system

Abstract

The system according to this embodiment aims to generate and apply the most suitable patterns for tableware based on the contents of a meal. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an application unit. The reception unit receives the details of the meal from the user. The generation unit generates patterns for tableware based on the information received by the reception unit. The application unit applies the design generated by the generation unit to the tableware.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to prepare tableware patterns according to the content of a meal, and there is a problem that there is a limit to improving the dining experience.

[0005] The system according to the embodiment aims to generate and apply an optimal tableware pattern based on the content of a meal.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an application unit. The reception unit receives the content of a meal from a user. The generation unit generates a tableware pattern based on the information received by the reception unit. The application unit applies the design generated by the generation unit to the tableware.

Effects of the Invention

[0007] The system according to this embodiment can generate and apply the most suitable patterns for tableware based on the contents of the meal. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tableware design generation system according to an embodiment of the present invention is a system that generates and applies the optimal pattern for tableware based on the content of a meal. This system comprises a reception unit in which the user inputs the content of the meal, a generation unit in which a generation AI analyzes the input information and generates the optimal design, and an application unit in which the generated design is applied to the tableware. For example, when a user is enjoying a Japanese dinner, the generation AI generates a traditional Japanese pattern and projects it onto the tableware. This enhances the atmosphere of the Japanese meal and increases the enjoyment of the meal. When a user is enjoying a Western lunch, the generation AI generates a modern design and projects it onto the tableware. This enhances the atmosphere of the Western meal and improves the satisfaction of the meal. Thus, a system that generates and applies the optimal pattern for tableware tailored to the content of a meal is a groundbreaking solution that improves the dining experience for all of humanity. As a result, the tableware design generation system can generate and apply the optimal pattern for tableware based on the user's meal content.

[0029] The tableware design generation system according to this embodiment comprises a reception unit, a generation unit, and an application unit. The reception unit receives input from the user regarding the contents of a meal. The contents of the meal entered by the user include, for example, the name of the dish, ingredients, and cooking method, but are not limited to such examples. The reception unit receives, for example, the genre of the meal and the specific name of the dish entered by the user. The generation unit uses a generation AI to generate an optimal design based on the information received by the reception unit. The generation unit generates a design based, for example, the genre of the meal and the characteristics of the dish. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a design according to the genre and characteristics of the meal. For example, the generation AI generates traditional Japanese patterns for Japanese food and modern designs for Western food. The application unit applies the generated design to the tableware. The application unit projects the generated design onto the tableware using, for example, a smart projector. This allows the user to enjoy their meal with beautiful tableware that matches the contents of the meal, improving visual satisfaction. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or without using AI. For example, the application unit can use AI to optimize the application method of the generated design when applying it to tableware. As a result, the tableware design generation system according to the embodiment can generate and apply the most suitable tableware pattern based on the user's meal content.

[0030] The reception unit can accept the genre of meal or the name of a specific dish entered by the user. For example, the reception unit accepts the genre of meal or the name of a specific dish entered by the user. The genre of meal includes, but is not limited to, Japanese food, Western food, Chinese food, etc. The name of a specific dish includes, but is not limited to, sushi, pasta, mapo tofu, etc. This allows the user to enter a specific genre of meal or the name of a dish. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the genre of meal or the name of a specific dish entered by the user into the AI, which can then use that information as basic data to generate an optimal design.

[0031] The generation unit can generate designs based on the genre of food or the characteristics of the dishes. For example, the generation unit generates designs based on the genre of food or the characteristics of the dishes. The genre of food includes, but is not limited to, Japanese food, Western food, Chinese food, etc. The characteristics of the dishes include, but is not limited to, spicy, sweet, healthy, etc. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates designs according to the genre and characteristics of the food. For example, the generation AI generates traditional Japanese patterns for Japanese food and modern designs for Western food. This makes it possible to generate designs according to the genre and characteristics of the food. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, when the generation unit generates designs based on the genre and characteristics of the food, it may use AI to optimize the design generation method.

