system

Abstract

The system according to this embodiment aims to analyze a customer's past dining history and real-time reaction data to suggest the most suitable dishes and beverages. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects the customer's past meal history and real-time reaction data. The analysis unit analyzes the data collected by the collection unit and learns the customer's preferences. The suggestion unit suggests the optimal dishes and drinks based on the results learned by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to fully utilize the customer's past meal history and real-time reaction data to propose personalized dishes and drinks, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the customer's past meal history and real-time reaction data and propose optimal dishes and drinks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects the customer's past meal history and real-time reaction data. The analysis unit analyzes the data collected by the data collection unit and learns the customer's preferences. The suggestion unit suggests the optimal dishes and beverages based on the results learned by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a customer's past dining history and real-time reaction data to suggest the most suitable dishes and beverages. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a service system specifically for repeat customers of restaurants that utilizes past dining history and real-time reaction data to offer highly personalized food and beverage recommendations. This system learns from each dining experience and, upon a return visit, provides even more optimized food recommendations based on the learned results. By recording customer preferences and dining history and reflecting this information in future recommendations, it is possible to significantly improve repeat customer rates and customer satisfaction. For example, when a customer visits a restaurant, past dining history and real-time reaction data are collected. For instance, the system records which dishes the customer ordered, which drinks they chose, and their reactions at the time (e.g., satisfaction level, changes in preferences, etc.). This information is input into an AI agent. Next, the AI ​​agent analyzes the collected data and learns the customer's preferences and dining history. For example, if a customer tends to prefer certain dishes, the system optimizes future recommendations based on this information. Furthermore, by analyzing real-time reaction data, it is possible to understand changes in customer preferences and new preferences. In addition, upon a return visit, the AI ​​agent provides optimized food and beverage recommendations based on the learned results. For example, the system can suggest new dishes based on the customer's favorites from their previous visit. It can also suggest drinks tailored to the customer's preferences. This allows customers to enjoy a different, more personalized dining experience each time. This system significantly improves repeat customer rates and customer satisfaction. Customers feel more special when they receive suggestions for food and drinks tailored to their preferences. Restaurants can also encourage repeat visits by increasing the satisfaction of their repeat customers. For example, if a customer really liked a particular dish on their last visit, the system can suggest new dishes related to that dish on their next visit. It can also suggest dishes that pair well with a customer's preferred drink. This allows customers to experience new surprises and satisfaction each time. In this way, by utilizing AI agents, restaurants can provide highly personalized dining experiences to their repeat customers. This can significantly improve repeat customer rates and customer satisfaction.This allows service systems specifically tailored to repeat customers of restaurants to offer highly personalized food and beverage recommendations based on customer preferences and dining history.

[0029] The service system for repeat customers of a restaurant according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects the customer's past dining history and real-time reaction data. The collection unit records, for example, which dishes the customer ordered and which drinks they chose. The collection unit records, for example, changes in the customer's satisfaction level and preferences in real time. The collection unit can also estimate the customer's emotions and adjust the timing of collecting dining history based on the estimated emotions. The collection unit can also analyze the customer's past dining history and select the optimal collection method. The collection unit can also filter the dining history based on the customer's current health status and allergy information when collecting it. The collection unit can also estimate the customer's emotions and determine the priority of the dining history to collect based on the estimated emotions. The collection unit can also prioritize the collection of highly relevant history based on the customer's geographical location information when collecting dining history. The collection unit can also analyze the customer's social media activity and collect relevant history when collecting dining history. The analysis unit learns the customer's preferences based on the collected data. The analysis unit can, for example, estimate customer emotions and adjust the presentation of the analysis based on the estimated customer emotions. The analysis unit can also, for example, adjust the level of detail of the analysis based on the importance of the meal history during analysis. The analysis unit can also, for example, apply different analysis algorithms depending on the category of the meal during analysis. The analysis unit can, for example, estimate customer emotions and adjust the length of the analysis based on the estimated customer emotions. The analysis unit can, for example, determine the priority of the analysis based on when the meal history was submitted during analysis. The analysis unit can also, for example, adjust the order of the analysis based on the relevance of the meal history during analysis. The suggestion unit suggests the best dishes and drinks for the next visit based on the learning results. The suggestion unit suggests new dishes and drinks tailored to the customer's preferences. The suggestion unit can, for example, estimate customer emotions and adjust the presentation of the suggestion based on the estimated customer emotions. The suggestion unit can also, for example, adjust the level of detail of the suggestion based on the importance of the dishes and drinks during suggestion.The suggestion unit can, for example, apply different suggestion algorithms depending on the food and beverage category when making suggestions. The suggestion unit can, for example, estimate the customer's emotions and adjust the length of the suggestions based on the estimated customer emotions. The suggestion unit can, for example, determine the priority of suggestions based on when the food and beverages are served when making suggestions. The suggestion unit can, for example, adjust the order of suggestions based on the relevance of the food and beverages when making suggestions. As a result, the service system tailored to repeat customers of a restaurant according to the embodiment can leverage the customer's past dining history and real-time reaction data to provide personalized food and beverage suggestions.

[0030] The data collection unit gathers customers' past dining history and real-time reaction data. Specifically, it meticulously records which dishes customers order and which drinks they choose. For example, it records the type and quantity of food ordered, the time of ordering, the type of drink, and whether or not alcohol was consumed. In addition, to record changes in customer satisfaction and preferences in real time, it monitors customers' facial expressions and behavior using cameras and sensors to estimate their emotions. This allows the system to understand whether customers are enjoying their meals and which dishes they are particularly satisfied with. Furthermore, it is possible to adjust the timing of collecting dining history based on the customer's emotions. For example, if a customer is particularly satisfied while eating a certain dish, detailed data about that dish will be prioritized for collection. It is also possible to filter the data collected based on the customer's current health status and allergy information. This enables data collection that takes the customer's health into consideration. Furthermore, it is possible to prioritize the collection of highly relevant history based on the customer's geographical location. For example, by prioritizing the collection of data on restaurants that customers frequently visit in a particular area, it becomes possible to make recommendations tailored to that area. It is also possible to analyze customers' social media activity and collect relevant history. This allows the system to understand customers' online preferences and trends, enabling more personalized recommendations.

