system

Abstract

The system according to this embodiment aims to analyze ambiguous orders and suggest menus that suit the customer's preferences and restrictions. [Solution] The system according to the embodiment comprises an order analysis unit, a menu presentation unit, a suggestion unit, and a display unit. The order analysis unit analyzes ambiguous orders. The menu presentation unit presents menus that take into account allergies and dietary restrictions based on the information analyzed by the order analysis unit. The suggestion unit analyzes the customer's preferences and makes suggestions based on the menus presented by the menu presentation unit. The display unit visually displays the menus suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is impossible to handle ambiguous orders and it is difficult to make proposals according to customer preferences and restrictions.

[0005] The system according to the embodiment aims to analyze ambiguous orders and propose a menu according to customer preferences and restrictions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an order analysis unit, a menu presentation unit, a suggestion unit, and a display unit. The order analysis unit analyzes ambiguous orders. The menu presentation unit presents menus that take into account allergies and dietary restrictions based on the information analyzed by the order analysis unit. The suggestion unit analyzes the customer's preferences and makes suggestions based on the menus presented by the menu presentation unit. The display unit visually displays the menus suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze ambiguous orders and suggest menus that suit the customer's preferences and restrictions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Smart Order AI Agent according to an embodiment of the present invention is a system that revolutionizes the ordering experience by utilizing AI technology. This system solves conventional problems such as the inability to handle ambiguous orders, difficulty in making suggestions that match customer preferences and restrictions, and inability to make suggestions that take into account congestion and serving times. The Smart Order AI Agent performs order analysis using natural language processing and can appropriately understand and make suggestions even for ambiguous orders and questions. For example, it can handle ambiguous orders such as "something healthy and warm." Next, it links with member information to present menus that take allergies and dietary restrictions into consideration. This allows it to suggest the optimal menu according to the customer's health condition and dietary restrictions. Furthermore, it analyzes customer preferences and makes suggestions based on experience. It can learn customer preferences from past order history and make optimal suggestions. For example, it suggests menus that customers like based on previously ordered menus and search history. It also provides a visual menu display. By providing a life-size menu display using images, videos, and AR technology, customers can intuitively select menus. Furthermore, it supports multiple languages. This allows it to provide a smooth ordering experience for diverse customers, such as foreign tourists. Finally, it grasps congestion levels and makes efficient order suggestions. By suggesting the most suitable dishes based on the expected serving time, the system can reduce customer waiting times and improve the operational efficiency of the store. In this way, the smart order AI agent leverages AI technology to innovate the ordering experience, improving customer satisfaction, streamlining the ordering process, reducing food waste, and optimizing store operations. As a result, the smart order AI agent can significantly enhance the customer ordering experience.

[0029] The smart order AI agent according to this embodiment comprises an order analysis unit, a menu presentation unit, a suggestion unit, and a display unit. The order analysis unit analyzes ambiguous orders. The order analysis unit analyzes ambiguous orders using, for example, natural language processing technology. The order analysis unit can appropriately understand ambiguous orders and make optimal suggestions to customers. The order analysis unit can also handle ambiguous orders such as, for example, "something healthy and warm." The order analysis unit can analyze orders containing ambiguous expressions using natural language processing technology and accurately grasp the customer's intent. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or without using AI. The menu presentation unit presents menus that take into account allergies and dietary restrictions based on the information analyzed by the order analysis unit. The menu presentation unit presents menus that take into account allergies and dietary restrictions in conjunction with, for example, member information. The menu presentation unit can suggest optimal menus according to the customer's health condition and dietary restrictions. The menu presentation unit acquires, for example, customer allergy information and dietary restriction information and presents menus based on that information. The menu display unit can display menus tailored to the customer's health condition and dietary restrictions, in conjunction with member information. Some or all of the above-described processes in the menu display unit may be performed using AI, for example, or without AI. The suggestion unit analyzes the customer's preferences and makes suggestions based on the menus displayed by the menu display unit. The suggestion unit analyzes the customer's preferences and makes suggestions based on past order history, for example. The suggestion unit can learn the customer's preferences and make optimal suggestions. The suggestion unit suggests menus that the customer will like, for example, based on past ordered menus and search history. The suggestion unit can analyze past order history and make optimal suggestions based on the customer's preferences. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. The display unit visually displays the menus suggested by the suggestion unit. The display unit provides a visual menu display using images, videos, or AR technology, for example. The display unit provides a visual menu display so that the customer can intuitively choose from the menu.The display unit can, for example, display a life-size menu, which can serve as a reference for customers when choosing from the menu. The display unit can use images, videos, and AR technology to enable customers to intuitively select from the menu. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. As a result, the smart order AI agent according to this embodiment can significantly improve the customer's ordering experience.

[0030] The Order Analysis Department analyzes ambiguous orders. For example, it uses natural language processing technology to analyze ambiguous orders. Specifically, the Order Analysis Department receives text and voice data entered by customers and analyzes its content using natural language processing technology. Natural language processing technology includes morphological analysis, contextual analysis, and semantic analysis, and these are combined to accurately grasp the customer's intent. For example, for an ambiguous order such as "something healthy and warm," the keywords "healthy" and "warm" are extracted, and an appropriate menu item is identified based on these. The Order Analysis Department uses AI to learn the customer's order history and preferences, enabling it to respond flexibly to ambiguous orders. For example, if a customer who previously ordered a "healthy salad" orders "something healthy," the AI ​​will refer to that history and suggest a salad. Furthermore, the Order Analysis Department can make more accurate suggestions by analyzing the nuances and emotions of the customer's speech. For example, if a customer appears tired and orders "something to give me energy," the AI ​​will analyze their emotions and suggest a menu item suitable for energy replenishment. This allows the order analysis department to accurately understand ambiguous customer orders and make optimal suggestions.

[0031] The menu display unit presents menus that take allergies and dietary restrictions into account, based on information analyzed by the order analysis unit. Specifically, the menu display unit links with the customer's membership information to obtain allergy and dietary restriction information. For example, if a customer has a nut allergy, menus containing nuts will not be displayed. It also accommodates dietary restrictions such as vegetarianism and gluten-free diets, presenting menus tailored to the customer's health condition. The menu display unit can use AI to learn the customer's health condition and dietary restrictions and suggest the most suitable menu. For example, if a customer is on a diet, it will prioritize displaying low-calorie menus. The menu display unit can also suggest menus according to the season and time of day. For example, it will suggest cold or refreshing dishes in the summer and warm or nutritious dishes in the winter. In this way, the menu display unit can provide the most suitable menu according to the customer's health condition and dietary restrictions, thereby improving customer satisfaction.

[0032] The suggestion department analyzes customer preferences and makes suggestions based on the menus presented by the menu presentation department. Specifically, the suggestion department analyzes customer preferences based on past order history and search history. For example, it prioritizes suggesting menus that customers have ordered many times in the past or that they have frequently searched for. Furthermore, the suggestion department can use AI to learn customer preferences and make optimal suggestions for individual customers. For example, if a customer likes spicy food, it will prioritize suggesting menus that include spicy dishes. The suggestion department can also make suggestions based on the customer's current situation and mood. For example, if a customer is tired, it will suggest relaxing or nutritious dishes. In addition, the suggestion department can also suggest popular or highly-rated menu items by referring to ratings and reviews from other customers. In this way, the suggestion department can make optimal suggestions tailored to the customer's preferences and situation, thereby improving customer satisfaction.

[0033] The display unit visually displays the menu proposed by the suggestion unit. Specifically, the display unit provides a visual menu display using images, videos, and AR technology. For example, it displays menu images in high resolution so that customers can check the appearance of the dishes. It can also attract customers' interest by using videos to introduce the cooking process and the finished product. Furthermore, it uses AR technology to display the menu at actual size so that customers can intuitively understand the size and appearance of the dishes. The display unit provides a visual menu display that allows customers to intuitively choose from the menu. For example, it uses a touchscreen so that customers can swipe to select from the menu. It also supports customer selection by using other senses in addition to sight by combining feedback such as voice guidance and vibration notifications. In this way, the display unit can enable customers to intuitively choose from the menu and improve the ordering experience.

[0034] The order analysis unit can analyze ambiguous orders using natural language processing. For example, the order analysis unit analyzes ambiguous orders using natural language processing technology. The order analysis unit can appropriately understand ambiguous orders and make optimal suggestions to customers. The order analysis unit can handle ambiguous orders such as, for example, "something healthy and warm." The order analysis unit can analyze orders containing ambiguous expressions using natural language processing technology and accurately grasp the customer's intent. This improves the accuracy of ambiguous order analysis by using natural language processing. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the order analysis unit may be performed using, for example, AI, or not. For example, the order analysis unit can analyze ambiguous orders using an AI model that takes ambiguous orders as input and outputs analysis results.

