System

Abstract

An object of a system according to an embodiment is to generate an attractive promotion of a restaurant and improve a user's meal experience.SOLUTION: A system according to an embodiment includes a promotion generation unit, a description providing unit, and an experience improvement unit. The promotion generation unit generates an attractive promotion of the restaurant by using the generated AI. The explanation providing unit provides the promotion generated by the promotion generating unit with a rich explanation and beautiful images. The experience enhancer enhances the user's dining experience based on the information provided by the explanation provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that restaurant promotions are not carried out effectively and the dining experience of users is not sufficiently improved.

[0005] The system according to the embodiment aims to create attractive promotions for restaurants and improve the dining experience of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a promotion generation unit, an explanation providing unit, and an experience improvement unit. The promotion generation unit uses a generation AI to generate attractive promotions for restaurants. The explanation providing unit provides the promotions generated by the promotion generation unit with rich explanations and beautiful images. The experience improvement unit improves the user's dining experience based on the information provided by the explanation providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate attractive promotions for restaurants and enhance the dining experience of users. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A food delivery service according to an embodiment of the present invention is a system that efficiently generates attractive promotions for restaurants and dramatically improves the dining experience for users. This allows the food delivery service to dramatically improve the dining experience for users.

[0029] A food delivery service according to an embodiment includes a promotion generation unit, an explanation providing unit, and an experience improvement unit. The promotion generation unit uses a generation AI to generate attractive promotions for restaurants. For example, the generation AI generates promotions that provide detailed explanations of the chef's preferences, the origins of the menu, the characteristics of the ingredients used, and so on, based on information provided by the restaurant. The generation AI can also provide detailed explanations of the history and cultural background of the cuisine, the chef's experience and techniques, and provide beautiful images related to the cuisine. For example, the generation AI receives prompts containing information about the chef's preferences and the origins of the menu, and generates promotions based on the prompts. The explanation providing unit provides the promotions generated by the promotion generation unit with rich explanations and beautiful images. For example, the explanation providing unit interactively provides the passion and story behind the cuisine with rich explanations and beautiful images. This allows users to deeply understand the significance and appeal of the cuisine. The experience improvement unit improves the user's dining experience based on the information provided by the explanation providing unit. For example, the experience improvement unit increases the user's interest in the cuisine by learning the story behind the cuisine, making the meal more satisfying. Furthermore, by understanding the significance and appeal of the food, the experience improvement unit makes the meal an experience that not only stimulates the five senses but also resonates with the heart. As a result, the food delivery service according to the embodiment improves the effectiveness of restaurant promotions, making the user's dining experience more satisfying. For example, the promotion generation unit generates attractive promotions for restaurants, the explanation provision unit provides the promotions with rich explanations and beautiful images, and the experience improvement unit improves the user's dining experience.

[0030] The promotion generation unit can analyze the cooking process in real time and generate promotions based on data such as temperature and time. For example, the promotion generation unit uses a generation AI to analyze the cooking process in real time and collect data such as temperature and time. For example, the promotion reflects the characteristics and preferences of the dish based on temperature changes and cooking time during cooking. The promotion generation unit also analyzes data from the cooking process and evaluates the doneness and quality of the dish. For example, it generates promotions that ensure the deliciousness of the dish by maintaining appropriate temperature control and cooking time. The promotion generation unit also generates promotions that emphasize the appeal of the dish based on data from the cooking process. For example, it reflects the flavor and texture obtained by cooking at a specific temperature and time in the promotion. In this way, the appeal of the dish can be emphasized by generating promotions based on data from the cooking process.

[0031] The promotion generation unit can learn promotional data from other restaurants and generate promotions in different styles. For example, the generation AI learns promotional data from other restaurants and generates promotions in different styles. For example, it generates promotions with a casual atmosphere or promotions with an upscale feel. The promotion generation unit also analyzes promotional data from other restaurants and extracts elements of successful promotions. For example, it generates unique promotions by referring to specific visuals or catchphrases. The promotion generation unit also utilizes promotional data from other restaurants to generate promotions in different styles. For example, it generates unique promotions by incorporating promotional styles from different cultures or regions. In this way, it is possible to generate promotions in different styles by learning promotional data from other restaurants.

[0032] The promotion generation unit can analyze a user's past order history and generate promotions optimized for each individual user. For example, the promotion generation unit uses a generation AI to analyze a user's past order history and generate promotions optimized for each individual user. For example, a personalized promotion is generated based on the user's favorite dishes and ingredients. The promotion generation unit also generates an attractive promotion based on the user's order history. For example, new menu items and special offers related to dishes the user has previously ordered are reflected in the promotion. The promotion generation unit also analyzes the order history to generate promotions optimized for each individual user. For example, a personalized promotion is provided taking into account the user's preferences and eating habits. In this way, a promotion optimized for each individual user can be generated by analyzing the user's past order history.