[0031] The analytics unit learns customer preferences based on collected data. Specifically, it analyzes customers' past dining history and real-time reaction data to understand their preferences and trends. For example, if a customer frequently orders a particular dish or drink, it determines that the dish or drink is a customer preference. It can also estimate customer emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a customer is particularly satisfied when eating a particular dish, it will analyze the data related to that dish in detail. Furthermore, it is possible to adjust the level of detail of the analysis based on the importance of the dining history. For example, it will analyze the data related to dishes and drinks that customers frequently order in detail to understand customer preferences more accurately. It can also apply different analysis algorithms depending on the category of the meal. For example, it can select the appropriate analysis algorithm for different categories of dishes, such as desserts and main dishes. Furthermore, it is possible to estimate customer emotions and adjust the length of the analysis based on the estimated emotions. For example, if a customer is particularly satisfied with a particular dish, it will analyze the data related to that dish in detail. It can also prioritize the analysis based on when the dining history was submitted. For example, it can prioritize the analysis of recent dining history to understand the latest preferences. This allows the analysis unit to accurately learn customer preferences and provide the foundational data needed to make optimal suggestions during subsequent visits.

[0032] The suggestion system proposes the most suitable dishes and drinks for the next visit based on the learning results. Specifically, it suggests new dishes and drinks tailored to the customer's preferences. For example, it suggests similar new menu items based on dishes and drinks the customer has previously ordered. It can also estimate the customer's emotions and adjust the way suggestions are presented based on those emotions. For example, if the customer is particularly satisfied with a certain dish, it will provide detailed suggestions about that dish. Furthermore, it can adjust the level of detail in suggestions based on the importance of the dishes and drinks. For example, it can provide detailed suggestions about dishes and drinks that the customer particularly likes to pique their interest. It can also apply different suggestion algorithms depending on the category of dishes and drinks. For example, it can select the appropriate suggestion algorithm for different categories of dishes, such as desserts and main courses. Furthermore, it can estimate the customer's emotions and adjust the length of suggestions based on those emotions. For example, if the customer is particularly satisfied with a certain dish, it will provide detailed suggestions about that dish. It can also prioritize suggestions based on when the dishes and drinks are presented. For example, it can prioritize suggestions based on recent dining history to address the customer's latest preferences. This allows the proposal department to suggest the most suitable dishes and drinks tailored to the customer's preferences, thereby improving customer satisfaction.

[0033] The data collection unit can record which dishes a customer orders and which drinks they choose. For example, the data collection unit can store the customer's order and drink choices as digital data. The data collection unit can also record the customer's order and drink choices on paper. The data collection unit can also record the customer's order and drink choices via voice input. This allows for recording the customer's order history, which can then be used to make future recommendations. Specific methods and criteria for recording include, but are not limited to, saving as digital data or recording on paper. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the customer's order history into AI, which can then analyze the order history and store it in a database.

[0034] The data collection unit can record changes in customer satisfaction and preferences in real time. For example, the data collection unit can record customer satisfaction through questionnaire surveys. For example, the data collection unit can also record customer satisfaction through facial expression analysis. For example, the data collection unit can also record customer satisfaction through voice analysis. For example, the data collection unit can record changes in customer preferences by comparing them with past selection history. For example, the data collection unit can also record changes in customer preferences by analyzing real-time reaction data. For example, the data collection unit can also record changes in customer preferences using sensor data. This allows for more accurate suggestions by recording customer satisfaction and changes in preferences in real time. Specific evaluation criteria and measurement methods for satisfaction include, but are not limited to, questionnaire surveys, facial expression analysis, and voice analysis. Specific content and measurement methods for changes in preferences include, but are not limited to, comparisons with past selection history and analysis of real-time reaction data. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input customer satisfaction levels and changes in preferences into the AI, which can then analyze these changes and store them in a database.

[0035] The analysis unit can learn customer preferences based on collected data. For example, the analysis unit can learn the types of food a customer likes based on collected data. The analysis unit can also learn the ingredients a customer dislikes based on collected data. The analysis unit can also learn the customer's past selection history based on collected data. This allows the system to learn customer preferences based on collected data and reflect them in future recommendations. The specific content and learning methods of preferences include, but are not limited to, the types of food a customer likes, the ingredients they dislike, and their past selection history. Some or all of the above-described processes 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 collected data into a generative AI, which can learn customer preferences and store them in a database.

[0036] The suggestion unit can suggest the most suitable dishes and drinks for the next visit based on the learning results. For example, the suggestion unit can suggest new dishes based on the dishes the customer liked on their previous visit. The suggestion unit can also suggest drinks that suit the customer's preferences. The suggestion unit can also suggest dishes that suit the customer's health condition. This improves customer satisfaction by suggesting the most suitable dishes and drinks for the next visit based on the learning results. Specific criteria and selection methods for the most suitable dishes and drinks include, but are not limited to, nutritional balance, customer preferences, and health condition. 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 learning results into a generative AI, which can then suggest the most suitable dishes and drinks.

[0037] The suggestion department can propose new dishes and drinks tailored to customer preferences. For example, the suggestion department can propose new dishes based on the customer's preferred food trends. The suggestion department can also propose new seasonal menu items. The suggestion department can also propose new drinks tailored to customer preferences. This improves customer repeat rates by proposing new dishes and drinks tailored to customer preferences. The specific content and methods of proposing new dishes and drinks include, but are not limited to, seasonal menus and new products. 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 customer preference data into a generative AI, which can then propose new dishes and drinks.

[0038] The data collection unit can analyze a customer's past dining history and select the optimal data collection method. For example, the data collection unit may prioritize collecting dishes that the customer has frequently ordered in the past. The data collection unit may also analyze the customer's past preferred dishes and collect a history of similar dishes. The data collection unit may also exclude dishes that the customer has avoided in the past. This allows the optimal data collection method to be selected by analyzing the customer's past dining history. Specific criteria and selection methods for the optimal data collection method include, but are not limited to, saving as digital data or recording on paper. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's past dining history into a generative AI, which can then select the optimal data collection method.

[0039] The collection unit can filter the collected meal history based on the customer's current health status and allergy information. For example, the collection unit can exclude dishes containing ingredients the customer is allergic to. For example, the collection unit can prioritize the collection of low-calorie dishes depending on the customer's health status. For example, the collection unit can collect nutritionally balanced dishes depending on the customer's health status. This allows for safer recommendations by filtering based on the customer's health status and allergy information. Specific details and evaluation methods of health status include, but are not limited to, medical data, self-reporting, and sensor data. Specific details and collection methods of allergy information include, but are not limited to, medical data and self-reporting. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the collection unit can input the customer's health status and allergy information into a generating AI, which can then perform the filtering.