[0035] The menu display unit can present menus that take into account allergies and dietary restrictions in conjunction with member information. For example, the menu display unit can present menus that take into account allergies and dietary restrictions in conjunction with member information. The menu display unit can suggest the optimal menu according to the customer's health condition and dietary restrictions. For example, the menu display unit can acquire the customer's allergy information and dietary restriction information and present menus based on that information. The menu display unit can present menus according to the customer's health condition and dietary restrictions in conjunction with member information. This allows the display of menus that take into account allergies and dietary restrictions by linking with member information. Member information includes, but is not limited to, allergy information and dietary restriction information. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not. For example, the menu display unit can present menus using an AI model that takes member information as input and outputs menus that take allergies and dietary restrictions into account.

[0036] The suggestion department can analyze customer preferences and make suggestions based on past order history. For example, the suggestion department can analyze customer preferences and make suggestions based on past order history. The suggestion department can learn customer preferences and make optimal suggestions. For example, the suggestion department can suggest menus that customers like based on previously ordered menus and search history. The suggestion department can analyze past order history and make optimal suggestions based on customer preferences. This makes it possible to make optimal suggestions by analyzing customer preferences based on past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not using AI. For example, the suggestion department can make suggestions using an AI model that takes past order history as input and outputs suggestions based on customer preferences.

[0037] The display unit can display a visual menu using images, videos, and AR technology. For example, the display unit displays a visual menu using images, videos, and AR technology. The display unit provides a visual menu display so that customers can intuitively select from the menu. For example, the display unit can display a life-size menu for customers to use as a reference when choosing from the menu. The display unit uses images, videos, and AR technology to enable customers to intuitively select from the menu. This allows for a visual display of the menu using images, videos, and AR technology. Images, videos, and AR technology include, but are not limited to, 3D models and interactive content. Some or all of the above processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can display a visual menu using an AI model that generates menu displays using images, videos, and AR technology.

[0038] The smart order AI agent is equipped with a multilingual support unit that handles orders and guidance in multiple languages. The multilingual support unit, for example, handles orders and guidance in multiple languages. The multilingual support unit can provide a smooth ordering experience to diverse customers, such as foreign tourists. Thus, by equipping the unit with a multilingual support unit, orders and guidance in multiple languages ​​become possible. Multilingual support includes, but is not limited to, the types of languages ​​supported and translation methods. Some or all of the processing described above in the multilingual support unit may be performed using AI, for example, or not using AI. For example, the multilingual support unit can perform multilingual support using an AI model that takes the customer's language as input and outputs orders and guidance in the corresponding language.

[0039] The smart order AI agent includes a suggestion optimization unit that understands the congestion situation and proposes the optimal dishes based on the expected serving time. The suggestion optimization unit, for example, understands the congestion situation and proposes the optimal dishes based on the expected serving time. The suggestion optimization unit can reduce customer waiting times and improve the operational efficiency of the store. Thus, by including the suggestion optimization unit, it is possible to understand the congestion situation and propose the optimal dishes based on the expected serving time. Congestion information includes, but is not limited to, real-time data and historical data. Some or all of the processing described above in the suggestion optimization unit may be performed using, for example, AI, or not using AI. For example, the suggestion optimization unit can take congestion data as input and make suggestions using an AI model that proposes the optimal dishes based on the expected serving time.

[0040] The order analysis unit can optimize the interpretation of ambiguous orders by referring to the user's past order history during order analysis. For example, the order analysis unit can clarify ambiguous orders based on menus the user has ordered in the past. The order analysis unit can also prioritize suggesting frequently ordered menus based on the user's past order history. The order analysis unit can also optimize the interpretation of ambiguous orders by analyzing the user's past order history. This allows for the optimization of ambiguous order interpretation by referring to the user's past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or not using AI. For example, the order analysis unit can take past order history as input and perform analysis using an AI model that optimizes the interpretation of ambiguous orders.

[0041] The order analysis unit can adjust the analysis results when analyzing an order, taking into account the user's current health status and dietary restrictions. For example, if the user inputs their health checkup results, the AI ​​will analyze ambiguous orders based on that information. The order analysis unit can also analyze ambiguous orders based on information if the user sets dietary restrictions. The order analysis unit can also analyze ambiguous orders based on information if the user inputs their current health status. This allows the analysis results of ambiguous orders to be adjusted by taking into account the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or not using AI. For example, the order analysis unit can perform analysis using an AI model that takes current health status and dietary restriction information as input and adjusts the analysis results of ambiguous orders.

[0042] The order analysis unit can analyze region-specific ambiguous orders by considering the user's geographical location information during order analysis. For example, if the user is in a specific region, the order analysis unit can analyze region-specific ambiguous orders. If the user is traveling, the order analysis unit can also analyze region-specific ambiguous orders. If the user is in their hometown, the order analysis unit can also analyze local-specific ambiguous orders. In this way, region-specific ambiguous orders can be analyzed by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or not using AI. For example, the order analysis unit can take geographical location information as input and perform analysis using an AI model that analyzes region-specific ambiguous orders.

[0043] The order analysis unit can analyze users' social media activity during order analysis and analyze related ambiguous orders. For example, the order analysis unit can analyze ambiguous orders based on information shared by users on social media. The order analysis unit can also analyze ambiguous orders based on information from accounts that users follow on social media. The order analysis unit can also analyze ambiguous orders based on posts that users have liked on social media. In this way, related ambiguous orders can be analyzed by analyzing users' social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the order analysis unit may be performed using AI, for example, or without AI. For example, the order analysis unit can take social media activity data as input and perform analysis using an AI model that analyzes related ambiguous orders.

[0044] The menu display unit can present the most suitable menu by referring to the user's past allergy information and dietary restrictions when displaying menus. For example, if the user has previously entered allergy information, the menu display unit can present the most suitable menu based on that information. If the user has set dietary restrictions, the menu display unit can also present the most suitable menu based on that information. If the user has entered health checkup results, the menu display unit can also present the most suitable menu based on that information. In this way, the optimal menu can be presented by referring to the user's past allergy information and dietary restrictions. Past allergy information includes, but is not limited to, medical records and self-declarations. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not using AI. For example, the menu display unit can use an AI model that takes past allergy information and dietary restriction information as input and presents the optimal menu.

[0045] The menu display unit can customize the menu when presenting it, taking into account the user's current health status and dietary restrictions. For example, if the user enters their current health status, the menu display unit can customize the menu based on that information. If the user sets dietary restrictions, the menu display unit can also customize the menu based on that information. If the user enters their health checkup results, the menu display unit can also customize the menu based on that information. In this way, the menu can be customized by taking into account the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not using AI. For example, the menu display unit can use an AI model that takes current health status and dietary restriction information as input and customize the menu.

[0046] The menu display unit can present region-specific menus by considering the user's geographical location information when displaying menus. For example, if the user is in a specific region, the menu display unit can present region-specific menus. If the user is traveling, the menu display unit can also present region-specific menus for the travel destination. If the user is in their local area, the menu display unit can also present local menus. In this way, region-specific menus can be presented by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not using AI. For example, the menu display unit can use an AI model that takes geographical location information as input and presents region-specific menus to make the presentation.

[0047] The menu presentation unit can analyze the user's social media activity and present relevant menu items when presenting a menu. For example, the menu presentation unit can present relevant menu items based on information the user has shared on social media. The menu presentation unit can also present relevant menu items based on information about accounts the user follows on social media. The menu presentation unit can also present relevant menu items based on posts the user has liked on social media. In this way, relevant menu items can be presented by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the menu presentation unit may be performed using, for example, AI, or not using AI. For example, the menu presentation unit can use an AI model that takes social media activity data as input and presents relevant menu items.

[0048] The suggestion unit can refer to past order history to best reflect the user's preferences when making suggestions. For example, the suggestion unit can make optimal suggestions based on menus the user has ordered in the past. The suggestion unit can also prioritize suggesting frequently ordered menus from the user's past order history. The suggestion unit can also analyze the user's past order history to make optimal suggestions. This makes it possible to make suggestions that best reflect the user's preferences by referring to past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can use an AI model that takes past order history as input and makes suggestions based on the user's preferences to make suggestions.