[0033] The explanation providing unit can generate a 3D model of a dish and provide a function that allows the user to interactively rotate and enlarge it. For example, the explanation providing unit allows the generation AI to generate a 3D model of a dish and provide a function that allows the user to interactively rotate and enlarge it. For example, it allows the user to check the details of the dish from 360 degrees. The explanation providing unit also generates a 3D model of a dish and provides a function that allows the user to interactively operate it. For example, it allows the user to enlarge and view specific parts of the dish. The explanation providing unit also provides a function that allows the generation AI to generate a 3D model of a dish and allow the user to interactively experience it. For example, it allows the user to check the internal structure of the dish and the arrangement of ingredients. This allows the user to interactively check the details of the dish.

[0034] The explanation providing unit can simulate the aroma and sounds of cooking to provide the user with an interactive experience that stimulates the five senses. For example, the explanation providing unit has the generation AI simulate the aroma of cooking to provide the user with an experience that stimulates the five senses. For example, the explanation providing unit can reproduce the aroma of a specific dish so that the user can smell that aroma. The explanation providing unit can also have the generation AI simulate the sounds of cooking to provide the user with an experience that stimulates the five senses. For example, the explanation providing unit can reproduce the sounds of food being grilled or simmered so that the user can hear those sounds. The explanation providing unit can also have the generation AI simulate the aroma and sounds of cooking to provide the user with an interactive experience. For example, the explanation providing unit can allow the user to check the details of the food while smelling the aroma and sounds of the food. This makes it possible to provide the user with an interactive experience that stimulates the five senses.

[0035] The explanation providing unit introduces cuisine from different cultures and regions, allowing the user to experience diverse food cultures. For example, the generation AI introduces cuisine from different cultures and regions, allowing the user to experience diverse food cultures. For example, it provides detailed explanations of cuisine from different regions, such as Asian cuisine and European cuisine. In addition, to introduce cuisine from different cultures and regions, the explanation providing unit has the generation AI explain the background and history of the cuisine. For example, it introduces the cultural background of how a particular dish was created. In addition, the explanation providing unit has the generation AI introduce cuisine from different cultures and regions, allowing the user to experience diverse food cultures. For example, it provides detailed explanations of the characteristics and cooking methods of cuisine from different regions. This allows the user to experience diverse food cultures.

[0036] The explanation providing unit provides customizable explanations according to the user's preferences, thereby providing optimal information for each individual user. In the explanation providing unit, for example, the generation AI provides customizable explanations according to the user's preferences. For example, information about dishes and ingredients that the user prefers is preferentially displayed. In addition, in order to provide customizable explanations according to the user's preferences, the generation AI analyzes the user's past behavioral data. For example, optimal information is provided based on past order history and browsing history. In addition, the explanation providing unit provides customizable explanations according to the user's preferences, thereby providing optimal information for each individual user. For example, an explanation is provided that uses content and visuals that are likely to interest the user. In this way, customizable explanations according to the user's preferences can be provided, thereby providing optimal information for each individual user.

[0037] The experience improvement unit can analyze the user's dining history and compare it with past experiences to propose a new dining experience. In the experience improvement unit, for example, the generation AI analyzes the user's dining history and compares it with past experiences to propose a new dining experience. For example, it proposes new dishes or menus based on dishes the user has enjoyed in the past. The experience improvement unit also proposes a new dining experience based on the user's dining history and compares it with past experiences. For example, it proposes a new menu with similar characteristics to dishes the user has ordered in the past. In addition, the experience improvement unit analyzes the user's dining history and compares it with past experiences to propose a new dining experience. For example, it proposes a new dish that incorporates elements of dishes the user has enjoyed in the past. In this way, the dining experience can be improved by proposing a new dining experience by comparing it with the user's past dining experiences.

[0038] The experience improvement unit can record the user's dining experience and generate a digital album that can be looked back on later. For example, the experience improvement unit uses a generation AI to record the user's dining experience and generate a digital album that can be looked back on later. For example, photos and impressions taken during the meal are automatically saved and displayed in album format. The experience improvement unit also records the user's dining experience and generates a digital album based on that data. For example, an album including photos of the meal and descriptions of the dishes is automatically generated. The experience improvement unit also uses a generation AI to record the user's dining experience and generate a digital album that can be looked back on later. For example, emotions and comments made during the meal are recorded and reflected in the album. In this way, the dining experience can be improved by generating a digital album that records the user's dining experience and can be looked back on later.

