unit can analyze the collected opinions from multiple angles and accurately understand consumer needs and desires. The analysis results are used by the proposal unit to propose menu improvements and new products. In this way, the analysis unit can analyze the collected data quickly and accurately and improve the overall performance of the system.

[0068] The Proposal Department makes proposals based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department makes menu improvement proposals based on the analysis results. For example, it can propose improvements to existing menus based on the characteristics of menus with high customer satisfaction. It can also propose new products based on the analysis results. For example, it can identify new needs and trends from consumer opinions and propose the development of new products accordingly. Furthermore, the Proposal Department can also propose optimizing menu composition and ingredient selection. For example, based on consumer opinions, it can propose increasing menu variations or adding menus using specific ingredients. When making these proposals, the Proposal Department considers not only consumer opinions but also cost information and supply chain information provided by the Cost Management Department. This allows the Proposal Department to make cost-effective proposals that meet consumer needs. The Proposal Department's proposals are reflected in actual menu improvements and new product development, contributing to increased consumer satisfaction and sales. In this way, the Proposal Department can make concrete proposals based on analysis results and improve the overall performance of the system.

[0069] The Cost Management Department provides upgrade and reduction measures based on the cost of ingredients and the supply chain, based on the proposals made by the Proposal Department. Specifically, the Cost Management Department provides cost reduction measures based on the proposals. For example, it can propose optimizing the cost of ingredients used in proposed menu improvements. The Cost Management Department can also optimize the supply chain. For example, it can propose reviewing ingredient suppliers and building a more cost-effective supply chain. Furthermore, the Cost Management Department can also propose changes to ingredients. For example, if ingredients of the same quality can be procured at a lower cost, it can propose switching to those ingredients. When making these proposals, the Cost Management Department also considers consumer opinions and analysis results provided by the Proposal Department. This allows the Cost Management Department to make cost-effective proposals while meeting consumer needs. The Cost Management Department's proposals are reflected in actual menu improvements and new product development, contributing to cost reduction and increased profits. In this way, the Cost Management Department can work in conjunction with the Proposal Department to improve the overall performance of the system.

[0070] The data collection unit can collect consumer opinions from review sites. For example, the data collection unit can collect text data posted on review sites. By collecting opinions from review sites, the data collection unit can efficiently obtain consumer feedback. The data collection unit can also collect data from specific review sites using APIs, for example. This allows for efficient acquisition of consumer feedback through the collection of opinions from review sites. Review sites include, but are not limited to, specific site names or methods of data collection using APIs. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input text data posted on review sites into a generative AI, which can then analyze the text data to collect consumer opinions.

[0071] The analysis unit can analyze the collected opinions and extract characteristics of menus that have high customer satisfaction and ratings. The analysis unit can analyze the collected opinions using, for example, text mining technology. The analysis unit can extract characteristics of menus that have high customer satisfaction and ratings from the collected opinions. The analysis unit can also analyze the emotions of the collected opinions using, for example, sentiment analysis technology. The analysis unit can also analyze the trends of the collected opinions using, for example, statistical analysis technology. This helps in improving the menu by extracting characteristics of menus that have high customer satisfaction and ratings. Customer satisfaction includes, for example, scoring systems and survey results. Characteristics of highly-rated menus include, for example, taste, appearance, price, etc. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected opinions into a generative AI, which can analyze the opinions and extract characteristics of menus that have high customer satisfaction and ratings.

[0072] The collection unit can collect photos posted on social media. For example, the collection unit collects photos posted on social media. By collecting photos from social media, the collection unit can identify elements that enhance visual appeal. The collection unit can also collect data from specific social media using APIs, for example. This allows the collection unit to identify elements that enhance visual appeal by collecting photos from social media. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input photos posted on social media into a generative AI, which can analyze the photos to identify elements that enhance visual appeal.