[0049] The suggestion unit can customize its suggestions by taking into account the user's current health status and dietary restrictions. For example, if the user inputs their current health status, the suggestion unit will customize the suggestions based on that information. If the user sets dietary restrictions, the suggestion unit can also customize the suggestions based on that information. If the user inputs their health checkup results, the suggestion unit can also customize the suggestions based on that information. In this way, the suggestions can be customized by taking into account the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can take current health status and dietary restriction information as input and make suggestions using an AI model that customizes the suggestions.

[0050] The suggestion unit can make region-specific suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will make region-specific suggestions for that region. If the user is traveling, the suggestion unit can also make region-specific suggestions for the travel destination. If the user is in their local area, the suggestion unit can also make local area-specific suggestions. This makes region-specific suggestions possible by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes geographical location information as input and makes region-specific suggestions.

[0051] The suggestion unit can analyze a user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on information shared by the user on social media. The suggestion unit can also make relevant suggestions based on information about accounts followed by the user on social media. The suggestion unit can also make relevant suggestions based on posts liked by the user on social media. This makes it possible to make relevant suggestions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can make suggestions using an AI model that takes social media activity data as input and makes relevant suggestions.

[0052] The display unit can select the optimal display method by referring to the user's past visual preferences when displaying information. For example, the display unit can select the optimal display method based on the colors and designs the user has preferred in the past. The display unit can also analyze the user's past visual preferences and select the optimal display method. The display unit can also select the optimal display method based on the menus the user has visually preferred in the past. In this way, the optimal display method can be selected by referring to the user's past visual preferences. Past visual preferences include, but are not limited to, past selection history and survey results. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can use an AI model that takes past visual preference data as input and selects the optimal display method to perform the display.

[0053] The display unit can customize the displayed content when displaying information, taking into account the user's current visual preferences and health status. For example, if the user inputs their current visual preferences, the display unit can customize the displayed content based on that information. The display unit can also customize the displayed content based on the user inputting their health checkup results. The display unit can also customize the displayed content based on the user inputting their current health status. In this way, the displayed content can be customized by taking into account the user's current visual preferences and health status. Current visual preferences include, but are not limited to, real-time selection history and survey results. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can use an AI model that takes current visual preference and health status data as input and customizes the displayed content to display information.

[0054] The display unit can display region-specific information by considering the user's geographical location information. For example, if the user is in a specific region, the display unit will display region-specific information. If the user is traveling, the display unit can also display region-specific information for the travel destination. If the user is in their local area, the display unit can also display local area-specific information. This makes region-specific display possible by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can use an AI model that takes geographical location information as input and performs region-specific display to display information.

[0055] The display unit can analyze the user's social media activity and display relevant information at the time of display. For example, the display unit can display relevant information based on information shared by the user on social media. The display unit can also display relevant information based on information of accounts followed by the user on social media. The display unit can also display relevant information based on posts liked by the user on social media. In this way, relevant information can be displayed by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes social media activity data as input and displays relevant information.

[0056] The multilingual support unit can select the optimal language by referring to the user's past language usage history when providing multilingual support. For example, the multilingual support unit can select the optimal language based on the languages ​​the user has used in the past. The multilingual support unit can also select the optimal language by analyzing the user's past language usage history. The multilingual support unit can also select the optimal language based on the frequency of the languages ​​the user has used in the past. In this way, the optimal language can be selected by referring to the user's past language usage history. Past language usage history includes, but is not limited to, past selection history and survey results. Some or all of the above processing in the multilingual support unit may be performed using, for example, AI, or not using AI. For example, the multilingual support unit can use an AI model that takes past language usage history as input and selects the optimal language to provide support.

[0057] The multilingual support unit can prioritize region-specific languages ​​by considering the user's geographical location information when providing multilingual support. For example, if the user is in a specific region, the multilingual support unit will prioritize the language specific to that region. If the user is traveling, the multilingual support unit can also prioritize the language specific to the destination region. If the user is in their hometown, the multilingual support unit can also prioritize the language specific to their hometown. This allows for prioritizing region-specific languages ​​by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the multilingual support unit may be performed using, for example, AI, or not using AI. For example, the multilingual support unit can use an AI model that takes geographical location information as input and prioritizes region-specific languages ​​to provide support.

[0058] The proposal optimization unit can make optimal suggestions by referring to past congestion data during the proposal optimization process. For example, the proposal optimization unit can make optimal suggestions based on past congestion data. The proposal optimization unit can also analyze past congestion data and make optimal suggestions. The proposal optimization unit can also make suggestions to avoid congestion by referring to past congestion data. This makes it possible to make optimal suggestions by referring to past congestion data. Past congestion data includes, but is not limited to, past customer visit data and reservation data. Some or all of the above processing in the proposal optimization unit may be performed using, for example, AI, or not using AI. For example, the proposal optimization unit can take past congestion data as input and make suggestions using an AI model that makes optimal suggestions.

[0059] The suggestion optimization unit can customize the suggested content by considering the user's current health status and dietary restrictions during the suggestion optimization process. For example, if the user inputs their current health status, the suggestion optimization unit will customize the suggested content based on that information. If the user sets dietary restrictions, the suggestion optimization unit can also customize the suggested content based on that information. If the user inputs their health checkup results, the suggestion optimization unit can also customize the suggested content based on that information. In this way, the suggested content can be customized by considering the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the suggestion optimization unit may be performed using, for example, AI, or not using AI. For example, the suggestion optimization unit can take current health status and dietary restriction information as input and make suggestions using an AI model that customizes the suggested content.

[0060] The suggestion optimization unit can make region-specific suggestions by considering the user's geographical location information during suggestion optimization. For example, if the user is in a specific region, the suggestion optimization unit will make region-specific suggestions for that region. If the user is traveling, the suggestion optimization unit can also make region-specific suggestions for the travel destination. If the user is in their local area, the suggestion optimization unit can also make local area-specific suggestions. This makes region-specific suggestions possible by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the suggestion optimization unit may be performed using, for example, AI, or not using AI. For example, the suggestion optimization unit can make suggestions using an AI model that takes geographical location information as input and makes region-specific suggestions.

[0061] The suggestion optimization unit can analyze the user's social media activity and make relevant suggestions during the suggestion optimization process. For example, the suggestion optimization unit can make relevant suggestions based on information shared by the user on social media. The suggestion optimization unit can also make relevant suggestions based on information about accounts followed by the user on social media. The suggestion optimization unit can also make relevant suggestions based on posts liked by the user on social media. This makes it possible to make relevant suggestions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the suggestion optimization unit may be performed using AI, for example, or without AI. For example, the suggestion optimization unit can make suggestions using an AI model that takes social media activity data as input and makes relevant suggestions.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The smart order AI agent can analyze region-specific ambiguous orders by taking into account the user's geographical location. For example, if the user is in a specific region, it can analyze region-specific ambiguous orders. If the user is traveling, it can also analyze region-specific ambiguous orders. Furthermore, if the user is in their hometown, it can analyze region-specific ambiguous orders. In this way, by taking into account the user's geographical location, region-specific ambiguous orders can be analyzed. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the order analysis unit may be performed using AI or not. For example, the order analysis unit can take geographical location information as input and perform analysis using an AI model that analyzes region-specific ambiguous orders.

[0064] The smart order AI agent can analyze a user's social media activity and analyze related ambiguous orders. For example, it can analyze ambiguous orders based on information shared by the user on social media. It can also analyze ambiguous orders based on information from accounts followed by the user on social media. Furthermore, it can analyze ambiguous orders based on posts liked by the user on social media. In this way, by analyzing a user's social media activity, it is possible to analyze related ambiguous orders. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the order analysis unit may be performed using AI or not. For example, the order analysis unit can take social media activity data as input and perform analysis using an AI model that analyzes related ambiguous orders.

[0065] The smart order AI agent can suggest the most suitable menu by referring to the user's past allergy information and dietary restrictions. For example, if the user has previously entered allergy information, the agent can suggest the most suitable menu based on that information. Similarly, if the user has set dietary restrictions, the agent can suggest the most suitable menu based on that information. Furthermore, if the user has entered health checkup results, the agent can suggest the most suitable menu based on that information. This allows the agent to suggest the most suitable menu by referring to the user's past allergy information and dietary restrictions. Past allergy information includes, but is not limited to, medical records and self-reported information. Some or all of the above processing in the menu presentation unit may be performed using AI or not. For example, the menu presentation unit can use an AI model that takes past allergy information and dietary restriction information as input and suggests the most suitable menu.