[0039] The experience improvement unit can provide a function that allows a user to share a dining experience with other users and form a community. For example, the experience improvement unit provides a function that allows the generation AI to share a user's dining experience with other users and form a community. For example, it provides a platform where photos and impressions of meals can be shared. The experience improvement unit also provides a community function that allows users to share their dining experiences and interact with other users. For example, it provides a function where users who enjoyed the same dish can exchange comments and ratings. The experience improvement unit also provides a function that allows the generation AI to share a user's dining experience with other users and form a community. For example, a user can post their own dining experience and receive feedback from other users. This allows the user to share their dining experience with other users and form a community, thereby improving the dining experience.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The promotion generation unit can also analyze a user's past reviews and feedback to generate promotions based on the user's preferences. For example, the promotion generation unit can extract characteristics of dishes and restaurants that the user has previously rated highly and generate promotions based on those characteristics. The promotion generation unit can also analyze the content of a user's review, extract specific keywords and phrases, and reflect them in promotions. For example, if a user writes in a review that they "like spicy food," the promotion generation unit can generate a promotion for spicy food based on that information. Furthermore, the promotion generation unit can analyze user feedback in real time and dynamically change the content of promotions. For example, if a user provides feedback that they would like more detailed information while viewing a promotion, the promotion content can be updated in response to that request. This makes it possible to provide more personalized promotions by utilizing the user's past reviews and feedback.

[0042] The promotion generation unit can also analyze a user's social media posts and generate promotions based on the user's interests. For example, it can analyze photos and comments about food shared by the user on social media and generate promotions based on that information. The promotion generation unit can also analyze posts from the user's social media followers and friends to generate promotions that the user might be interested in. For example, if the user's friends highly rate a particular restaurant, it can provide the user with a promotion for that restaurant. Furthermore, the promotion generation unit can analyze the user's social media trends and generate promotions based on the latest trends. For example, if a particular dish or ingredient is trending on social media, it can generate a promotion that reflects that trend. This makes it possible to utilize the user's social media posts to provide more interesting promotions.

[0043] The promotion generation unit can also analyze the user's health data and generate promotions according to the user's health condition. For example, it can generate promotions for health-conscious menus based on data obtained from the user's health app. The promotion generation unit can also generate appropriate promotions taking into account the user's allergy information and dietary restrictions. For example, if the user is allergic to a specific ingredient, it can provide promotions for menus that do not contain that ingredient. Furthermore, the promotion generation unit can generate promotions according to the user's health goals. For example, if the user is on a diet, it can provide promotions for low-calorie menus and dishes using healthy ingredients. In this way, it is possible to utilize the user's health data to provide healthier promotions.

[0044] The promotion generation unit can also analyze the user's location information and generate promotions specialized for a region. For example, if the user is in a specific region, promotions related to restaurants and local specialties in that region are provided. The promotion generation unit can also provide promotions for nearby restaurants in real time based on the user's location information. For example, if the user is in a specific area, promotions for restaurants in that area are preferentially displayed. Furthermore, the promotion generation unit can analyze the user's location information and provide promotions related to local events and festivals. For example, if the user is participating in a specific event, promotions for restaurants and menus related to that event are provided. In this way, the user's location information can be utilized to provide promotions specialized for the region.

[0045] The promotion generation unit can also analyze a user's purchase history and generate promotions based on specific brands or products. For example, based on products of a brand that the user has previously purchased, a promotion for new products or special offers of that brand can be provided. The promotion generation unit can also generate promotions for related products based on the user's purchase history. For example, if a user purchases a specific ingredient, a promotion for recipes or dishes using that ingredient can be provided. Furthermore, the promotion generation unit can analyze a user's purchase history and generate promotions tailored to specific seasons or events. For example, if a user has previously purchased a specific product during the Christmas season, a promotion tailored to that season can be provided. This makes it possible to utilize the user's purchase history to provide more relevant promotions.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The promotion generation unit uses the generation AI to generate attractive promotions for restaurants. For example, based on information provided by the restaurant, the generation AI generates promotions that provide detailed explanations of the chef's preferences, the origins of the menu, and the characteristics of the ingredients used. The generation AI can also provide detailed explanations of the history and cultural background of the cuisine, the chef's experience and techniques, and provide beautiful related images. Step 2: The explanation provider provides the promotions generated by the promotion generator with rich descriptions and beautiful images. For example, the explanation provider interactively provides the passion and story behind a dish with rich descriptions and beautiful images. This allows the user to deeply understand the significance and appeal of the dish. Step 3: The experience improvement unit improves the user's dining experience based on the information provided by the explanation provision unit. For example, by learning the story behind the dish, the experience improvement unit increases the user's interest in the dish and makes the meal more satisfying. Furthermore, by helping the user understand the significance and appeal of the dish, the experience improvement unit makes the meal an experience that not only stimulates the five senses but also resonates with the heart.