[0073] The analysis unit can analyze the collected photographs and identify elements that enhance visual appeal, such as "Instagrammable" or "cute." The analysis unit can analyze the collected photographs using, for example, image analysis technology. The analysis unit can identify elements that enhance visual appeal, such as "Instagrammable" or "cute," from the collected photographs. The analysis unit can also analyze the colors of the collected photographs using, for example, color analysis technology. The analysis unit can also analyze the composition of the collected photographs using, for example, composition analysis technology. By doing so, the visual appeal of the menu is improved by identifying elements that enhance visual appeal. Elements that enhance visual appeal include, but are not limited to, color, composition, and decoration. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected photographs into a generative AI, which can analyze the photographs and identify elements that enhance visual appeal.

[0074] The proposal unit can make suggestions that take into account the balance between menu items based on the analysis results. For example, the proposal unit can make suggestions to improve menu items based on the analysis results. The proposal unit can also make suggestions for new products, for example. The proposal unit can also make suggestions to optimize menu composition and ingredient selection, for example. This makes it possible to optimize menu composition by making suggestions that take into account the balance between menu items. The balance between menu items includes, but is not limited to, nutritional balance and price balance. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input the analysis results into a generative AI, and the generative AI can make suggestions that take into account the balance between menu items.

[0075] The cost management department can provide upgrade or reduction measures based on the proposed content, including the cost of ingredients and the supply chain. The cost management department can, for example, provide cost reduction measures based on the proposed content. The cost management department can also, for example, optimize the supply chain. The cost management department can also, for example, propose changes to ingredients. This enables cost optimization by providing upgrade or reduction measures based on the cost of ingredients and the supply chain. Upgrade and reduction measures include, but are not limited to, changes to ingredients and improvements to cooking methods. Some or all of the above processes in the cost management department may be performed, for example, using a generative AI, or not using a generative AI. For example, the cost management department can input the proposed content into a generative AI, and the generative AI can provide upgrade or reduction measures based on the cost of ingredients and the supply chain.

[0076] The data collection unit can estimate the user's emotions and determine the priority of opinions to collect based on the estimated emotions. For example, if the user expresses positive emotions, the data collection unit will prioritize collecting those opinions. For example, if the user expresses negative emotions, the data collection unit can also collect those opinions in detail and identify the issues. For example, if the user expresses neutral emotions, the data collection unit can collect those opinions by comparing them with other opinions. This allows for the efficient collection of more important opinions by prioritizing opinions based on the user's emotions. User emotions include, but are not limited to, sentiment analysis algorithms and survey results. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input the user's emotions into a generative AI, which can estimate the emotions and determine the priority of opinions.

[0077] The data collection unit can analyze the user's past review history during data collection and select the optimal collection method. For example, the data collection unit may prioritize collecting opinions on menus that the user has previously given high ratings to. The data collection unit may also collect detailed opinions on menus that the user has previously given low ratings to. The data collection unit may also concentrate data collection during times when the user frequently posted reviews in the past. This allows the optimal collection method to be selected by analyzing the user's past review history. The optimal collection method includes, but is not limited to, the timing and means of data collection. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit may input the user's past review history into a generative AI, which can then select the optimal collection method.

[0078] The data collection unit can prioritize collecting opinions from specific time periods or event periods. For example, it can prioritize collecting opinions posted during specific time periods such as lunchtime or dinnertime. The data collection unit can also prioritize collecting opinions posted during event periods such as Christmas or Valentine's Day. The data collection unit can also prioritize collecting opinions posted when a new menu is announced. This allows for the efficient collection of important opinions by prioritizing opinions from specific time periods or event periods. Specific time periods or event periods include, but are not limited to, peak times or sales periods. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input opinions from specific time periods or event periods into a generative AI, which can then determine the priority of the opinions.