[0040] The data collection unit can prioritize collecting highly relevant history based on the customer's geographical location information when collecting meal history. For example, the data collection unit can prioritize collecting the history of restaurants that the customer frequently visits in a particular area. For example, if the customer is traveling, the data collection unit can also prioritize collecting meal history in the area of ​​their travel destination. For example, the data collection unit can also prioritize collecting meal history at restaurants near the customer's home. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant history based on the customer's geographical location information. The specific content and collection methods of geographical location information include, but are not limited to, GPS data and address information. The specific criteria and selection methods for highly relevant history include, but are not limited to, past selection history and current location information. Some or all of the above processing 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 customer's geographical location information into a generative AI, which can then prioritize the collection of highly relevant history.

[0041] The data collection unit can analyze the customer's social media activity and collect relevant history when collecting meal history. For example, the data collection unit can collect the history of dishes the customer has shared on social media. The data collection unit can also collect the history of dishes the customer has "liked" on social media. The data collection unit can also collect the history of dishes from restaurants the customer follows on social media. This allows for the collection of relevant history by analyzing the customer's social media activity. The specific content and analysis methods of social media activity include, but are not limited to, posts, the number of likes, and comments. Some or all of the above processing 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 customer's social media activity data into a generative AI, which can then collect relevant history.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the meal history during the analysis. For example, the analysis unit can perform a detailed analysis of dishes that the customer frequently orders. The analysis unit can also simplify the analysis of dishes that the customer has ordered only once. The analysis unit can also perform a detailed analysis of dishes that the customer particularly likes. This allows for a more detailed analysis of important data by adjusting the level of detail based on the importance of the meal history. Specific evaluation criteria and measurement methods for importance include, but are not limited to, customer preferences, health status, and past selection history. Specific adjustment methods and criteria for level of detail include, but are not limited to, data granularity and the level of detail displayed. 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 meal history data into a generative AI, which can then adjust the level of detail of the analysis based on importance.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the meal during analysis. For example, the analysis unit can apply different analysis algorithms to the main dish and dessert. For example, the analysis unit can apply different analysis algorithms to drinks and dishes. For example, the analysis unit can apply different analysis algorithms to appetizers and main dishes. By applying different analysis algorithms depending on the category of the meal, more appropriate analysis results can be provided. The specific content and classification methods of meal categories include, but are not limited to, Japanese food, Western food, Chinese food, etc. The specific types and application methods of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms, etc. 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 meal category data into a generative AI, and the generative AI can apply different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the submission timing of meal history data. For example, the analysis unit may prioritize the analysis of recent meal history. The analysis unit may also prioritize the analysis of meal history data from specific events. The analysis unit may also prioritize the analysis of seasonal meal history data. By prioritizing analysis based on the submission timing of meal history data, more important data can be analyzed preferentially. Specific details and evaluation methods for submission timing include, but are not limited to, the date and time of the meal and the timing of data submission. Specific criteria and methods for determining priority include, but are not limited to, importance, urgency, and customer preferences. 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 meal history data into a generative AI, which can then determine the priority of analysis based on the submission timing.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the meal history during the analysis. For example, the analysis unit may prioritize the analysis of the history of dishes the customer preferred. The analysis unit may also postpone the analysis of the history of dishes the customer avoided. The analysis unit may also prioritize the analysis of the history of dishes related to the customer's preferences. By adjusting the order of analysis based on the relevance of the meal history, more important data can be analyzed preferentially. Specific evaluation criteria and measurement methods for relevance include, but are not limited to, past selection history and current circumstances. Specific methods and criteria for adjusting the order include, but are not limited to, importance, relevance, and customer preferences. 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 meal history data into a generative AI, which can then adjust the order of analysis based on relevance.

[0046] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the food and beverages. For example, the suggestion unit can provide detailed suggestions for dishes the customer liked. The suggestion unit can also simplify suggestions for dishes the customer avoided. The suggestion unit can also provide detailed suggestions for beverages the customer particularly liked. This allows for more detailed suggestions by adjusting the level of detail based on the importance of the food and beverages. Specific evaluation criteria and measurement methods for importance include, but are not limited to, customer preferences, health status, and past selection history. Specific adjustment methods and criteria for level of detail include, but are not limited to, data granularity and the level of detail displayed. 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 food and beverage data into a generative AI, which can then adjust the level of detail of the suggestions based on importance.

[0047] The suggestion unit can apply different suggestion algorithms depending on the food and beverage categories when making suggestions. For example, the suggestion unit can apply different suggestion algorithms for main dishes and desserts. It can also apply different suggestion algorithms for beverages and dishes. It can also apply different suggestion algorithms for appetizers and main dishes. By applying different suggestion algorithms depending on the food and beverage categories, more appropriate suggestions can be provided. The specific content and classification methods of food and beverage categories include, but are not limited to, Japanese food, Western food, Chinese food, etc. The specific types and application methods of suggestion algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms, etc. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input food and beverage category data into a generative AI, and the generative AI can apply different suggestion algorithms.

[0048] The proposal department can prioritize proposals based on the timing of food and beverage submissions. For example, the proposal department may make proposals based on recent meal history. The proposal department may also make proposals based on meal history during specific events. The proposal department may also make proposals based on seasonal meal history. This allows for prioritizing more important proposals by determining the timing of food and beverage submissions. Specific details and evaluation methods for submission timing include, but are not limited to, the date and time of meals and the timing of data submission. Specific criteria and methods for determining priority include, but are not limited to, importance, urgency, and customer preferences. 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 meal history data into a generative AI, which can then determine the priority of proposals based on submission timing.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the food and beverages when making suggestions. For example, the suggestion unit may prioritize suggesting dishes that the customer liked. The suggestion unit may also postpone suggesting dishes that the customer avoided. The suggestion unit may also prioritize suggesting dishes related to the customer's preferences. By adjusting the order of suggestions based on the relevance of the food and beverages, more appropriate suggestions can be provided. Specific evaluation criteria and measurement methods for relevance include, but are not limited to, past selection history and current circumstances. Specific methods and criteria for adjusting the order include, but are not limited to, importance, relevance, and customer preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input meal history data into generative AI, which can then adjust the order of suggestions based on relevance.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can adjust the types of data collected based on the customer's health status. For example, if a customer is diagnosed with high blood pressure during a health checkup, the system can prioritize collecting a history of low-sodium meals. If a customer is diagnosed with diabetes, it can prioritize collecting a history of low-sugar meals. If a customer has allergies, it can prioritize collecting a history of allergen-free meals. By adjusting the types of data collected based on the customer's health status, more relevant data can be collected.