[0048] (Example 2) A food delivery service according to an embodiment of the present invention is a system that efficiently generates attractive promotions for restaurants and dramatically improves the dining experience for users. This allows the food delivery service to dramatically improve the dining experience for users.

[0049] A food delivery service according to an embodiment includes a promotion generation unit, an explanation providing unit, and an experience improvement unit. The promotion generation unit uses a generation AI to generate attractive promotions for restaurants. For example, the generation AI generates promotions that provide detailed explanations of the chef's preferences, the origins of the menu, the characteristics of the ingredients used, and so on, based on information provided by the restaurant. The generation AI can also provide detailed explanations of the history and cultural background of the cuisine, the chef's experience and techniques, and provide beautiful images related to the cuisine. For example, the generation AI receives prompts containing information about the chef's preferences and the origins of the menu, and generates promotions based on the prompts. The explanation providing unit provides the promotions generated by the promotion generation unit with rich explanations and beautiful images. For example, the explanation providing unit interactively provides the passion and story behind the cuisine with rich explanations and beautiful images. This allows users to deeply understand the significance and appeal of the cuisine. The experience improvement unit improves the user's dining experience based on the information provided by the explanation providing unit. For example, the experience improvement unit increases the user's interest in the cuisine by learning the story behind the cuisine, making the meal more satisfying. Furthermore, by understanding the significance and appeal of the food, the experience improvement unit makes the meal an experience that not only stimulates the five senses but also resonates with the heart. As a result, the food delivery service according to the embodiment improves the effectiveness of restaurant promotions, making the user's dining experience more satisfying. For example, the promotion generation unit generates attractive promotions for restaurants, the explanation provision unit provides the promotions with rich explanations and beautiful images, and the experience improvement unit improves the user's dining experience.

[0050] The promotion generation unit can analyze interview videos, read emotions from the voice, and reflect them in promotions. For example, the promotion generation unit uses a generation AI to analyze an interview video of a chef and read emotions from the voice. For example, it analyzes the tone of voice and emphasized parts when the chef talks about a particular dish and reflects those emotions in the promotion. The promotion generation unit also analyzes the audio data of the interview video to detect changes in the chef's emotions. For example, it emphasizes parts where the chef speaks passionately and reflects those emotions in the text and images of the promotion. The promotion generation unit also analyzes the interview video of the chef and identifies the intensity and type of emotions. For example, it extracts parts where emotions of joy and pride are strong and reflects those emotions in the promotion's catchphrase and visuals. In this way, promotions that reflect the chef's emotions can be generated, encouraging emotional empathy among users.

[0051] The promotion generation unit can analyze the cooking process in real time and generate promotions based on data such as temperature and time. For example, the promotion generation unit uses a generation AI to analyze the cooking process in real time and collect data such as temperature and time. For example, the promotion reflects the characteristics and preferences of the dish based on temperature changes and cooking time during cooking. The promotion generation unit also analyzes data from the cooking process and evaluates the doneness and quality of the dish. For example, it generates promotions that ensure the deliciousness of the dish by maintaining appropriate temperature control and cooking time. The promotion generation unit also generates promotions that emphasize the appeal of the dish based on data from the cooking process. For example, it reflects the flavor and texture obtained by cooking at a specific temperature and time in the promotion. In this way, the appeal of the dish can be emphasized by generating promotions based on data from the cooking process.

[0052] The promotion generation unit uses the emotion estimation function to generate a promotion that reflects the chef's passion and emotions, thereby encouraging emotional empathy among users. The promotion generation unit, for example, uses the emotion estimation function to analyze the chef's passion and emotions. For example, it estimates the emotions the chef feels when talking about a particular dish and reflects those emotions in the promotion. The promotion generation unit also generates a promotion that reflects the chef's emotions. For example, it encourages emotional empathy among users by using a catchphrase or visual that emphasizes the chef's passion or pride. The promotion generation unit also uses the emotion estimation function to analyze the chef's emotions in real time and reflects the results in the promotion. For example, it emphasizes the parts of the chef that speak passionately, thereby generating a promotion that encourages emotional empathy among users. In this way, by generating a promotion that reflects the chef's passion and emotions, it is possible to encourage emotional empathy among users.

[0053] The promotion generation unit can learn promotional data from other restaurants and generate promotions in different styles. For example, the generation AI learns promotional data from other restaurants and generates promotions in different styles. For example, it generates promotions with a casual atmosphere or promotions with an upscale feel. The promotion generation unit also analyzes promotional data from other restaurants and extracts elements of successful promotions. For example, it generates unique promotions by referring to specific visuals or catchphrases. The promotion generation unit also utilizes promotional data from other restaurants to generate promotions in different styles. For example, it generates unique promotions by incorporating promotional styles from different cultures or regions. In this way, it is possible to generate promotions in different styles by learning promotional data from other restaurants.