[0079] The collection unit can estimate the user's emotions and determine the priority of photos to collect based on the estimated emotions. For example, if the user is showing positive emotions, the collection unit will prioritize collecting those photos. For example, if the user is showing negative emotions, the collection unit can also collect those photos in detail and identify the problems. For example, if the user is showing neutral emotions, the collection unit can collect those photos by comparing them with other photos. This allows for the efficient collection of more important photos by prioritizing photos based on the user's emotions. Photo prioritization includes, but is not limited to, the results of emotion analysis and the content of the photos. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can input the user's emotions into a generative AI, which can estimate the emotions and determine the priority of photos.

[0080] The data collection unit can prioritize collecting highly relevant opinions by considering the user's geographical location information during the collection process. For example, if the user is near a restaurant, the data collection unit will prioritize collecting opinions related to that restaurant. For example, if the user is in a specific region, the data collection unit can also prioritize collecting opinions related to that region. For example, if the user is traveling, the data collection unit can also prioritize collecting opinions related to their travel destination. This allows for the efficient collection of highly relevant opinions by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and regional tags. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize collecting highly relevant opinions.

[0081] The data collection unit can analyze a user's social media activity and collect relevant opinions during the collection process. For example, if a user uses a specific hashtag, the data collection unit can prioritize collecting opinions related to that hashtag. The data collection unit can also prioritize collecting opinions related to a specific group or community if the user belongs to one. The data collection unit can also prioritize collecting opinions related to an event if the user participates in one. This allows for the efficient collection of relevant opinions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posting frequency and follower count. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input the user's social media activity into a generative AI, which can then collect relevant opinions.

[0082] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can improve the accuracy of the analysis if the user is showing positive emotions. For example, the analysis unit can also perform a more detailed analysis if the user is showing negative emotions. For example, the analysis unit can compare the user's emotions with other opinions for analysis if the user is showing neutral emotions. This allows for a more accurate analysis by adjusting the accuracy of the analysis based on the user's emotions. Accuracy of the analysis includes, but is not limited to, adjusting algorithm parameters and filtering data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the accuracy of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions during the analysis. For example, the analysis unit can perform a detailed analysis on highly rated opinions. The analysis unit can also perform a detailed analysis on low-rated opinions. For example, the analysis unit can analyze neutral opinions in comparison with other opinions. This allows important opinions to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the opinions. The importance of an opinion includes, but is not limited to, the content of the opinion and the influence of the submitter. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of opinions into a generative AI, and the generative AI can adjust the level of detail of the analysis based on the importance of the opinions.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the opinion during analysis. For example, the analysis unit can apply a specific analysis algorithm to opinions about taste. The analysis unit can also apply a specific analysis algorithm to opinions about appearance. The analysis unit can also apply a specific analysis algorithm to opinions about service. By applying different analysis algorithms depending on the category of the opinion, more accurate analysis becomes possible. The categories of opinions include, for example, positive, negative, and neutral, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of opinions into a generative AI, and the generative AI can apply different analysis algorithms depending on the category of the opinion.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is showing positive emotions, the analysis unit can display the analysis results in bright colors. For example, if the user is showing negative emotions, the analysis unit can also display the analysis results in calm colors. For example, if the user is showing neutral emotions, the analysis unit can also display the analysis results in standard colors. This allows for a more appropriate display by adjusting the display method of the analysis results based on the user's emotions. The display method of the analysis results includes, but is not limited to, graph displays and text displays. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's emotions into a generative AI, which can estimate the emotions and adjust the display method of the analysis results.