[0066] The smart order AI agent can make region-specific suggestions by taking into account the user's geographical location. For example, if the user is in a specific region, it can make region-specific suggestions. If the user is traveling, it can also make region-specific suggestions for their travel destination. Furthermore, if the user is in their hometown, it can make local-specific suggestions. This enables region-specific suggestions by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use an AI model that takes geographical location information as input and makes region-specific suggestions.

[0067] The smart order AI agent can select the optimal display method by referring to the user's past visual preferences. For example, it can select the optimal display method based on the colors and designs the user has liked in the past. It can also analyze the user's past visual preferences and select the optimal display method. Furthermore, it can select the optimal display method based on the menus the user has visually preferred in the past. In this way, the optimal display method can be selected by referring to the user's past visual preferences. Past visual preferences include, but are not limited to, past selection history and survey results. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can take past visual preference data as input and display using an AI model that selects the optimal display method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The order analysis department analyzes ambiguous orders. For example, it uses natural language processing technology to analyze ambiguous orders and accurately understand the customer's intent. This allows it to make the best possible suggestions to the customer. The order analysis department can handle ambiguous orders such as, for example, "something healthy and warm." Step 2: The menu presentation unit presents menus that take allergies and dietary restrictions into account, based on the information analyzed by the order analysis unit. For example, it obtains customer allergy and dietary restriction information in conjunction with member information and presents menus based on that information. This makes it possible to suggest the most suitable menu according to the customer's health condition and dietary restrictions. Step 3: The suggestion department analyzes customer preferences and makes suggestions based on the menu presented by the menu presentation department. For example, it can learn customer preferences based on past order history and search history to make optimal suggestions. Step 4: The display unit visually displays the menu proposed by the suggestion unit. For example, images, videos, or AR technology are used to visually display the menu, allowing customers to intuitively select from the menu.

[0070] (Example of form 2) The Smart Order AI Agent according to an embodiment of the present invention is a system that revolutionizes the ordering experience by utilizing AI technology. This system solves conventional problems such as the inability to handle ambiguous orders, difficulty in making suggestions that match customer preferences and restrictions, and inability to make suggestions that take into account congestion and serving times. The Smart Order AI Agent performs order analysis using natural language processing and can appropriately understand and make suggestions even for ambiguous orders and questions. For example, it can handle ambiguous orders such as "something healthy and warm." Next, it links with member information to present menus that take allergies and dietary restrictions into consideration. This allows it to suggest the optimal menu according to the customer's health condition and dietary restrictions. Furthermore, it analyzes customer preferences and makes suggestions based on experience. It can learn customer preferences from past order history and make optimal suggestions. For example, it suggests menus that customers like based on previously ordered menus and search history. It also provides a visual menu display. By providing a life-size menu display using images, videos, and AR technology, customers can intuitively select menus. Furthermore, it supports multiple languages. This allows it to provide a smooth ordering experience for diverse customers, such as foreign tourists. Finally, it grasps congestion levels and makes efficient order suggestions. By suggesting the most suitable dishes based on the expected serving time, the system can reduce customer waiting times and improve the operational efficiency of the store. In this way, the smart order AI agent leverages AI technology to innovate the ordering experience, improving customer satisfaction, streamlining the ordering process, reducing food waste, and optimizing store operations. As a result, the smart order AI agent can significantly enhance the customer ordering experience.

[0071] The smart order AI agent according to this embodiment comprises an order analysis unit, a menu presentation unit, a suggestion unit, and a display unit. The order analysis unit analyzes ambiguous orders. The order analysis unit analyzes ambiguous orders using, for example, natural language processing technology. The order analysis unit can appropriately understand ambiguous orders and make optimal suggestions to customers. The order analysis unit can also handle ambiguous orders such as, for example, "something healthy and warm." The order analysis unit can analyze orders containing ambiguous expressions using natural language processing technology and accurately grasp the customer's intent. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or without using AI. The menu presentation unit presents menus that take into account allergies and dietary restrictions based on the information analyzed by the order analysis unit. The menu presentation unit presents menus that take into account allergies and dietary restrictions in conjunction with, for example, member information. The menu presentation unit can suggest optimal menus according to the customer's health condition and dietary restrictions. The menu presentation unit acquires, for example, customer allergy information and dietary restriction information and presents menus based on that information. The menu display unit can display menus tailored to the customer's health condition and dietary restrictions, in conjunction with member information. Some or all of the above-described processes in the menu display unit may be performed using AI, for example, or without AI. The suggestion unit analyzes the customer's preferences and makes suggestions based on the menus displayed by the menu display unit. The suggestion unit analyzes the customer's preferences and makes suggestions based on past order history, for example. The suggestion unit can learn the customer's preferences and make optimal suggestions. The suggestion unit suggests menus that the customer will like, for example, based on past ordered menus and search history. The suggestion unit can analyze past order history and make optimal suggestions based on the customer's preferences. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. The display unit visually displays the menus suggested by the suggestion unit. The display unit provides a visual menu display using images, videos, or AR technology, for example. The display unit provides a visual menu display so that the customer can intuitively choose from the menu.The display unit can, for example, display a life-size menu, which can serve as a reference for customers when choosing from the menu. The display unit can use images, videos, and AR technology to enable customers to intuitively select from the menu. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. As a result, the smart order AI agent according to this embodiment can significantly improve the customer's ordering experience.

[0072] The Order Analysis Department analyzes ambiguous orders. For example, it uses natural language processing technology to analyze ambiguous orders. Specifically, the Order Analysis Department receives text and voice data entered by customers and analyzes its content using natural language processing technology. Natural language processing technology includes morphological analysis, contextual analysis, and semantic analysis, and these are combined to accurately grasp the customer's intent. For example, for an ambiguous order such as "something healthy and warm," the keywords "healthy" and "warm" are extracted, and an appropriate menu item is identified based on these. The Order Analysis Department uses AI to learn the customer's order history and preferences, enabling it to respond flexibly to ambiguous orders. For example, if a customer who previously ordered a "healthy salad" orders "something healthy," the AI ​​will refer to that history and suggest a salad. Furthermore, the Order Analysis Department can make more accurate suggestions by analyzing the nuances and emotions of the customer's speech. For example, if a customer appears tired and orders "something to give me energy," the AI ​​will analyze their emotions and suggest a menu item suitable for energy replenishment. This allows the order analysis department to accurately understand ambiguous customer orders and make optimal suggestions.

[0073] The menu display unit presents menus that take allergies and dietary restrictions into account, based on information analyzed by the order analysis unit. Specifically, the menu display unit links with the customer's membership information to obtain allergy and dietary restriction information. For example, if a customer has a nut allergy, menus containing nuts will not be displayed. It also accommodates dietary restrictions such as vegetarianism and gluten-free diets, presenting menus tailored to the customer's health condition. The menu display unit can use AI to learn the customer's health condition and dietary restrictions and suggest the most suitable menu. For example, if a customer is on a diet, it will prioritize displaying low-calorie menus. The menu display unit can also suggest menus according to the season and time of day. For example, it will suggest cold or refreshing dishes in the summer and warm or nutritious dishes in the winter. In this way, the menu display unit can provide the most suitable menu according to the customer's health condition and dietary restrictions, thereby improving customer satisfaction.

[0074] The suggestion department analyzes customer preferences and makes suggestions based on the menus presented by the menu presentation department. Specifically, the suggestion department analyzes customer preferences based on past order history and search history. For example, it prioritizes suggesting menus that customers have ordered many times in the past or that they have frequently searched for. Furthermore, the suggestion department can use AI to learn customer preferences and make optimal suggestions for individual customers. For example, if a customer likes spicy food, it will prioritize suggesting menus that include spicy dishes. The suggestion department can also make suggestions based on the customer's current situation and mood. For example, if a customer is tired, it will suggest relaxing or nutritious dishes. In addition, the suggestion department can also suggest popular or highly-rated menu items by referring to ratings and reviews from other customers. In this way, the suggestion department can make optimal suggestions tailored to the customer's preferences and situation, thereby improving customer satisfaction.

[0075] The display unit visually displays the menu proposed by the suggestion unit. Specifically, the display unit provides a visual menu display using images, videos, and AR technology. For example, it displays menu images in high resolution so that customers can check the appearance of the dishes. It can also attract customers' interest by using videos to introduce the cooking process and the finished product. Furthermore, it uses AR technology to display the menu at actual size so that customers can intuitively understand the size and appearance of the dishes. The display unit provides a visual menu display that allows customers to intuitively choose from the menu. For example, it uses a touchscreen so that customers can swipe to select from the menu. It also supports customer selection by using other senses in addition to sight by combining feedback such as voice guidance and vibration notifications. In this way, the display unit can enable customers to intuitively choose from the menu and improve the ordering experience.