[0054] The promotion generation unit can analyze a user's past order history and generate promotions optimized for each individual user. For example, the promotion generation unit uses a generation AI to analyze a user's past order history and generate promotions optimized for each individual user. For example, a personalized promotion is generated based on the user's favorite dishes and ingredients. The promotion generation unit also generates an attractive promotion based on the user's order history. For example, new menu items and special offers related to dishes the user has previously ordered are reflected in the promotion. The promotion generation unit also analyzes the order history to generate promotions optimized for each individual user. For example, a personalized promotion is provided taking into account the user's preferences and eating habits. In this way, a promotion optimized for each individual user can be generated by analyzing the user's past order history.

[0055] The promotion generation unit uses the emotion estimation function to analyze the emotion of the user when viewing a promotion in real time, and can provide an optimal promotion. The promotion generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when viewing a promotion in real time. For example, the promotion generation unit analyzes the user's facial expression and voice and calculates an emotion score. The promotion generation unit also analyzes the user's emotion in real time and provides an optimal promotion based on the results. For example, promotions with strong positive emotions are preferentially displayed. The promotion generation unit also uses the emotion estimation function to provide a promotion according to the user's emotion. For example, a promotion is generated using content and visuals that are likely to interest the user. In this way, the optimal promotion can be provided by analyzing the user's emotion in real time.

[0056] The explanation providing unit can generate a 3D model of a dish and provide a function that allows the user to interactively rotate and enlarge it. For example, the explanation providing unit allows the generation AI to generate a 3D model of a dish and provide a function that allows the user to interactively rotate and enlarge it. For example, it allows the user to check the details of the dish from 360 degrees. The explanation providing unit also generates a 3D model of a dish and provides a function that allows the user to interactively operate it. For example, it allows the user to enlarge and view specific parts of the dish. The explanation providing unit also provides a function that allows the generation AI to generate a 3D model of a dish and allow the user to interactively experience it. For example, it allows the user to check the internal structure of the dish and the arrangement of ingredients. This allows the user to interactively check the details of the dish.

[0057] The explanation providing unit can simulate the aroma and sounds of cooking to provide the user with an interactive experience that stimulates the five senses. For example, the explanation providing unit has the generation AI simulate the aroma of cooking to provide the user with an experience that stimulates the five senses. For example, the explanation providing unit can reproduce the aroma of a specific dish so that the user can smell that aroma. The explanation providing unit can also have the generation AI simulate the sounds of cooking to provide the user with an experience that stimulates the five senses. For example, the explanation providing unit can reproduce the sounds of food being grilled or simmered so that the user can hear those sounds. The explanation providing unit can also have the generation AI simulate the aroma and sounds of cooking to provide the user with an interactive experience. For example, the explanation providing unit can allow the user to check the details of the food while smelling the aroma and sounds of the food. This makes it possible to provide the user with an interactive experience that stimulates the five senses.

[0058] The explanation providing unit can use the emotion estimation function to analyze the emotion of the user when viewing the explanation and provide additional information according to the emotion. The explanation providing unit, for example, uses the emotion estimation function to analyze the emotion of the user when viewing the explanation. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The explanation providing unit also analyzes the user's emotion and provides additional information according to the emotion based on the analysis result. For example, if the emotion is strong, it provides detailed information about the dish or behind-the-scenes stories. The explanation providing unit also uses the emotion estimation function to provide additional information according to the user's emotion. For example, it provides additional information using content and visuals that are likely to interest the user. This makes it possible to improve the interactive experience by providing additional information according to the user's emotion.

[0059] The explanation providing unit introduces cuisine from different cultures and regions, allowing the user to experience diverse food cultures. For example, the generation AI introduces cuisine from different cultures and regions, allowing the user to experience diverse food cultures. For example, it provides detailed explanations of cuisine from different regions, such as Asian cuisine and European cuisine. In addition, to introduce cuisine from different cultures and regions, the explanation providing unit has the generation AI explain the background and history of the cuisine. For example, it introduces the cultural background of how a particular dish was created. In addition, the explanation providing unit has the generation AI introduce cuisine from different cultures and regions, allowing the user to experience diverse food cultures. For example, it provides detailed explanations of the characteristics and cooking methods of cuisine from different regions. This allows the user to experience diverse food cultures.