[0086] The analysis unit can determine the priority of analysis based on when the opinions were submitted. For example, the analysis unit may prioritize the analysis of the most recent opinions. The analysis unit may also prioritize the analysis of opinions submitted during a specific event period. The analysis unit may also prioritize the analysis of current opinions while referring to past opinions. This allows for the prioritization of the analysis of the most recent opinions by determining the priority of analysis based on when the opinions were submitted. The submission period of opinions includes, but is not limited to, the submission date and time or the event period. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit can input the submission period of opinions into a generating AI, and the generating AI can determine the priority of analysis based on the submission period of the opinions.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the opinions during the analysis. For example, the analysis unit may prioritize the analysis of highly rated opinions. The analysis unit may also prioritize the analysis of low-rated opinions. The analysis unit may also analyze neutral opinions in comparison with other opinions. This allows important opinions to be prioritized by adjusting the order of analysis based on the relevance of the opinions. The relevance of opinions includes, but is not limited to, similarity of content or relationship between submitters. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of opinions into a generative AI, which can then adjust the order of analysis based on the relevance of the opinions.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on those emotions. For example, if the user is expressing positive emotions, the suggestion unit will make suggestions in a cheerful tone. If the user is expressing negative emotions, the suggestion unit may make suggestions in a calm tone. If the user is expressing neutral emotions, the suggestion unit may make suggestions in a standard tone. By adjusting the way the suggestion is expressed based on the user's emotions, more appropriate suggestions can be made. The way the suggestion is expressed includes, but is not limited to, the wording and format of the suggestion. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's emotions into a generative AI, which can estimate the emotions and adjust the way the suggestion is expressed.

[0089] The suggestion department can adjust the level of detail in its suggestions based on the importance of the menu items. For example, the suggestion department can provide detailed suggestions for popular menu items. It can also provide detailed suggestions for new menu items. It can also provide detailed suggestions for seasonal menu items. By adjusting the level of detail in suggestions based on the importance of the menu items, it becomes possible to provide detailed suggestions for important menu items. Menu importance includes, but is not limited to, sales data and customer ratings. Some or all of the above processing in the suggestion department may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion department can input the importance of the menu items into a generative AI, which can then adjust the level of detail in its suggestions based on the importance of the menu items.

[0090] The suggestion unit can apply different suggestion algorithms depending on the menu category when making suggestions. For example, the suggestion unit can apply a specific suggestion algorithm to a dessert menu. The suggestion unit can also apply a specific suggestion algorithm to a main dish menu. The suggestion unit can also apply a specific suggestion algorithm to a drink menu menu. By applying different suggestion algorithms depending on the menu category, more appropriate suggestions become possible. Menu categories include, but are not limited to, appetizers, main dishes, and desserts. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the menu category into a generative AI, which can then apply a different suggestion algorithm depending on the menu category.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is showing positive emotions, the suggestion unit can provide a detailed suggestion. For example, if the user is showing negative emotions, the suggestion unit can provide a concise suggestion. For example, if the user is showing neutral emotions, the suggestion unit can provide a suggestion of standard length. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotions. The length of the suggestion may include, but is not limited to, the level of detail in the suggestion and the user's level of interest. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's emotions into a generative AI, which can estimate the emotions and adjust the length of the suggestion.

[0092] The proposal department can determine the priority of proposals based on the submission timing of the menus. For example, the proposal department may prioritize proposals for the latest menus. The proposal department may also prioritize proposals for seasonal menus. The proposal department may also prioritize proposals for current menus, while referring to past menus. This allows for prioritizing proposals for the latest menus by determining the priority of proposals based on the submission timing of the menus. The submission timing of the menus includes, but is not limited to, the submission date and time or the event period. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the menu submission timing into a generative AI, which can then determine the priority of proposals based on the submission timing.

[0093] The suggestion unit can adjust the order of suggestions based on the relevance of the menu items. For example, the suggestion unit may prioritize suggesting popular menu items. The suggestion unit may also prioritize suggesting new menu items. The suggestion unit may also prioritize suggesting seasonal menu items. By adjusting the order of suggestions based on the relevance of the menu items, it becomes possible to prioritize suggesting important menu items. Menu relevance includes, but is not limited to, similarity of content and customer preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the menu relevance into a generative AI, which can then adjust the order of suggestions based on the relevance.