[0076] The order analysis unit can analyze ambiguous orders using natural language processing. For example, the order analysis unit analyzes ambiguous orders using natural language processing technology. The order analysis unit can appropriately understand ambiguous orders and make optimal suggestions to customers. The order analysis unit can handle ambiguous orders such as, for example, "something healthy and warm." The order analysis unit can analyze orders containing ambiguous expressions using natural language processing technology and accurately grasp the customer's intent. This improves the accuracy of ambiguous order analysis by using natural language processing. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the order analysis unit may be performed using, for example, AI, or not. For example, the order analysis unit can analyze ambiguous orders using an AI model that takes ambiguous orders as input and outputs analysis results.

[0077] The menu display unit can present menus that take into account allergies and dietary restrictions in conjunction with member information. For example, the menu display unit can present menus that take into account allergies and dietary restrictions in conjunction with member information. The menu display unit can suggest the optimal menu according to the customer's health condition and dietary restrictions. For example, the menu display unit can acquire the customer's allergy information and dietary restriction information and present menus based on that information. The menu display unit can present menus according to the customer's health condition and dietary restrictions in conjunction with member information. This allows the display of menus that take into account allergies and dietary restrictions by linking with member information. Member information includes, but is not limited to, allergy information and dietary restriction information. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not. For example, the menu display unit can present menus using an AI model that takes member information as input and outputs menus that take allergies and dietary restrictions into account.

[0078] The suggestion department can analyze customer preferences and make suggestions based on past order history. For example, the suggestion department can analyze customer preferences and make suggestions based on past order history. The suggestion department can learn customer preferences and make optimal suggestions. For example, the suggestion department can suggest menus that customers like based on previously ordered menus and search history. The suggestion department can analyze past order history and make optimal suggestions based on customer preferences. This makes it possible to make optimal suggestions by analyzing customer preferences based on past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not using AI. For example, the suggestion department can make suggestions using an AI model that takes past order history as input and outputs suggestions based on customer preferences.

[0079] The display unit can display a visual menu using images, videos, and AR technology. For example, the display unit displays a visual menu using images, videos, and AR technology. The display unit provides a visual menu display so that customers can intuitively select from the menu. For example, the display unit can display a life-size menu for customers to use as a reference when choosing from the menu. The display unit uses images, videos, and AR technology to enable customers to intuitively select from the menu. This allows for a visual display of the menu using images, videos, and AR technology. Images, videos, and AR technology include, but are not limited to, 3D models and interactive content. Some or all of the above processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can display a visual menu using an AI model that generates menu displays using images, videos, and AR technology.

[0080] The smart order AI agent is equipped with a multilingual support unit that handles orders and guidance in multiple languages. The multilingual support unit, for example, handles orders and guidance in multiple languages. The multilingual support unit can provide a smooth ordering experience to diverse customers, such as foreign tourists. Thus, by equipping the unit with a multilingual support unit, orders and guidance in multiple languages ​​become possible. Multilingual support includes, but is not limited to, the types of languages ​​supported and translation methods. Some or all of the processing described above in the multilingual support unit may be performed using AI, for example, or not using AI. For example, the multilingual support unit can perform multilingual support using an AI model that takes the customer's language as input and outputs orders and guidance in the corresponding language.

[0081] The smart order AI agent includes a suggestion optimization unit that understands the congestion situation and proposes the optimal dishes based on the expected serving time. The suggestion optimization unit, for example, understands the congestion situation and proposes the optimal dishes based on the expected serving time. The suggestion optimization unit can reduce customer waiting times and improve the operational efficiency of the store. Thus, by including the suggestion optimization unit, it is possible to understand the congestion situation and propose the optimal dishes based on the expected serving time. Congestion information includes, but is not limited to, real-time data and historical data. Some or all of the processing described above in the suggestion optimization unit may be performed using, for example, AI, or not using AI. For example, the suggestion optimization unit can take congestion data as input and make suggestions using an AI model that proposes the optimal dishes based on the expected serving time.

[0082] The order analysis unit can estimate the user's emotions and improve the accuracy of analyzing ambiguous orders based on the estimated emotions. For example, if the user is stressed, the AI ​​in the order analysis unit can suggest ways to relax and clarify the ambiguous order. If the user is in a hurry, the AI ​​in the order analysis unit can perform a rapid analysis and present the most appropriate suggestions. If the user is enjoying themselves, the AI ​​in the order analysis unit can also suggest ways to match the user's mood and clarify the ambiguous order. This improves the accuracy of analyzing ambiguous orders based on the user's emotions, enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the order analysis unit may be performed using AI, for example, or without AI. For example, the order analysis unit can take user emotion data as input and perform analysis using an AI model that improves the accuracy of analyzing ambiguous orders.

[0083] The order analysis unit can optimize the interpretation of ambiguous orders by referring to the user's past order history during order analysis. For example, the order analysis unit can clarify ambiguous orders based on menus the user has ordered in the past. The order analysis unit can also prioritize suggesting frequently ordered menus based on the user's past order history. The order analysis unit can also optimize the interpretation of ambiguous orders by analyzing the user's past order history. This allows for the optimization of ambiguous order interpretation by referring to the user's past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or not using AI. For example, the order analysis unit can take past order history as input and perform analysis using an AI model that optimizes the interpretation of ambiguous orders.

[0084] The order analysis unit can adjust the analysis results when analyzing an order, taking into account the user's current health status and dietary restrictions. For example, if the user inputs their health checkup results, the AI ​​will analyze ambiguous orders based on that information. The order analysis unit can also analyze ambiguous orders based on information if the user sets dietary restrictions. The order analysis unit can also analyze ambiguous orders based on information if the user inputs their current health status. This allows the analysis results of ambiguous orders to be adjusted by taking into account the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or not using AI. For example, the order analysis unit can perform analysis using an AI model that takes current health status and dietary restriction information as input and adjusts the analysis results of ambiguous orders.

[0085] The order analysis unit can estimate the user's emotions and determine the priority of ambiguous orders based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize suggestions to help them relax. If the user is in a hurry, the AI ​​can also prioritize suggestions for quick responses. If the user is enjoying themselves, the AI ​​can also prioritize suggestions that match the user's mood. This allows for more appropriate suggestions by determining the priority of ambiguous orders based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the order analysis unit may be performed using AI or not. For example, the order analysis unit can take user emotion data as input and perform analysis using an AI model that determines the priority of ambiguous orders.

[0086] The order analysis unit can analyze region-specific ambiguous orders by considering the user's geographical location information during order analysis. For example, if the user is in a specific region, the order analysis unit can analyze region-specific ambiguous orders. If the user is traveling, the order analysis unit can also analyze region-specific ambiguous orders. If the user is in their hometown, the order analysis unit can also analyze local-specific ambiguous orders. In this way, region-specific ambiguous orders can be analyzed by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the order analysis unit may be performed using, for example, AI, or not using AI. For example, the order analysis unit can take geographical location information as input and perform analysis using an AI model that analyzes region-specific ambiguous orders.

[0087] The order analysis unit can analyze users' social media activity during order analysis and analyze related ambiguous orders. For example, the order analysis unit can analyze ambiguous orders based on information shared by users on social media. The order analysis unit can also analyze ambiguous orders based on information from accounts that users follow on social media. The order analysis unit can also analyze ambiguous orders based on posts that users have liked on social media. In this way, related ambiguous orders can be analyzed by analyzing users' social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the order analysis unit may be performed using AI, for example, or without AI. For example, the order analysis unit can take social media activity data as input and perform analysis using an AI model that analyzes related ambiguous orders.

[0088] The menu presentation unit can estimate the user's emotions and adjust the menu presentation method based on the estimated emotions. For example, if the user is relaxed, the menu presentation unit will present the menu at a leisurely pace. If the user is in a hurry, the menu presentation unit can also present the menu quickly. If the user is enjoying themselves, the menu presentation unit can also present a visually appealing menu. By adjusting the menu presentation method based on the user's emotions, a more appropriate menu presentation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the menu presentation unit may be performed using AI, for example, or without AI. For example, the menu presentation unit can use an AI model that takes user emotion data as input and adjusts the menu presentation method to make a presentation.