[0052] The suggestion department can adjust the content of suggestions based on the customer's past order history. For example, it can prioritize suggesting dishes that the customer has frequently ordered in the past. It can also exclude dishes that the customer has avoided in the past. It can even suggest dishes that the customer liked on specific occasions. In this way, by adjusting the content of suggestions based on the customer's past order history, more appropriate suggestions can be made.

[0053] The analysis unit can adjust its analysis algorithm based on the customer's eating history. For example, if a customer prefers Japanese food, a Japanese-specific analysis algorithm can be applied. If a customer prefers Western food, a Western-specific analysis algorithm can be applied. If a customer prefers Chinese food, a Chinese-specific analysis algorithm can be applied. By adjusting the analysis algorithm based on the customer's eating history, more appropriate analysis can be performed.

[0054] The data collection unit can adjust the types of data it collects based on the customer's geographical location. For example, it can prioritize collecting the customer's dining history at restaurants they frequently visit in a particular area. If the customer is traveling, it can also prioritize collecting their dining history in their travel destination. It can also prioritize collecting their dining history at restaurants near their home. By adjusting the types of data collected based on the customer's geographical location, it becomes possible to collect more relevant data.

[0055] The proposal department can adjust the content of its recommendations based on the customer's social media activity. For example, it can prioritize suggesting dishes that the customer has shared on social media. It can also suggest dishes that the customer has "liked" on social media. It can even suggest dishes from restaurants that the customer follows on social media. By adjusting the content of recommendations based on the customer's social media activity, it becomes possible to provide more appropriate suggestions.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the customer's past dining history and real-time reaction data. For example, it records which dishes the customer ordered and which drinks they chose, and records changes in customer satisfaction and preferences in real time. It can also estimate the customer's emotions and adjust the timing and priority of collecting dining history based on the estimated emotions. Furthermore, it can filter data based on the customer's current health status and allergy information, and collect relevant history by analyzing geographical location information and social media activity. Step 2: The analysis unit learns customer preferences based on the collected data. For example, it can estimate customer emotions and adjust the presentation, level of detail, and length of the analysis based on the estimated emotions. It can also adjust the analysis algorithm and order based on the importance, category, submission timing, and relevance of meal history. Step 3: The suggestion unit proposes the most suitable dishes and drinks based on the results learned by the analysis unit. For example, it can suggest new dishes and drinks tailored to the customer's preferences, estimate the customer's emotions, and adjust the expression, level of detail, and length of the suggestions. It can also adjust the algorithm and order of suggestions based on the importance, category, submission timing, and relevance of the dishes and drinks.

[0058] (Example of form 2) An embodiment of the present invention provides a service system specifically for repeat customers of restaurants that utilizes past dining history and real-time reaction data to offer highly personalized food and beverage recommendations. This system learns from each dining experience and, upon a return visit, provides even more optimized food recommendations based on the learned results. By recording customer preferences and dining history and reflecting this information in future recommendations, it is possible to significantly improve repeat customer rates and customer satisfaction. For example, when a customer visits a restaurant, past dining history and real-time reaction data are collected. For instance, the system records which dishes the customer ordered, which drinks they chose, and their reactions at the time (e.g., satisfaction level, changes in preferences, etc.). This information is input into an AI agent. Next, the AI ​​agent analyzes the collected data and learns the customer's preferences and dining history. For example, if a customer tends to prefer certain dishes, the system optimizes future recommendations based on this information. Furthermore, by analyzing real-time reaction data, it is possible to understand changes in customer preferences and new preferences. In addition, upon a return visit, the AI ​​agent provides optimized food and beverage recommendations based on the learned results. For example, the system can suggest new dishes based on the customer's favorites from their previous visit. It can also suggest drinks tailored to the customer's preferences. This allows customers to enjoy a different, more personalized dining experience each time. This system significantly improves repeat customer rates and customer satisfaction. Customers feel more special when they receive suggestions for food and drinks tailored to their preferences. Restaurants can also encourage repeat visits by increasing the satisfaction of their repeat customers. For example, if a customer really liked a particular dish on their last visit, the system can suggest new dishes related to that dish on their next visit. It can also suggest dishes that pair well with a customer's preferred drink. This allows customers to experience new surprises and satisfaction each time. In this way, by utilizing AI agents, restaurants can provide highly personalized dining experiences to their repeat customers. This can significantly improve repeat customer rates and customer satisfaction.This allows service systems specifically tailored to repeat customers of restaurants to offer highly personalized food and beverage recommendations based on customer preferences and dining history.

[0059] The service system for repeat customers of a restaurant according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects the customer's past dining history and real-time reaction data. The collection unit records, for example, which dishes the customer ordered and which drinks they chose. The collection unit records, for example, changes in the customer's satisfaction level and preferences in real time. The collection unit can also estimate the customer's emotions and adjust the timing of collecting dining history based on the estimated emotions. The collection unit can also analyze the customer's past dining history and select the optimal collection method. The collection unit can also filter the dining history based on the customer's current health status and allergy information when collecting it. The collection unit can also estimate the customer's emotions and determine the priority of the dining history to collect based on the estimated emotions. The collection unit can also prioritize the collection of highly relevant history based on the customer's geographical location information when collecting dining history. The collection unit can also analyze the customer's social media activity and collect relevant history when collecting dining history. The analysis unit learns the customer's preferences based on the collected data. The analysis unit can, for example, estimate customer emotions and adjust the presentation of the analysis based on the estimated customer emotions. The analysis unit can also, for example, adjust the level of detail of the analysis based on the importance of the meal history during analysis. The analysis unit can also, for example, apply different analysis algorithms depending on the category of the meal during analysis. The analysis unit can, for example, estimate customer emotions and adjust the length of the analysis based on the estimated customer emotions. The analysis unit can, for example, determine the priority of the analysis based on when the meal history was submitted during analysis. The analysis unit can also, for example, adjust the order of the analysis based on the relevance of the meal history during analysis. The suggestion unit suggests the best dishes and drinks for the next visit based on the learning results. The suggestion unit suggests new dishes and drinks tailored to the customer's preferences. The suggestion unit can, for example, estimate customer emotions and adjust the presentation of the suggestion based on the estimated customer emotions. The suggestion unit can also, for example, adjust the level of detail of the suggestion based on the importance of the dishes and drinks during suggestion.The suggestion unit can, for example, apply different suggestion algorithms depending on the food and beverage category when making suggestions. The suggestion unit can, for example, estimate the customer's emotions and adjust the length of the suggestions based on the estimated customer emotions. The suggestion unit can, for example, determine the priority of suggestions based on when the food and beverages are served when making suggestions. The suggestion unit can, for example, adjust the order of suggestions based on the relevance of the food and beverages when making suggestions. As a result, the service system tailored to repeat customers of a restaurant according to the embodiment can leverage the customer's past dining history and real-time reaction data to provide personalized food and beverage suggestions.