[0060] The explanation providing unit provides customizable explanations according to the user's preferences, thereby providing optimal information for each individual user. In the explanation providing unit, for example, the generation AI provides customizable explanations according to the user's preferences. For example, information about dishes and ingredients that the user prefers is preferentially displayed. In addition, in order to provide customizable explanations according to the user's preferences, the generation AI analyzes the user's past behavioral data. For example, optimal information is provided based on past order history and browsing history. In addition, the explanation providing unit provides customizable explanations according to the user's preferences, thereby providing optimal information for each individual user. For example, an explanation is provided that uses content and visuals that are likely to interest the user. In this way, customizable explanations according to the user's preferences can be provided, thereby providing optimal information for each individual user.

[0061] The explanation providing unit uses the emotion estimation function to preferentially display information that is of most interest to the user, thereby improving the interactive experience. The explanation providing unit, for example, uses the emotion estimation function to preferentially display information that is of most interest to the user. For example, it analyzes the user's facial expressions and voice and preferentially displays information that is of greatest interest to the user. The explanation providing unit also analyzes the user's emotions and displays information that is of most interest to the user based on the results of the analysis. For example, it preferentially displays information that has a strong positive emotion. The explanation providing unit also uses the emotion estimation function to preferentially display information that is of most interest to the user, thereby improving the interactive experience. For example, it provides information that uses content and visuals that are likely to interest the user. This allows the interactive experience to be improved by preferentially displaying information that is of greatest interest to the user.

[0062] The experience improvement unit can analyze the user's facial expressions while eating and provide additional information and suggestions according to their emotions in real time. For example, the generation AI in the experience improvement unit analyzes the user's facial expressions while eating and provides additional information according to their emotions in real time. For example, if the user has an expression of enjoyment, the unit can provide the inside story of the dish or recommended ways to eat it. The experience improvement unit can also analyze the user's facial expressions while eating and make suggestions according to their emotions based on the results. For example, if the user has a surprised expression, the unit can suggest unexpected aspects of the dish or new ways to eat it. The experience improvement unit can also analyze the user's facial expressions while eating in real time and provide additional information and suggestions according to their emotions. For example, if the user has a satisfied expression, the unit can provide a detailed explanation of the history and cultural background of the dish. This can improve the dining experience by providing additional information and suggestions according to the user's emotions while eating.

[0063] The experience improvement unit can analyze the user's dining history and compare it with past experiences to propose a new dining experience. In the experience improvement unit, for example, the generation AI analyzes the user's dining history and compares it with past experiences to propose a new dining experience. For example, it proposes new dishes or menus based on dishes the user has enjoyed in the past. The experience improvement unit also proposes a new dining experience based on the user's dining history and compares it with past experiences. For example, it proposes a new menu with similar characteristics to dishes the user has ordered in the past. In addition, the experience improvement unit analyzes the user's dining history and compares it with past experiences to propose a new dining experience. For example, it proposes a new dish that incorporates elements of dishes the user has enjoyed in the past. In this way, the dining experience can be improved by proposing a new dining experience by comparing it with the user's past dining experiences.

[0064] The experience improvement unit can use the emotion estimation function to analyze the user's emotions while eating and suggest music and lighting according to the emotions. For example, the experience improvement unit can use the emotion estimation function to analyze the user's emotions while eating and suggest music according to the emotions. For example, if the user is relaxed, soft music can be suggested. The experience improvement unit can also analyze the user's emotions while eating and suggest lighting according to the emotions based on the results. For example, if the user is enjoying themselves, bright lighting can be suggested. The experience improvement unit can also use the emotion estimation function to analyze the user's emotions while eating in real time and suggest music and lighting according to the emotions. For example, if the user is moved, moving music and soft lighting can be suggested. In this way, the dining experience can be improved by suggesting music and lighting according to the user's emotions while eating.

[0065] The experience improvement unit can record the user's dining experience and generate a digital album that can be looked back on later. For example, the experience improvement unit uses a generation AI to record the user's dining experience and generate a digital album that can be looked back on later. For example, photos and impressions taken during the meal are automatically saved and displayed in album format. The experience improvement unit also records the user's dining experience and generates a digital album based on that data. For example, an album including photos of the meal and descriptions of the dishes is automatically generated. The experience improvement unit also uses a generation AI to record the user's dining experience and generate a digital album that can be looked back on later. For example, emotions and comments made during the meal are recorded and reflected in the album. In this way, the dining experience can be improved by generating a digital album that records the user's dining experience and can be looked back on later.

[0066] The experience improvement unit can provide a function that allows a user to share a dining experience with other users and form a community. For example, the experience improvement unit provides a function that allows the generation AI to share a user's dining experience with other users and form a community. For example, it provides a platform where photos and impressions of meals can be shared. The experience improvement unit also provides a community function that allows users to share their dining experiences and interact with other users. For example, it provides a function where users who enjoyed the same dish can exchange comments and ratings. The experience improvement unit also provides a function that allows the generation AI to share a user's dining experience with other users and form a community. For example, a user can post their own dining experience and receive feedback from other users. This allows the user to share their dining experience with other users and form a community, thereby improving the dining experience.