[0089] The menu display unit can present the most suitable menu by referring to the user's past allergy information and dietary restrictions when displaying menus. For example, if the user has previously entered allergy information, the menu display unit can present the most suitable menu based on that information. If the user has set dietary restrictions, the menu display unit can also present the most suitable menu based on that information. If the user has entered health checkup results, the menu display unit can also present the most suitable menu based on that information. In this way, the optimal menu can be presented by referring to the user's past allergy information and dietary restrictions. Past allergy information includes, but is not limited to, medical records and self-declarations. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not using AI. For example, the menu display unit can use an AI model that takes past allergy information and dietary restriction information as input and presents the optimal menu.

[0090] The menu display unit can customize the menu when presenting it, taking into account the user's current health status and dietary restrictions. For example, if the user enters their current health status, the menu display unit can customize the menu based on that information. If the user sets dietary restrictions, the menu display unit can also customize the menu based on that information. If the user enters their health checkup results, the menu display unit can also customize the menu based on that information. In this way, the menu can be customized by taking into account the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not using AI. For example, the menu display unit can use an AI model that takes current health status and dietary restriction information as input and customize the menu.

[0091] The menu presentation unit can estimate the user's emotions and determine the priority of menu items based on the estimated emotions. For example, if the user is relaxed, the menu presentation unit will present the menu at a leisurely pace. If the user is in a hurry, the menu presentation unit can also present the menu quickly. If the user is enjoying themselves, the menu presentation unit can also present visually appealing menu items. This allows for more appropriate menu presentation by determining the priority of menu items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the menu presentation unit may be performed using AI, for example, or without AI. For example, the menu presentation unit can take user emotion data as input and use an AI model to determine the priority of menu items to make presentations.

[0092] The menu display unit can present region-specific menus by considering the user's geographical location information when displaying menus. For example, if the user is in a specific region, the menu display unit can present region-specific menus. If the user is traveling, the menu display unit can also present region-specific menus for the travel destination. If the user is in their local area, the menu display unit can also present local menus. In this way, region-specific menus can be presented by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the menu display unit may be performed using, for example, AI, or not using AI. For example, the menu display unit can use an AI model that takes geographical location information as input and presents region-specific menus to make the presentation.

[0093] The menu presentation unit can analyze the user's social media activity and present relevant menu items when presenting a menu. For example, the menu presentation unit can present relevant menu items based on information the user has shared on social media. The menu presentation unit can also present relevant menu items based on information about accounts the user follows on social media. The menu presentation unit can also present relevant menu items based on posts the user has liked on social media. In this way, relevant menu items can be presented by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the menu presentation unit may be performed using, for example, AI, or not using AI. For example, the menu presentation unit can use an AI model that takes social media activity data as input and presents relevant menu items.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions at a relaxed pace. If the user is in a hurry, the suggestion unit can present suggestions quickly. If the user is having fun, the suggestion unit can present visually appealing suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI, or not. For example, the suggestion unit can take user emotion data as input and make suggestions using an AI model that adjusts the way suggestions are presented.

[0095] The suggestion unit can refer to past order history to best reflect the user's preferences when making suggestions. For example, the suggestion unit can make optimal suggestions based on menus the user has ordered in the past. The suggestion unit can also prioritize suggesting frequently ordered menus from the user's past order history. The suggestion unit can also analyze the user's past order history to make optimal suggestions. This makes it possible to make suggestions that best reflect the user's preferences by referring to past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can use an AI model that takes past order history as input and makes suggestions based on the user's preferences to make suggestions.

[0096] The suggestion unit can customize its suggestions by taking into account the user's current health status and dietary restrictions. For example, if the user inputs their current health status, the suggestion unit will customize the suggestions based on that information. If the user sets dietary restrictions, the suggestion unit can also customize the suggestions based on that information. If the user inputs their health checkup results, the suggestion unit can also customize the suggestions based on that information. In this way, the suggestions can be customized by taking into account the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can take current health status and dietary restriction information as input and make suggestions using an AI model that customizes the suggestions.

[0097] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will make suggestions at a relaxed pace. If the user is in a hurry, the suggestion unit can also make suggestions quickly. If the user is having fun, the suggestion unit can also make visually appealing suggestions. This allows for more appropriate suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can take user emotion data as input and make suggestions using an AI model that determines the priority of suggestions.

[0098] The suggestion unit can make region-specific suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will make region-specific suggestions for that region. If the user is traveling, the suggestion unit can also make region-specific suggestions for the travel destination. If the user is in their local area, the suggestion unit can also make local area-specific suggestions. This makes region-specific suggestions possible by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes geographical location information as input and makes region-specific suggestions.

[0099] The suggestion unit can analyze a user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on information shared by the user on social media. The suggestion unit can also make relevant suggestions based on information about accounts followed by the user on social media. The suggestion unit can also make relevant suggestions based on posts liked by the user on social media. This makes it possible to make relevant suggestions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can make suggestions using an AI model that takes social media activity data as input and makes relevant suggestions.

[0100] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display unit will display at a relaxed pace. If the user is in a hurry, the display unit can also display quickly. If the user is having fun, the display unit can also display in a visually pleasing way. By adjusting the display method based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take user emotion data as input and perform the display using an AI model that adjusts the display method.

[0101] The display unit can select the optimal display method by referring to the user's past visual preferences when displaying information. For example, the display unit can select the optimal display method based on the colors and designs the user has preferred in the past. The display unit can also analyze the user's past visual preferences and select the optimal display method. The display unit can also select the optimal display method based on the menus the user has visually preferred in the past. In this way, the optimal display method can be selected by referring to the user's past visual preferences. Past visual preferences include, but are not limited to, past selection history and survey results. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can use an AI model that takes past visual preference data as input and selects the optimal display method to perform the display.

[0102] The display unit can customize the displayed content when displaying information, taking into account the user's current visual preferences and health status. For example, if the user inputs their current visual preferences, the display unit can customize the displayed content based on that information. The display unit can also customize the displayed content based on the user inputting their health checkup results. The display unit can also customize the displayed content based on the user inputting their current health status. In this way, the displayed content can be customized by taking into account the user's current visual preferences and health status. Current visual preferences include, but are not limited to, real-time selection history and survey results. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can use an AI model that takes current visual preference and health status data as input and customizes the displayed content to display information.

[0103] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, the display unit will display at a relaxed pace. If the user is in a hurry, the display unit can also display quickly. If the user is having fun, the display unit can also display visually enjoyable content. This allows for more appropriate displays by determining the display priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can take user emotion data as input and perform displays using an AI model that determines the display priority.

[0104] The display unit can display region-specific information by considering the user's geographical location information. For example, if the user is in a specific region, the display unit will display region-specific information. If the user is traveling, the display unit can also display region-specific information for the travel destination. If the user is in their local area, the display unit can also display local area-specific information. This makes region-specific display possible by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can use an AI model that takes geographical location information as input and performs region-specific display to display information.

[0105] The display unit can analyze the user's social media activity and display relevant information at the time of display. For example, the display unit can display relevant information based on information shared by the user on social media. The display unit can also display relevant information based on information of accounts followed by the user on social media. The display unit can also display relevant information based on posts liked by the user on social media. In this way, relevant information can be displayed by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes social media activity data as input and displays relevant information.

[0106] The multilingual support unit can estimate the user's emotions and adjust the multilingual support expression based on the estimated emotions. For example, if the user is relaxed, the multilingual support unit will provide multilingual support at a relaxed pace. If the user is in a hurry, the multilingual support unit can also provide multilingual support quickly. If the user is enjoying themselves, the multilingual support unit can provide visually engaging multilingual support. By adjusting the multilingual support expression based on the user's emotions, more appropriate multilingual support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can take user emotion data as input and use an AI model to adjust the multilingual support expression to provide support.

[0107] The multilingual support unit can select the optimal language by referring to the user's past language usage history when providing multilingual support. For example, the multilingual support unit can select the optimal language based on the languages ​​the user has used in the past. The multilingual support unit can also select the optimal language by analyzing the user's past language usage history. The multilingual support unit can also select the optimal language based on the frequency of the languages ​​the user has used in the past. In this way, the optimal language can be selected by referring to the user's past language usage history. Past language usage history includes, but is not limited to, past selection history and survey results. Some or all of the above processing in the multilingual support unit may be performed using, for example, AI, or not using AI. For example, the multilingual support unit can use an AI model that takes past language usage history as input and selects the optimal language to provide support.