[0094] The cost management department can estimate user emotions and adjust cost management methods based on those estimated emotions. For example, if a user expresses positive emotions, the cost management department can propose methods to maintain quality while keeping costs down. If a user expresses negative emotions, the cost management department can also propose cost management methods that prioritize quality. If a user expresses neutral emotions, the cost management department can also propose balanced cost management methods. By adjusting cost management methods based on user emotions, more appropriate cost management becomes possible. Cost management methods include, but are not limited to, cost reduction measures and supply chain optimization. Some or all of the above processes in the cost management department may be performed using, for example, generative AI, or not using generative AI. For example, the cost management department can input user emotions into generative AI, which can estimate emotions and adjust cost management methods.

[0095] The cost management department can monitor fluctuations in ingredient prices in real time during cost management and select the optimal cost management method. For example, if ingredient prices rise sharply, the cost management department can suggest alternative ingredients. For example, if ingredient prices fall, the cost management department can suggest more expensive ingredients to improve quality. For example, if ingredient prices are stable, the cost management department can suggest ways to maintain quality while keeping costs down. This enables optimal cost management by monitoring ingredient price fluctuations in real time. Ingredient price fluctuations include, but are not limited to, real-time data and price prediction models. Some or all of the above processes in the cost management department may be performed using, for example, generative AI, or not. For example, the cost management department can input ingredient price fluctuations into a generative AI, which can then select the optimal cost management method based on the price fluctuations.

[0096] The cost management department can customize its cost management methods when managing costs, taking into account the stability of the supply chain. For example, if the supply chain is stable, the cost management department can propose methods to maintain quality while keeping costs down. If the supply chain is unstable, the cost management department can also propose alternative suppliers. If the supply chain is fluctuating, the cost management department can also propose diversification of suppliers. This allows for more appropriate cost management by considering the stability of the supply chain. Supply chain stability includes, but is not limited to, the reliability of suppliers and fluctuations in supply quantities. Some or all of the above processes in the cost management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the cost management department can input the stability of the supply chain into a generative AI, and the generative AI can customize the cost management methods based on that stability.

[0097] The cost management department can estimate user emotions and determine cost management priorities based on those estimated emotions. For example, if a user expresses positive emotions, the cost management department might prioritize cost reduction. If a user expresses negative emotions, the cost management department might prioritize quality improvement. If a user expresses neutral emotions, the cost management department might pursue balanced cost management. This allows for more appropriate cost management by determining cost management priorities based on user emotions. Cost management priorities include, but are not limited to, cost reduction effects and supply risks. Some or all of the above processes in the cost management department may be performed using, for example, generative AI, or not. For example, the cost management department can input user emotions into a generative AI, which can then estimate the emotions and determine cost management priorities.

[0098] The cost management department can select the optimal cost management method when managing costs, taking into account the geographical distribution of ingredients. For example, the cost management department can reduce transportation costs by prioritizing the use of local ingredients. For example, when using imported ingredients, the cost management department can also select the optimal supplier, taking into account transportation costs. For example, the cost management department can propose the optimal cost management method by taking into account the geographical distribution of ingredients each season. This makes it possible to manage costs more appropriately by taking into account the geographical distribution of ingredients. The geographical distribution of ingredients includes, but is not limited to, information on the place of origin and transportation costs. Some or all of the above processes in the cost management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the cost management department can input the geographical distribution of ingredients into a generative AI, and the generative AI can select the optimal cost management method based on the geographical distribution.

[0099] The cost management department can improve the accuracy of cost management by referring to relevant literature on ingredients during cost management. For example, the cost management department can refer to literature on ingredient price fluctuations to propose the optimal cost management method. For example, the cost management department can refer to literature on ingredient quality to propose methods for reducing costs while maintaining quality. For example, the cost management department can refer to literature on ingredient supply chains to propose diversification of suppliers. In this way, the accuracy of cost management is improved by referring to relevant literature on ingredients. Relevant literature on ingredients includes, but is not limited to, academic papers and industry reports. Some or all of the above processes in the cost management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the cost management department can input relevant literature on ingredients into a generative AI, and the generative AI can improve the accuracy of cost management based on the literature.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can estimate the user's emotions and determine the priority of opinions to collect based on those estimated emotions. For example, if a user expresses positive emotions, those opinions can be collected preferentially. If a user expresses negative emotions, those opinions can be collected in detail to identify problems. Furthermore, if a user expresses neutral emotions, those opinions can be collected in comparison to other opinions. This allows for the efficient collection of more important opinions by prioritizing them based on the user's emotions.