[0067] The experience improvement unit can use the emotion estimation function to analyze the user's dining experience and suggest optimal meal plans and recipes. The experience improvement unit, for example, uses the emotion estimation function to analyze the user's dining experience and suggest optimal meal plans. For example, if the user is relaxed, dishes that have a relaxing effect are suggested. The experience improvement unit also analyzes the user's dining experience and suggests optimal recipes based on the results. For example, if the user is enjoying themselves, recipes that will increase the enjoyment are suggested. The experience improvement unit also uses the emotion estimation function to analyze the user's dining experience in real time and suggest optimal meal plans and recipes. For example, if the user is moved, dishes that will deepen the emotion are suggested. In this way, the dining experience can be improved by analyzing the user's dining experience and suggesting optimal meal plans and recipes.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The promotion generation unit can also analyze a user's past reviews and feedback to generate promotions based on the user's preferences. For example, the promotion generation unit can extract characteristics of dishes and restaurants that the user has previously rated highly and generate promotions based on those characteristics. The promotion generation unit can also analyze the content of a user's review, extract specific keywords and phrases, and reflect them in promotions. For example, if a user writes in a review that they "like spicy food," the promotion generation unit can generate a promotion for spicy food based on that information. Furthermore, the promotion generation unit can analyze user feedback in real time and dynamically change the content of promotions. For example, if a user provides feedback that they would like more detailed information while viewing a promotion, the promotion content can be updated in response to that request. This makes it possible to provide more personalized promotions by utilizing the user's past reviews and feedback.

[0070] The promotion generation unit can also analyze a user's social media posts and generate promotions based on the user's interests. For example, it can analyze photos and comments about food shared by the user on social media and generate promotions based on that information. The promotion generation unit can also analyze posts from the user's social media followers and friends to generate promotions that the user might be interested in. For example, if the user's friends highly rate a particular restaurant, it can provide the user with a promotion for that restaurant. Furthermore, the promotion generation unit can analyze the user's social media trends and generate promotions based on the latest trends. For example, if a particular dish or ingredient is trending on social media, it can generate a promotion that reflects that trend. This makes it possible to utilize the user's social media posts to provide more interesting promotions.

[0071] The promotion generation unit can also analyze the user's health data and generate promotions according to the user's health condition. For example, it can generate promotions for health-conscious menus based on data obtained from the user's health app. The promotion generation unit can also generate appropriate promotions taking into account the user's allergy information and dietary restrictions. For example, if the user is allergic to a specific ingredient, it can provide promotions for menus that do not contain that ingredient. Furthermore, the promotion generation unit can generate promotions according to the user's health goals. For example, if the user is on a diet, it can provide promotions for low-calorie menus and dishes using healthy ingredients. In this way, it is possible to utilize the user's health data to provide healthier promotions.

[0072] The promotion generation unit can also analyze the user's location information and generate promotions specialized for a region. For example, if the user is in a specific region, promotions related to restaurants and local specialties in that region are provided. The promotion generation unit can also provide promotions for nearby restaurants in real time based on the user's location information. For example, if the user is in a specific area, promotions for restaurants in that area are preferentially displayed. Furthermore, the promotion generation unit can analyze the user's location information and provide promotions related to local events and festivals. For example, if the user is participating in a specific event, promotions for restaurants and menus related to that event are provided. In this way, the user's location information can be utilized to provide promotions specialized for the region.

[0073] The promotion generation unit can also analyze a user's purchase history and generate promotions based on specific brands or products. For example, based on products of a brand that the user has previously purchased, a promotion for new products or special offers of that brand can be provided. The promotion generation unit can also generate promotions for related products based on the user's purchase history. For example, if a user purchases a specific ingredient, a promotion for recipes or dishes using that ingredient can be provided. Furthermore, the promotion generation unit can analyze a user's purchase history and generate promotions tailored to specific seasons or events. For example, if a user has previously purchased a specific product during the Christmas season, a promotion tailored to that season can be provided. This makes it possible to utilize the user's purchase history to provide more relevant promotions.

[0074] The promotion generation unit uses the emotion estimation function to analyze the emotions of the user when viewing a promotion in real time, and can provide the optimal promotion. For example, the emotion estimation function may analyze the user's facial expressions and voice and calculate an emotion score. The promotion generation unit also analyzes the user's emotions in real time and provides the optimal promotion based on the results. For example, promotions that evoke strong positive emotions may be preferentially displayed. The promotion generation unit also uses the emotion estimation function to provide promotions that correspond to the user's emotions. For example, the promotion generation unit generates promotions that use content and visuals that are likely to interest the user. This allows the optimal promotion to be provided by analyzing the user's emotions in real time.