[0108] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the user is relaxed, the multilingual support unit will provide multilingual support at a relaxed pace. If the user is in a hurry, the multilingual support unit can also provide multilingual support quickly. If the user is enjoying themselves, the multilingual support unit can provide visually engaging multilingual support. This allows for more appropriate multilingual support by determining the priority of multilingual support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can take user emotion data as input and use an AI model to determine the priority of multilingual support to provide support.

[0109] The multilingual support unit can prioritize region-specific languages ​​by considering the user's geographical location information when providing multilingual support. For example, if the user is in a specific region, the multilingual support unit will prioritize the language specific to that region. If the user is traveling, the multilingual support unit can also prioritize the language specific to the destination region. If the user is in their hometown, the multilingual support unit can also prioritize the language specific to their hometown. This allows for prioritizing region-specific languages ​​by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the multilingual support unit may be performed using, for example, AI, or not using AI. For example, the multilingual support unit can use an AI model that takes geographical location information as input and prioritizes region-specific languages ​​to provide support.

[0110] The suggestion optimization unit can estimate the user's emotions and adjust the suggestion optimization method based on the estimated user emotions. For example, if the user is relaxed, the suggestion optimization unit will make suggestions at a relaxed pace. If the user is in a hurry, the suggestion optimization unit can also make suggestions quickly. If the user is having fun, the suggestion optimization unit can also make visually enjoyable suggestions. In this way, by adjusting the suggestion optimization method based on the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion optimization unit may be performed using AI, for example, or without AI. For example, the suggestion optimization unit can make suggestions using an AI model that takes user emotion data as input and adjusts the suggestion optimization method.

[0111] The proposal optimization unit can make optimal suggestions by referring to past congestion data during the proposal optimization process. For example, the proposal optimization unit can make optimal suggestions based on past congestion data. The proposal optimization unit can also analyze past congestion data and make optimal suggestions. The proposal optimization unit can also make suggestions to avoid congestion by referring to past congestion data. This makes it possible to make optimal suggestions by referring to past congestion data. Past congestion data includes, but is not limited to, past customer visit data and reservation data. Some or all of the above processing in the proposal optimization unit may be performed using, for example, AI, or not using AI. For example, the proposal optimization unit can take past congestion data as input and make suggestions using an AI model that makes optimal suggestions.

[0112] The suggestion optimization unit can customize the suggested content by considering the user's current health status and dietary restrictions during the suggestion optimization process. For example, if the user inputs their current health status, the suggestion optimization unit will customize the suggested content based on that information. If the user sets dietary restrictions, the suggestion optimization unit can also customize the suggested content based on that information. If the user inputs their health checkup results, the suggestion optimization unit can also customize the suggested content based on that information. In this way, the suggested content can be customized by considering the user's current health status and dietary restrictions. Current health status includes, but is not limited to, health checkup results and self-reported information. Some or all of the above processing in the suggestion optimization unit may be performed using, for example, AI, or not using AI. For example, the suggestion optimization unit can take current health status and dietary restriction information as input and make suggestions using an AI model that customizes the suggested content.

[0113] The suggestion optimization unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion optimization unit will make suggestions at a relaxed pace. If the user is in a hurry, the suggestion optimization unit can also make suggestions quickly. If the user is having fun, the suggestion optimization unit can also make visually appealing suggestions. This allows for more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion optimization unit may be performed using AI, for example, or without AI. For example, the suggestion optimization unit can take user emotion data as input and make suggestions using an AI model that determines the priority of suggestions.

[0114] The suggestion optimization unit can make region-specific suggestions by considering the user's geographical location information during suggestion optimization. For example, if the user is in a specific region, the suggestion optimization unit will make region-specific suggestions for that region. If the user is traveling, the suggestion optimization unit can also make region-specific suggestions for the travel destination. If the user is in their local area, the suggestion optimization unit can also make local area-specific suggestions. This makes region-specific suggestions possible by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the suggestion optimization unit may be performed using, for example, AI, or not using AI. For example, the suggestion optimization unit can make suggestions using an AI model that takes geographical location information as input and makes region-specific suggestions.

[0115] The suggestion optimization unit can analyze the user's social media activity and make relevant suggestions during the suggestion optimization process. For example, the suggestion optimization unit can make relevant suggestions based on information shared by the user on social media. The suggestion optimization unit can also make relevant suggestions based on information about accounts followed by the user on social media. The suggestion optimization unit can also make relevant suggestions based on posts liked by the user on social media. This makes it possible to make relevant suggestions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the suggestion optimization unit may be performed using AI, for example, or without AI. For example, the suggestion optimization unit can make suggestions using an AI model that takes social media activity data as input and makes relevant suggestions.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The smart order AI agent can estimate the user's emotions and perform order analysis based on those emotions. For example, if the user is stressed, the AI ​​can offer suggestions to help them relax and clarify vague orders. If the user is in a hurry, the AI ​​can perform a quick analysis and present the most appropriate suggestions. Furthermore, if the user is enjoying themselves, the AI ​​can offer suggestions tailored to the user's mood and clarify vague orders. This improves the accuracy of analyzing vague orders based on the user's emotions, enabling more appropriate suggestions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the order analysis unit may be performed using AI or not. For example, the order analysis unit can take user emotion data as input and perform analysis using an AI model that improves the accuracy of analyzing vague orders.

[0118] The smart order AI agent can analyze region-specific ambiguous orders by taking into account the user's geographical location. For example, if the user is in a specific region, it can analyze region-specific ambiguous orders. If the user is traveling, it can also analyze region-specific ambiguous orders. Furthermore, if the user is in their hometown, it can analyze region-specific ambiguous orders. In this way, by taking into account the user's geographical location, region-specific ambiguous orders can be analyzed. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the order analysis unit may be performed using AI or not. For example, the order analysis unit can take geographical location information as input and perform analysis using an AI model that analyzes region-specific ambiguous orders.

[0119] The smart order AI agent can analyze a user's social media activity and analyze related ambiguous orders. For example, it can analyze ambiguous orders based on information shared by the user on social media. It can also analyze ambiguous orders based on information from accounts followed by the user on social media. Furthermore, it can analyze ambiguous orders based on posts liked by the user on social media. In this way, by analyzing a user's social media activity, it is possible to analyze related ambiguous orders. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the order analysis unit may be performed using AI or not. For example, the order analysis unit can take social media activity data as input and perform analysis using an AI model that analyzes related ambiguous orders.

[0120] The smart order AI agent can estimate the user's emotions and adjust the menu presentation method based on the estimated emotions. For example, if the user is relaxed, the menu can be presented at a leisurely pace. If the user is in a hurry, the menu can be presented quickly. Furthermore, if the user is enjoying themselves, a visually appealing menu can be presented. By adjusting the menu presentation method based on the user's emotions, a more appropriate menu presentation becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the menu presentation unit may be performed using AI or not. For example, the menu presentation unit can use an AI model that takes user emotion data as input and adjusts the menu presentation method to make the presentation.

[0121] The smart order AI agent can suggest the most suitable menu by referring to the user's past allergy information and dietary restrictions. For example, if the user has previously entered allergy information, the agent can suggest the most suitable menu based on that information. Similarly, if the user has set dietary restrictions, the agent can suggest the most suitable menu based on that information. Furthermore, if the user has entered health checkup results, the agent can suggest the most suitable menu based on that information. This allows the agent to suggest the most suitable menu by referring to the user's past allergy information and dietary restrictions. Past allergy information includes, but is not limited to, medical records and self-reported information. Some or all of the above processing in the menu presentation unit may be performed using AI or not. For example, the menu presentation unit can use an AI model that takes past allergy information and dietary restriction information as input and suggests the most suitable menu.

[0122] The smart order AI agent can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can make suggestions at a leisurely pace. If the user is in a hurry, it can make suggestions quickly. Furthermore, if the user is having fun, it can make visually appealing suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can take user emotion data as input and make suggestions using an AI model that adjusts the way suggestions are presented.

[0123] The smart order AI agent can make region-specific suggestions by taking into account the user's geographical location. For example, if the user is in a specific region, it can make region-specific suggestions. If the user is traveling, it can also make region-specific suggestions for their travel destination. Furthermore, if the user is in their hometown, it can make local-specific suggestions. This enables region-specific suggestions by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use an AI model that takes geographical location information as input and makes region-specific suggestions.