[0102] The analysis unit can analyze collected opinions and extract characteristics of menus that receive high customer satisfaction ratings. For example, it can use text mining techniques to analyze collected opinions. It can also use sentiment analysis techniques to analyze the emotions behind the collected opinions. Furthermore, it can use statistical analysis techniques to analyze trends in the collected opinions. This helps in improving menus by extracting characteristics of menus that receive high customer satisfaction ratings.

[0103] The proposal department can make suggestions that consider the balance between menu items based on the analysis results. For example, it can make suggestions to improve menu items based on the analysis results. It can also propose new products. Furthermore, it can make suggestions to optimize the menu composition and ingredient selection. This makes it possible to optimize the menu composition by making suggestions that consider the balance between menu items.

[0104] The cost management department can provide upgrade or reduction measures based on the proposed content, including cost analysis of ingredients and supply chains. For example, it can provide cost reduction measures based on the proposed content. It can also optimize the supply chain. Furthermore, it can propose changes to ingredients. By providing upgrade or reduction measures based on ingredient costs and supply chains, cost optimization becomes possible.

[0105] The collection unit can estimate the user's emotions and determine the priority of photos to collect based on those estimated emotions. For example, if the user expresses positive emotions, those photos can be prioritized for collection. If the user expresses negative emotions, those photos can be collected in detail to identify problems. Furthermore, if the user expresses neutral emotions, those photos can be collected in comparison to other photos. This allows for the efficient collection of more important photos by prioritizing photos based on the user's emotions.

[0106] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user shows positive emotions, the accuracy of the analysis can be improved. If the user shows negative emotions, a more detailed analysis can be performed. Furthermore, if the user shows neutral emotions, the analysis can be performed in comparison with other opinions. In this way, by adjusting the accuracy of the analysis based on the user's emotions, a more accurate analysis becomes possible.

[0107] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is showing positive emotions, the proposal can be presented in a cheerful tone. If the user is showing negative emotions, the proposal can be presented in a calm tone. Furthermore, if the user is showing neutral emotions, the proposal can be presented in a standard tone. By adjusting the presentation of proposals based on the user's emotions, more appropriate suggestions can be made.

[0108] The cost management department can estimate user sentiment and adjust cost management methods based on that estimation. For example, if a user expresses positive sentiment, it can propose methods to maintain quality while keeping costs down. If a user expresses negative sentiment, it can propose cost management methods that prioritize quality. Furthermore, if a user expresses neutral sentiment, it can propose balanced cost management methods. By adjusting cost management methods based on user sentiment, more appropriate cost management becomes possible.

[0109] The cost management department can monitor fluctuations in ingredient prices in real time during cost management and select the optimal cost management method. For example, if ingredient prices rise sharply, they can propose alternative ingredients. If ingredient prices fall, they can propose more expensive ingredients to improve quality. Furthermore, if ingredient prices are stable, they can propose methods to maintain quality while keeping costs down. In this way, optimal cost management becomes possible by monitoring fluctuations in ingredient prices in real time.