[0060] The data collection unit gathers customers' past dining history and real-time reaction data. Specifically, it meticulously records which dishes customers order and which drinks they choose. For example, it records the type and quantity of food ordered, the time of ordering, the type of drink, and whether or not alcohol was consumed. In addition, to record changes in customer satisfaction and preferences in real time, it monitors customers' facial expressions and behavior using cameras and sensors to estimate their emotions. This allows the system to understand whether customers are enjoying their meals and which dishes they are particularly satisfied with. Furthermore, it is possible to adjust the timing of collecting dining history based on the customer's emotions. For example, if a customer is particularly satisfied while eating a certain dish, detailed data about that dish will be prioritized for collection. It is also possible to filter the data collected based on the customer's current health status and allergy information. This enables data collection that takes the customer's health into consideration. Furthermore, it is possible to prioritize the collection of highly relevant history based on the customer's geographical location. For example, by prioritizing the collection of data on restaurants that customers frequently visit in a particular area, it becomes possible to make recommendations tailored to that area. It is also possible to analyze customers' social media activity and collect relevant history. This allows the system to understand customers' online preferences and trends, enabling more personalized recommendations.

[0061] The analytics unit learns customer preferences based on collected data. Specifically, it analyzes customers' past dining history and real-time reaction data to understand their preferences and trends. For example, if a customer frequently orders a particular dish or drink, it determines that the dish or drink is a customer preference. It can also estimate customer emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a customer is particularly satisfied when eating a particular dish, it will analyze the data related to that dish in detail. Furthermore, it is possible to adjust the level of detail of the analysis based on the importance of the dining history. For example, it will analyze the data related to dishes and drinks that customers frequently order in detail to understand customer preferences more accurately. It can also apply different analysis algorithms depending on the category of the meal. For example, it can select the appropriate analysis algorithm for different categories of dishes, such as desserts and main dishes. Furthermore, it is possible to estimate customer emotions and adjust the length of the analysis based on the estimated emotions. For example, if a customer is particularly satisfied with a particular dish, it will analyze the data related to that dish in detail. It can also prioritize the analysis based on when the dining history was submitted. For example, it can prioritize the analysis of recent dining history to understand the latest preferences. This allows the analysis unit to accurately learn customer preferences and provide the foundational data needed to make optimal suggestions during subsequent visits.

[0062] The suggestion system proposes the most suitable dishes and drinks for the next visit based on the learning results. Specifically, it suggests new dishes and drinks tailored to the customer's preferences. For example, it suggests similar new menu items based on dishes and drinks the customer has previously ordered. It can also estimate the customer's emotions and adjust the way suggestions are presented based on those emotions. For example, if the customer is particularly satisfied with a certain dish, it will provide detailed suggestions about that dish. Furthermore, it can adjust the level of detail in suggestions based on the importance of the dishes and drinks. For example, it can provide detailed suggestions about dishes and drinks that the customer particularly likes to pique their interest. It can also apply different suggestion algorithms depending on the category of dishes and drinks. For example, it can select the appropriate suggestion algorithm for different categories of dishes, such as desserts and main courses. Furthermore, it can estimate the customer's emotions and adjust the length of suggestions based on those emotions. For example, if the customer is particularly satisfied with a certain dish, it will provide detailed suggestions about that dish. It can also prioritize suggestions based on when the dishes and drinks are presented. For example, it can prioritize suggestions based on recent dining history to address the customer's latest preferences. This allows the proposal department to suggest the most suitable dishes and drinks tailored to the customer's preferences, thereby improving customer satisfaction.

[0063] The data collection unit can record which dishes a customer orders and which drinks they choose. For example, the data collection unit can store the customer's order and drink choices as digital data. The data collection unit can also record the customer's order and drink choices on paper. The data collection unit can also record the customer's order and drink choices via voice input. This allows for recording the customer's order history, which can then be used to make future recommendations. Specific methods and criteria for recording include, but are not limited to, saving as digital data or recording on paper. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the customer's order history into AI, which can then analyze the order history and store it in a database.

[0064] The data collection unit can record changes in customer satisfaction and preferences in real time. For example, the data collection unit can record customer satisfaction through questionnaire surveys. For example, the data collection unit can also record customer satisfaction through facial expression analysis. For example, the data collection unit can also record customer satisfaction through voice analysis. For example, the data collection unit can record changes in customer preferences by comparing them with past selection history. For example, the data collection unit can also record changes in customer preferences by analyzing real-time reaction data. For example, the data collection unit can also record changes in customer preferences using sensor data. This allows for more accurate suggestions by recording customer satisfaction and changes in preferences in real time. Specific evaluation criteria and measurement methods for satisfaction include, but are not limited to, questionnaire surveys, facial expression analysis, and voice analysis. Specific content and measurement methods for changes in preferences include, but are not limited to, comparisons with past selection history and analysis of real-time reaction data. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input customer satisfaction levels and changes in preferences into the AI, which can then analyze these changes and store them in a database.

[0065] The analysis unit can learn customer preferences based on collected data. For example, the analysis unit can learn the types of food a customer likes based on collected data. The analysis unit can also learn the ingredients a customer dislikes based on collected data. The analysis unit can also learn the customer's past selection history based on collected data. This allows the system to learn customer preferences based on collected data and reflect them in future recommendations. The specific content and learning methods of preferences include, but are not limited to, the types of food a customer likes, the ingredients they dislike, and their past selection history. Some or all of the above-described processes 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 collected data into a generative AI, which can learn customer preferences and store them in a database.

[0066] The suggestion unit can suggest the most suitable dishes and drinks for the next visit based on the learning results. For example, the suggestion unit can suggest new dishes based on the dishes the customer liked on their previous visit. The suggestion unit can also suggest drinks that suit the customer's preferences. The suggestion unit can also suggest dishes that suit the customer's health condition. This improves customer satisfaction by suggesting the most suitable dishes and drinks for the next visit based on the learning results. Specific criteria and selection methods for the most suitable dishes and drinks include, but are not limited to, nutritional balance, customer preferences, and health condition. 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 learning results into a generative AI, which can then suggest the most suitable dishes and drinks.