[0075] The explanation providing unit can use the emotion estimation function to analyze the emotion of the user when viewing the explanation and provide additional information according to the emotion. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The explanation providing unit can also analyze the user's emotion and provide additional information according to the emotion based on the results. For example, if the emotion is strong, it can provide detailed information about the dish or behind-the-scenes stories. The explanation providing unit can also use the emotion estimation function to provide additional information according to the user's emotion. For example, it can provide additional information using content and visuals that are likely to interest the user. This can improve the interactive experience by providing additional information according to the user's emotion.

[0076] The experience improvement unit can use the emotion estimation function to analyze the user's emotions while eating and suggest music and lighting according to the emotions. For example, if the user is relaxed, soft music is suggested. The experience improvement unit can also analyze the user's emotions while eating and, based on the results, suggest lighting according to the emotions. For example, if the user is enjoying themselves, bright lighting is suggested. The experience improvement unit can also use the emotion estimation function to analyze the user's emotions while eating in real time and suggest music and lighting according to the emotions. For example, if the user is moved, moving music and soft lighting are suggested. In this way, the dining experience can be improved by suggesting music and lighting according to the user's emotions while eating.

[0077] The experience improvement unit can use the emotion estimation function to analyze the user's dining experience and suggest optimal meal plans and recipes. For example, if the user is relaxed, it will suggest dishes that have a relaxing effect. The experience improvement unit also analyzes the user's dining experience and suggests optimal recipes based on the results. For example, if the user is enjoying themselves, it will suggest recipes that will increase their enjoyment. The experience improvement unit also uses the emotion estimation function to analyze the user's dining experience in real time and suggest optimal meal plans and recipes. For example, if the user is moved, it will suggest dishes that will deepen the emotion. In this way, the dining experience can be improved by analyzing the user's dining experience and suggesting optimal meal plans and recipes.

[0078] The experience improvement unit uses the emotion estimation function to analyze the user's facial expressions while eating and can provide additional information and suggestions according to the emotions in real time. For example, if the user has an expression of enjoyment, the unit can provide the inside story of the dish or recommended ways to eat it. The experience improvement unit also analyzes the user's facial expressions while eating and makes suggestions according to the emotions based on the results. For example, if the user has a surprised expression, the unit can suggest unexpected aspects of the dish or new ways to eat it. The experience improvement unit also uses the generation AI to analyze the user's facial expressions while eating in real time and provide additional information and suggestions according to the emotions. For example, if the user has a satisfied expression, the unit can provide a detailed explanation of the history and cultural background of the dish. This can improve the dining experience by providing additional information and suggestions according to the user's emotions while eating.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The promotion generation unit uses the generation AI to generate attractive promotions for restaurants. For example, based on information provided by the restaurant, the generation AI generates promotions that provide detailed explanations of the chef's preferences, the origins of the menu, and the characteristics of the ingredients used. The generation AI can also provide detailed explanations of the history and cultural background of the cuisine, the chef's experience and techniques, and provide beautiful related images. Step 2: The explanation provider provides the promotions generated by the promotion generator with rich descriptions and beautiful images. For example, the explanation provider interactively provides the passion and story behind a dish with rich descriptions and beautiful images. This allows the user to deeply understand the significance and appeal of the dish. Step 3: The experience improvement unit improves the user's dining experience based on the information provided by the explanation provision unit. For example, by learning the story behind the dish, the experience improvement unit increases the user's interest in the dish and makes the meal more satisfying. Furthermore, by helping the user understand the significance and appeal of the dish, the experience improvement unit makes the meal an experience that not only stimulates the five senses but also resonates with the heart.

[0081] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a 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.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a promotion generation unit that uses generation AI to generate attractive promotions for restaurants; an explanation provision unit that provides the promotions generated by the promotion generation unit with abundant explanations and beautiful images; and an experience improvement unit that improves the user's dining experience based on the information provided by the explanation provision unit.

2. The system according to claim 1 , wherein the promotion generating unit analyzes the cooking process in real time and generates promotions based on data such as temperature and time.

3. The system of claim 1 , wherein the description provider generates the 3D model of the dish and provides the user with the ability to interactively rotate and zoom the model.

4. The system according to claim 1 , wherein the experience improvement unit analyzes the user's facial expressions while eating and provides additional information or suggestions according to the emotions in real time.

5. 2. The system according to claim 1, wherein the promotion generation unit analyzes the interview video, reads emotions from the voice, and reflects the emotions in the promotion.

Images