[0110] The cost management department can customize its cost management methods when conducting cost management, taking into account the stability of the supply chain. For example, if the supply chain is stable, it can propose methods to maintain quality while keeping costs down. If the supply chain is unstable, it can propose alternative suppliers. Furthermore, if the supply chain is fluctuating, it can propose diversification of suppliers. By considering the stability of the supply chain, more appropriate cost management becomes possible.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit gathers consumer opinions. For example, it collects consumer opinions from review sites and gathers text data posted on those sites. The data collection unit can also collect data from specific review sites using APIs. Step 2: The analysis unit analyzes the opinions collected by the collection unit. For example, it may use text mining techniques to analyze the collected opinions and extract characteristics of customer satisfaction and highly-rated menu items. It may also use sentiment analysis techniques and statistical analysis techniques to analyze the sentiment and trends of the collected opinions. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. For example, they may make suggestions for menu improvements or new products based on the analysis results. They can also make suggestions to optimize menu composition or ingredient selection. Step 4: The Cost Management Department provides upgrade or reduction measures based on the cost of ingredients and the supply chain, based on the proposals made by the Proposal Department. For example, they may provide cost reduction measures based on the proposals and suggest optimization of the supply chain or changes to ingredients.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and cost management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects consumer opinions from review sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes suggestions for menu improvements based on the analysis results. The cost management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides upgrade or reduction measures based on the cost of ingredients and the supply chain based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and cost management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects consumer opinions from review sites. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes suggestions for menu improvements based on the analysis results. The cost management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides upgrade or reduction measures based on the cost of ingredients and the supply chain based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and cost management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects consumer opinions from review sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes suggestions for menu improvements based on the analysis results. The cost management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides upgrade or reduction measures based on the cost of ingredients and the supply chain based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and cost management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects consumer opinions from review sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes suggestions for menu improvements based on the analysis results. The cost management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides upgrade or reduction measures based on the cost of ingredients and the supply chain based on the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A department that collects consumer opinions, An analysis unit analyzes the opinions collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, The Cost Management Department provides cost-saving measures for ingredients and supply chains based on the proposals made by the aforementioned Proposal Department. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect consumer opinions from review sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected opinions are analyzed to extract characteristics of menu items that have high customer satisfaction and high ratings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect photos posted on social media. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected photos are analyzed to identify elements that enhance their visual appeal, such as "Instagrammable" or "cute." The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the analysis results, we will make suggestions that take into account the balance between menu items. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned cost control department, Based on the proposed content, we will provide upgrade and reduction measures based on the cost of ingredients and the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates user sentiment and determines the priority of opinions to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system analyzes the user's past review history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting feedback, prioritize opinions gathered during specific time periods or event durations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of photos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant opinions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, we analyze users' social media activity and gather relevant opinions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of each opinion. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the opinion. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the opinions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the order of analysis will be adjusted based on the relevance of the opinions. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the menu items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the menu category. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the menus were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the menu items. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned cost control department, We estimate user sentiment and adjust cost management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned cost control department, During cost management, we monitor fluctuations in ingredient prices in real time and select the optimal cost management method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned cost control department, When managing costs, customize cost management methods to take into account the stability of the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned cost control department, Estimate user sentiment and determine cost management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned cost control department, When managing costs, the optimal cost management method is selected by considering the geographical distribution of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned cost control department, When managing costs, refer to relevant literature on ingredients to improve the accuracy of cost management. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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 department that collects consumer opinions, An analysis unit analyzes the opinions collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, The Cost Management Department provides cost-saving measures for ingredients and supply chains based on the proposals made by the aforementioned Proposal Department. A system characterized by the following features.

2. The aforementioned collection unit is Collect consumer opinions from review sites. The system according to feature 1.

3. The aforementioned analysis unit, The collected opinions are analyzed to extract characteristics of menu items that have high customer satisfaction and high ratings. The system according to feature 1.

4. The aforementioned collection unit is Collect photos posted on social media. The system according to feature 1.

5. The aforementioned analysis unit, We analyze the collected photographs to identify elements that enhance their visual appeal. The system according to feature 1.

6. The aforementioned proposal section is, Based on the analysis results, we will make suggestions that take into account the balance between menu items. The system according to feature 1.

7. The aforementioned cost control department, Based on the proposed content, we will provide upgrade and reduction measures based on the cost of ingredients and the supply chain. The system according to feature 1.

8. The aforementioned collection unit is It estimates user sentiment and determines the priority of opinions to collect based on the estimated user sentiment. The system according to feature 1.

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