[0067] The suggestion department can propose new dishes and drinks tailored to customer preferences. For example, the suggestion department can propose new dishes based on the customer's preferred food trends. The suggestion department can also propose new seasonal menu items. The suggestion department can also propose new drinks tailored to customer preferences. This improves customer repeat rates by proposing new dishes and drinks tailored to customer preferences. The specific content and methods of proposing new dishes and drinks include, but are not limited to, seasonal menus and new products. 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 customer preference data into a generative AI, which can then propose new dishes and drinks.

[0068] The data collection unit can estimate the customer's emotions and adjust the timing of meal history collection based on the estimated emotions. For example, if the customer is relaxed, the data collection unit will collect the meal history as the meal nears its end. For example, if the customer is in a hurry, the data collection unit can quickly collect the meal history in the middle of the meal. For example, if the customer is enjoying the meal, the data collection unit can collect the meal history in real time during the meal. This allows for data to be collected at a more appropriate time by adjusting the collection timing based on the customer's emotions. Specific methods and criteria for estimating customer emotions include, but are not limited to, facial recognition, voice analysis, and sensor data. Specific criteria and methods for adjusting the collection timing include, but are not limited to, during the meal, after the meal, or during specific events. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the timing of data collection.

[0069] The data collection unit can analyze a customer's past dining history and select the optimal data collection method. For example, the data collection unit may prioritize collecting dishes that the customer has frequently ordered in the past. The data collection unit may also analyze the customer's past preferred dishes and collect a history of similar dishes. The data collection unit may also exclude dishes that the customer has avoided in the past. This allows the optimal data collection method to be selected by analyzing the customer's past dining history. Specific criteria and selection methods for the optimal data collection method include, but are not limited to, saving as digital data or recording on paper. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the customer's past dining history into a generative AI, which can then select the optimal data collection method.

[0070] The collection unit can filter the collected meal history based on the customer's current health status and allergy information. For example, the collection unit can exclude dishes containing ingredients the customer is allergic to. For example, the collection unit can prioritize the collection of low-calorie dishes depending on the customer's health status. For example, the collection unit can collect nutritionally balanced dishes depending on the customer's health status. This allows for safer recommendations by filtering based on the customer's health status and allergy information. Specific details and evaluation methods of health status include, but are not limited to, medical data, self-reporting, and sensor data. Specific details and collection methods of allergy information include, but are not limited to, medical data and self-reporting. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the collection unit can input the customer's health status and allergy information into a generating AI, which can then perform the filtering.

[0071] The data collection unit can estimate customer emotions and determine the priority of meal history to collect based on the estimated emotions. For example, if a customer is satisfied, the data collection unit will prioritize collecting the history of their favorite dishes. For example, if a customer is dissatisfied, the data collection unit can also prioritize collecting the history of dishes they avoided. For example, if a customer is excited, the data collection unit can also prioritize collecting the history of new dishes. This allows for the priority collection of more important data by determining the priority of meal history to collect based on customer emotions. Specific methods and criteria for estimating customer emotions include, but are not limited to, facial recognition, voice analysis, and sensor data. Specific criteria and methods for determining priorities include, but are not limited to, importance, urgency, and customer preferences. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input customer emotion data into a generating AI, which can then estimate the emotion and determine the priority of meal history to collect.

[0072] The data collection unit can prioritize collecting highly relevant history based on the customer's geographical location information when collecting meal history. For example, the data collection unit can prioritize collecting the history of restaurants that the customer frequently visits in a particular area. For example, if the customer is traveling, the data collection unit can also prioritize collecting meal history in the area of ​​their travel destination. For example, the data collection unit can also prioritize collecting meal history at restaurants near the customer's home. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant history based on the customer's geographical location information. The specific content and collection methods of geographical location information include, but are not limited to, GPS data and address information. The specific criteria and selection methods for highly relevant history include, but are not limited to, past selection history and current location information. Some or all of the above processing 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 customer's geographical location information into a generative AI, which can then prioritize the collection of highly relevant history.

[0073] The data collection unit can analyze the customer's social media activity and collect relevant history when collecting meal history. For example, the data collection unit can collect the history of dishes the customer has shared on social media. The data collection unit can also collect the history of dishes the customer has "liked" on social media. The data collection unit can also collect the history of dishes from restaurants the customer follows on social media. This allows for the collection of relevant history by analyzing the customer's social media activity. The specific content and analysis methods of social media activity include, but are not limited to, posts, the number of likes, and comments. Some or all of the above processing 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 customer's social media activity data into a generative AI, which can then collect relevant history.

[0074] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results. For example, if the customer is in a hurry, the analysis unit can also provide concise analysis results. For example, if the customer is excited, the analysis unit can also provide visually appealing analysis results. By adjusting the presentation of the analysis based on the customer's emotions, more appropriate analysis results can be provided. Specific methods and criteria for estimating customer emotions include, but are not limited to, facial recognition, voice analysis, and sensor data. Specific content and adjustment methods for the presentation of the analysis include, but are not limited to, graph display, text display, and audio output. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the way the analysis is expressed.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the meal history during the analysis. For example, the analysis unit can perform a detailed analysis of dishes that the customer frequently orders. The analysis unit can also simplify the analysis of dishes that the customer has ordered only once. The analysis unit can also perform a detailed analysis of dishes that the customer particularly likes. This allows for a more detailed analysis of important data by adjusting the level of detail based on the importance of the meal history. Specific evaluation criteria and measurement methods for importance include, but are not limited to, customer preferences, health status, and past selection history. Specific adjustment methods and criteria for level of detail include, but are not limited to, data granularity and the level of detail displayed. 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 meal history data into a generative AI, which can then adjust the level of detail of the analysis based on importance.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the meal during analysis. For example, the analysis unit can apply different analysis algorithms to the main dish and dessert. For example, the analysis unit can apply different analysis algorithms to drinks and dishes. For example, the analysis unit can apply different analysis algorithms to appetizers and main dishes. By applying different analysis algorithms depending on the category of the meal, more appropriate analysis results can be provided. The specific content and classification methods of meal categories include, but are not limited to, Japanese food, Western food, Chinese food, etc. The specific types and application methods of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms, etc. 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 meal category data into a generative AI, and the generative AI can apply different analysis algorithms.