[0124] The smart order AI agent can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display can be shown at a leisurely pace. If the user is in a hurry, the display can be shown quickly. Furthermore, if the user is having fun, the display can be shown in a visually pleasing way. By adjusting the display method based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can take user emotion data as input and perform the display using an AI model that adjusts the display method.

[0125] The smart order AI agent can select the optimal display method by referring to the user's past visual preferences. For example, it can select the optimal display method based on the colors and designs the user has liked in the past. It can also analyze the user's past visual preferences and select the optimal display method. Furthermore, it can select the optimal display method based on the menus the user has visually preferred in the past. In this way, the optimal display method can be selected by referring to the user's past visual preferences. Past visual preferences include, but are not limited to, past selection history and survey results. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can take past visual preference data as input and display using an AI model that selects the optimal display method.

[0126] The smart order AI agent can estimate the user's emotions and adjust the multilingual expression based on those emotions. For example, if the user is relaxed, it can provide multilingual support at a leisurely pace. If the user is in a hurry, it can provide multilingual support quickly. Furthermore, if the user is enjoying themselves, it can provide visually engaging multilingual support. By adjusting the multilingual expression based on the user's emotions, more appropriate multilingual support becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI or not. For example, the multilingual support unit can take user emotion data as input and use an AI model to adjust the multilingual expression to provide support.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The order analysis department analyzes ambiguous orders. For example, it uses natural language processing technology to analyze ambiguous orders and accurately understand the customer's intent. This allows it to make the best possible suggestions to the customer. The order analysis department can handle ambiguous orders such as, for example, "something healthy and warm." Step 2: The menu presentation unit presents menus that take allergies and dietary restrictions into account, based on the information analyzed by the order analysis unit. For example, it obtains customer allergy and dietary restriction information in conjunction with member information and presents menus based on that information. This makes it possible to suggest the most suitable menu according to the customer's health condition and dietary restrictions. Step 3: The suggestion department analyzes customer preferences and makes suggestions based on the menu presented by the menu presentation department. For example, it can learn customer preferences based on past order history and search history to make optimal suggestions. Step 4: The display unit visually displays the menu proposed by the suggestion unit. For example, images, videos, or AR technology are used to visually display the menu, allowing customers to intuitively select from the menu.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the order analysis unit, menu presentation unit, suggestion unit, display unit, multilingual support unit, and suggestion optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the order analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes ambiguous orders using natural language processing technology. The menu presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents menus that take allergies and dietary restrictions into consideration. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes customer preferences based on past order history and makes suggestions. The display unit is implemented by the control unit 46A of the smart device 14 and displays menus visually using images, videos, and AR technology. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports orders and guidance in multiple languages. The suggestion optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes by understanding the congestion situation and anticipating the serving time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 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.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the 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.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 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.

[0148] Each of the multiple elements described above, including the order analysis unit, menu presentation unit, suggestion unit, display unit, multilingual support unit, and suggestion optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the order analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes ambiguous orders using natural language processing technology. The menu presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents menus that take into account allergies and dietary restrictions. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes customer preferences based on past order history and makes suggestions. The display unit is implemented by the control unit 46A of the smart glasses 214 and displays menus visually using images, videos, and AR technology. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports orders and guidance in multiple languages. The suggestion optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes by understanding the congestion situation and anticipating the serving time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the order analysis unit, menu presentation unit, suggestion unit, display unit, multilingual support unit, and suggestion optimization unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the order analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes ambiguous orders using natural language processing technology. The menu presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents menus that take allergies and dietary restrictions into consideration. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes customer preferences based on past order history and makes suggestions. The display unit is implemented by the control unit 46A of the headset terminal 314 and displays menus visually using images, videos, and AR technology. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports orders and guidance in multiple languages. The suggestion optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes by understanding the congestion situation and anticipating the serving time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the order analysis unit, menu presentation unit, suggestion unit, display unit, multilingual support unit, and suggestion optimization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the order analysis unit is implemented by the control unit 46A of the robot 414 and analyzes ambiguous orders using natural language processing technology. The menu presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents menus that take into account allergies and dietary restrictions. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes customer preferences based on past order history and makes suggestions. The display unit is implemented by the control unit 46A of the robot 414 and displays menus visually using images, videos, and AR technology. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports orders and guidance in multiple languages. The suggestion optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the optimal dishes by understanding the congestion situation and anticipating the serving time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The order analysis department analyzes ambiguous orders, A menu presentation unit presents menus that take into account allergies and dietary restrictions based on the information analyzed by the aforementioned order analysis unit, A suggestion unit analyzes customer preferences and makes suggestions based on the menu presented by the menu presentation unit, The system includes a display unit that visually displays the menu proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned order analysis unit, Analyzing ambiguous orders using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The menu display unit is, The system uses member information to provide menus that take allergies and dietary restrictions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on past order history, we analyze customer preferences and make suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Visual menu displays are created using images, videos, and AR technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) Equipped with a multilingual support section to handle orders and instructions in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 7) The restaurant has a proposal optimization department that assesses congestion levels and suggests the most suitable dishes based on estimated serving times. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned order analysis unit, It estimates user sentiment and improves the accuracy of analyzing ambiguous orders based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned order analysis unit, During order analysis, the system optimizes the interpretation of ambiguous orders by referencing the user's past order history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned order analysis unit, When analyzing orders, the analysis results are adjusted to take into account the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned order analysis unit, It estimates the user's emotions and determines the priority of ambiguous orders based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned order analysis unit, During order analysis, the system considers the user's geographical location to analyze region-specific and ambiguous orders. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned order analysis unit, During order analysis, we analyze users' social media activity and identify related, ambiguous orders. The system described in Appendix 1, characterized by the features described herein. (Note 14) The menu display unit is, The system estimates the user's emotions and adjusts the menu presentation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The menu display unit is, When presenting the menu, the system will refer to the user's past allergy information and dietary restrictions to suggest the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 16) The menu display unit is, When presenting the menu, customize it to take into account the user's current health condition and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The menu display unit is, It estimates the user's emotions and determines menu priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The menu display unit is, When presenting the menu, the system takes the user's geographical location into consideration and displays region-specific menu items. The system described in Appendix 1, characterized by the features described herein. (Note 19) The menu display unit is, When presenting the menu, the system analyzes the user's social media activity and suggests relevant menu items. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, refer to past order history to best reflect the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, the suggestions are customized to take into account the user's current health condition and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, we take the user's geographical location into consideration to provide region-specific recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying content, the system selects the optimal display method by referencing the user's past visual preferences. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying content, the displayed content is customized to take into account the user's current visual preferences and health status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying content, region-specific information is displayed, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying content, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the multilingual expression based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned multilingual support unit is When supporting multiple languages, the system selects the most suitable language by referring to the user's past language usage history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned multilingual support unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned multilingual support unit is When supporting multiple languages, prioritize region-specific languages ​​by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal optimization unit, It estimates the user's emotions and adjusts how suggestions are optimized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal optimization unit, When optimizing proposals, we refer to past congestion data to make the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal optimization unit, When optimizing suggestions, the suggestions are customized to take into account the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal optimization unit, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal optimization unit, When optimizing suggestions, the system takes the user's geographical location into consideration to provide region-specific suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal optimization unit, When optimizing suggestions, we analyze users' social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. The order analysis department analyzes ambiguous orders, A menu presentation unit presents menus that take into account allergies and dietary restrictions based on the information analyzed by the aforementioned order analysis unit, A suggestion unit analyzes customer preferences and makes suggestions based on the menu presented by the menu presentation unit, The system includes a display unit that visually displays the menu proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned order analysis unit, Analyzing ambiguous orders using natural language processing. The system according to feature 1.

3. The menu display unit is, The system uses member information to provide menus that take allergies and dietary restrictions into consideration. The system according to feature 1.

4. The aforementioned proposal section is, Based on past order history, we analyze customer preferences and make suggestions. The system according to feature 1.

5. The aforementioned display unit is Visual menu displays are created using images, videos, and AR technology. The system according to feature 1.

6. Equipped with a multilingual support section to handle orders and instructions in multiple languages. The system according to feature 1.

7. The restaurant has a proposal optimization department that assesses congestion levels and suggests the most suitable dishes based on estimated serving times. The system according to feature 1.

8. The aforementioned order analysis unit, It estimates user sentiment and improves the accuracy of analyzing ambiguous orders based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned order analysis unit, During order analysis, the system optimizes the interpretation of ambiguous orders by referencing the user's past order history. The system according to feature 1.

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