[0077] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the customer is relaxed, the analysis unit can also provide a detailed analysis. For example, if the customer is excited, the analysis unit can also provide a visually engaging analysis. By adjusting the length of the analysis based on the customer's emotions, more appropriate analysis results can be provided. Specific methods and criteria for estimating customer emotions include, but are not limited to, facial recognition, voice analysis, and sensor data. Specific methods and criteria for adjusting the length of the analysis include, but are not limited to, the amount of data and the depth of the analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the submission timing of meal history data. For example, the analysis unit may prioritize the analysis of recent meal history. The analysis unit may also prioritize the analysis of meal history data from specific events. The analysis unit may also prioritize the analysis of seasonal meal history data. By prioritizing analysis based on the submission timing of meal history data, more important data can be analyzed preferentially. Specific details and evaluation methods for submission timing include, but are not limited to, the date and time of the meal and the timing of data submission. Specific criteria and methods for determining priority include, but are not limited to, importance, urgency, and customer preferences. 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 meal history data into a generative AI, which can then determine the priority of analysis based on the submission timing.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the meal history during the analysis. For example, the analysis unit may prioritize the analysis of the history of dishes the customer preferred. The analysis unit may also postpone the analysis of the history of dishes the customer avoided. The analysis unit may also prioritize the analysis of the history of dishes related to the customer's preferences. By adjusting the order of analysis based on the relevance of the meal history, more important data can be analyzed preferentially. Specific evaluation criteria and measurement methods for relevance include, but are not limited to, past selection history and current circumstances. Specific methods and criteria for adjusting the order include, but are not limited to, importance, relevance, and customer preferences. 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 meal history data into a generative AI, which can then adjust the order of analysis based on relevance.

[0080] The suggestion unit can estimate the customer's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the customer is relaxed, the suggestion unit can provide detailed suggestions. If the customer is in a hurry, the suggestion unit can provide concise suggestions. If the customer is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the way it presents suggestions based on the customer's emotions, it is possible to provide more appropriate suggestions. Specific methods and criteria for estimating customer emotions include, but are not limited to, facial recognition, voice analysis, and sensor data. Specific methods and adjustments for presenting suggestions include, but are not limited to, text display, voice output, and visual display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the way the proposal is expressed.

[0081] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the food and beverages. For example, the suggestion unit can provide detailed suggestions for dishes the customer liked. The suggestion unit can also simplify suggestions for dishes the customer avoided. The suggestion unit can also provide detailed suggestions for beverages the customer particularly liked. This allows for more detailed suggestions by adjusting the level of detail based on the importance of the food and beverages. Specific evaluation criteria and measurement methods for importance include, but are not limited to, customer preferences, health status, and past selection history. Specific adjustment methods and criteria for level of detail include, but are not limited to, data granularity and the level of detail displayed. 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 food and beverage data into a generative AI, which can then adjust the level of detail of the suggestions based on importance.

[0082] The suggestion unit can apply different suggestion algorithms depending on the food and beverage categories when making suggestions. For example, the suggestion unit can apply different suggestion algorithms for main dishes and desserts. It can also apply different suggestion algorithms for beverages and dishes. It can also apply different suggestion algorithms for appetizers and main dishes. By applying different suggestion algorithms depending on the food and beverage categories, more appropriate suggestions can be provided. The specific content and classification methods of food and beverage categories include, but are not limited to, Japanese food, Western food, Chinese food, etc. The specific types and application methods of suggestion algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms, etc. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input food and beverage category data into a generative AI, and the generative AI can apply different suggestion algorithms.

[0083] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the customer is in a hurry, the suggestion unit will make a short, to-the-point suggestion. For example, if the customer is relaxed, the suggestion unit may make a detailed suggestion. For example, if the customer is excited, the suggestion unit may make a visually appealing suggestion. By adjusting the length of the suggestion based on the customer's emotions, more appropriate suggestions can be provided. Specific methods and criteria for estimating customer emotions include, but are not limited to, facial recognition, voice analysis, and sensor data. Specific methods and criteria for adjusting the length of the suggestion include, but are not limited to, the amount of data and the level of detail in the suggestion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the length of the proposal accordingly.

[0084] The proposal department can prioritize proposals based on the timing of food and beverage submissions. For example, the proposal department may make proposals based on recent meal history. The proposal department may also make proposals based on meal history during specific events. The proposal department may also make proposals based on seasonal meal history. This allows for prioritizing more important proposals by determining the timing of food and beverage submissions. Specific details and evaluation methods for submission timing include, but are not limited to, the date and time of meals and the timing of data submission. Specific criteria and methods for determining priority include, but are not limited to, importance, urgency, and customer preferences. 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 meal history data into a generative AI, which can then determine the priority of proposals based on submission timing.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the food and beverages when making suggestions. For example, the suggestion unit may prioritize suggesting dishes that the customer liked. The suggestion unit may also postpone suggesting dishes that the customer avoided. The suggestion unit may also prioritize suggesting dishes related to the customer's preferences. By adjusting the order of suggestions based on the relevance of the food and beverages, more appropriate suggestions can be provided. Specific evaluation criteria and measurement methods for relevance include, but are not limited to, past selection history and current circumstances. Specific methods and criteria for adjusting the order include, but are not limited to, importance, relevance, and customer preferences. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input meal history data into generative AI, which can then adjust the order of suggestions based on relevance.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The suggestion department can estimate the customer's emotions and adjust the timing of suggestions based on those emotions. For example, if the customer is relaxed, a suggestion can be made as the meal nears its end. If the customer is in a hurry, a suggestion can be made quickly in the middle of the meal. If the customer is enjoying themselves, a suggestion can be made in real time during the meal. By adjusting the timing of suggestions based on the customer's emotions, suggestions can be made at a more appropriate time.

[0088] The analysis unit can estimate customer emotions and determine analysis priorities based on those estimated emotions. For example, if a customer is satisfied, it can prioritize analyzing their favorite dishes. If a customer is dissatisfied, it can prioritize analyzing dishes they avoided. If a customer is excited, it can prioritize analyzing new dishes. By prioritizing analysis based on customer emotions, more important data can be analyzed first.

[0089] The data collection unit can estimate the customer's emotions and adjust the type of data collected based on those emotions. For example, if the customer is relaxed, detailed meal history can be collected. If the customer is in a hurry, a brief meal history can be collected. If the customer is enjoying themselves, real-time emotion data can be collected. This allows for the collection of more relevant data by adjusting the type of data collected based on the customer's emotions.

[0090] The suggestion department can estimate the customer's emotions and adjust the content of the suggestion based on those emotions. For example, if the customer is relaxed, it can suggest detailed dishes. If the customer is in a hurry, it can suggest concise dishes. If the customer is enjoying themselves, it can suggest visually appealing dishes. In this way, by adjusting the content of the suggestion based on the customer's emotions, it can make more appropriate suggestions.

[0091] The analysis unit can estimate the customer's emotions and adjust the depth of the analysis based on those emotions. For example, if the customer is relaxed, a detailed analysis can be performed. If the customer is in a hurry, a concise analysis can be performed. If the customer is enjoying themselves, a visually engaging analysis can be performed. By adjusting the depth of the analysis based on the customer's emotions, a more appropriate analysis can be performed.

[0092] The data collection unit can adjust the types of data collected based on the customer's health status. For example, if a customer is diagnosed with high blood pressure during a health checkup, the system can prioritize collecting a history of low-sodium meals. If a customer is diagnosed with diabetes, it can prioritize collecting a history of low-sugar meals. If a customer has allergies, it can prioritize collecting a history of allergen-free meals. By adjusting the types of data collected based on the customer's health status, more relevant data can be collected.

[0093] The suggestion department can adjust the content of suggestions based on the customer's past order history. For example, it can prioritize suggesting dishes that the customer has frequently ordered in the past. It can also exclude dishes that the customer has avoided in the past. It can even suggest dishes that the customer liked on specific occasions. In this way, by adjusting the content of suggestions based on the customer's past order history, more appropriate suggestions can be made.

[0094] The analysis unit can adjust its analysis algorithm based on the customer's eating history. For example, if a customer prefers Japanese food, a Japanese-specific analysis algorithm can be applied. If a customer prefers Western food, a Western-specific analysis algorithm can be applied. If a customer prefers Chinese food, a Chinese-specific analysis algorithm can be applied. By adjusting the analysis algorithm based on the customer's eating history, more appropriate analysis can be performed.

[0095] The data collection unit can adjust the types of data it collects based on the customer's geographical location. For example, it can prioritize collecting the customer's dining history at restaurants they frequently visit in a particular area. If the customer is traveling, it can also prioritize collecting their dining history in their travel destination. It can also prioritize collecting their dining history at restaurants near their home. By adjusting the types of data collected based on the customer's geographical location, it becomes possible to collect more relevant data.

[0096] The proposal department can adjust the content of its recommendations based on the customer's social media activity. For example, it can prioritize suggesting dishes that the customer has shared on social media. It can also suggest dishes that the customer has "liked" on social media. It can even suggest dishes from restaurants that the customer follows on social media. By adjusting the content of recommendations based on the customer's social media activity, it becomes possible to provide more appropriate suggestions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects the customer's past dining history and real-time reaction data. For example, it records which dishes the customer ordered and which drinks they chose, and records changes in customer satisfaction and preferences in real time. It can also estimate the customer's emotions and adjust the timing and priority of collecting dining history based on the estimated emotions. Furthermore, it can filter data based on the customer's current health status and allergy information, and collect relevant history by analyzing geographical location information and social media activity. Step 2: The analysis unit learns customer preferences based on the collected data. For example, it can estimate customer emotions and adjust the presentation, level of detail, and length of the analysis based on the estimated emotions. It can also adjust the analysis algorithm and order based on the importance, category, submission timing, and relevance of meal history. Step 3: The suggestion unit proposes the most suitable dishes and drinks based on the results learned by the analysis unit. For example, it can suggest new dishes and drinks tailored to the customer's preferences, estimate the customer's emotions, and adjust the expression, level of detail, and length of the suggestions. It can also adjust the algorithm and order of suggestions based on the importance, category, submission timing, and relevance of the dishes and drinks.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects customer meal history and real-time reaction data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns customer preferences based on the collected data. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes and drinks for the next visit based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the customer's meal history and real-time reaction data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the customer's preferences based on the collected data. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes and drinks for the next visit based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects customer meal history and real-time reaction data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns customer preferences based on the collected data. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes and drinks for the next visit based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect customer meal history and real-time reaction data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns customer preferences based on the collected data. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and suggests the optimal dishes and drinks for the next visit based on the learning results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A data collection unit that collects customers' past meal history and real-time reaction data, An analysis unit analyzes the data collected by the aforementioned collection unit and learns customer preferences, The system includes a suggestion unit that proposes the optimal dishes and drinks based on the results learned by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Record which dishes customers order and which drinks they choose. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Record customer satisfaction and changes in preferences in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Learn customer preferences based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the learning results, we will suggest the best dishes and drinks for your next visit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose new dishes and drinks tailored to the customer's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate customer emotions and adjust the timing of meal history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the customer's past meal history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting meal history, filtering is performed based on the customer's current health status and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates customer emotions and prioritizes the collection of dining history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting meal history, the system prioritizes collecting highly relevant history based on the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting meal history, analyze the customer's social media activity and collect relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate customer emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the meal category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates customer emotions and adjusts the length of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses will be determined based on when the dietary history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the food and beverages. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the food and beverage categories. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the customer's emotions and adjust the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of food and beverage submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the food and drinks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 data collection unit that collects customers' past meal history and real-time reaction data, An analysis unit analyzes the data collected by the aforementioned collection unit and learns customer preferences, The system includes a suggestion unit that proposes the optimal dishes and drinks based on the results learned by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Record which dishes customers order and which drinks they choose. The system according to feature 1.

3. The aforementioned collection unit is Record customer satisfaction and changes in preferences in real time. The system according to feature 1.

4. The aforementioned analysis unit, Learn customer preferences based on collected data. The system according to feature 1.

5. The aforementioned proposal section is, Based on the learning results, we will suggest the best dishes and drinks for your next visit. The system according to feature 1.

6. The aforementioned proposal section is, We propose new dishes and drinks tailored to the customer's preferences. The system according to feature 1.

7. The aforementioned collection unit is We estimate customer emotions and adjust the timing of meal history collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze the customer's past meal history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting meal history, filtering is performed based on the customer's current health status and allergy information. The system according to feature 1.

10. The aforementioned collection unit is The system estimates customer emotions and prioritizes the collection of dining history based on those estimated emotions. The system according to feature 1.

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