[0032] The application unit can project a design generated using a smart projector onto tableware. For example, the application unit projects a design generated using a smart projector onto tableware. The smart projector may include, but is not limited to, features such as high resolution, short throw, and wireless connectivity. This allows the design to be projected onto tableware using a smart projector. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can connect a smart projector to an AI and send the AI-generated design to the projector for projection onto tableware.

[0033] The generation unit can generate Japanese patterns for Japanese food and designs for Western food. For example, the generation unit can generate traditional Japanese patterns for Japanese food and modern designs for Western food. Japanese patterns include, but are not limited to, cherry blossom patterns, wave patterns, and crane patterns. Modern designs include, but are not limited to, geometric patterns and minimalist designs. This makes it possible to generate appropriate designs for Japanese and Western food. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, when generating designs for Japanese and Western food, the generation unit can use AI to optimize the design generation method.

[0034] The application unit can improve user satisfaction by applying the generated design to tableware. For example, the application unit can improve the user's visual satisfaction by applying the generated design to tableware. The criteria for evaluating satisfaction include, but are not limited to, surveys and user feedback. This can improve the user's visual satisfaction. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, when applying the generated design to tableware, the application unit can use AI to evaluate user satisfaction and optimize the application method based on the evaluation results.

[0035] The reception desk can analyze the user's past meal history and suggest input methods. For example, the reception desk analyzes the user's past meal history and suggests the optimal input method. Meal history includes, but is not limited to, past meal records and order history. For example, it can automatically display meal contents that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest meal contents that the user tends to eat at specific times of the day based on their past meal history. This allows the reception desk to suggest the optimal input method based on past meal history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past meal history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception unit can filter meal entries based on the user's current health status and nutritional balance. For example, the reception unit can filter meal entries based on the user's current health status and nutritional balance. Health status includes, but is not limited to, weight, blood pressure, and blood sugar levels. Nutritional balance includes, but is not limited to, calories, vitamins, and minerals. For example, if the user is on a diet, the reception unit will prioritize displaying low-calorie meal options. Also, if the user needs to consume a specific nutrient, the reception unit can suggest meal options that are rich in that nutrient. Furthermore, if the user has allergies, the reception unit will filter and display meal options that do not contain allergens. In this way, meal options can be filtered based on health status and nutritional balance. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's health status and nutritional balance data into a generating AI and have the generating AI perform the filtering.

[0037] The reception desk can prioritize inputting meal options that are highly relevant based on the user's geographical location when the user enters meal details. For example, when the user enters meal details, the reception desk prioritizes inputting meal options that are highly relevant based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, the reception desk can suggest meal options that use local specialties. Also, if the user is traveling, the reception desk can prioritize displaying local specialty dishes. Furthermore, if the user is at home, the reception desk can suggest meal options that use ingredients available at nearby supermarkets. In this way, highly relevant meal options can be suggested based on geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant meal options.

[0038] The reception desk can analyze the user's social media activity when they input their meal details and suggest relevant meal options. For example, when a user inputs their meal details, the reception desk can analyze their social media activity and suggest relevant meal options. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, based on meals the user has shared on social media, the reception desk can suggest relevant recipes. It can also suggest meals introduced by influencers the user follows. Furthermore, it can analyze trends in food-related groups and communities the user participates in and suggest relevant meal options. This allows the reception desk to suggest relevant meal options based on social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant meal options.

[0039] The generation unit can adjust the level of detail of a design based on the priority of the meal during design generation. For example, the generation unit can adjust the level of detail of a design based on the priority of the meal during design generation. Priority includes, but is not limited to, importance, urgency, and frequency. For example, in the case of a special event meal, the generation unit can generate a detailed and luxurious design. In the case of an everyday meal, the generation unit can generate a simple and practical design. Furthermore, if the user invites friends, the generation unit can generate a flashy and eye-catching design. This allows the level of detail of the design to be adjusted based on the importance of the meal. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input meal priority data into a generation AI and have the generation AI perform the adjustment of the level of detail of the design.

[0040] The generation unit can apply different design algorithms depending on the type of meal when generating designs. For example, the generation unit can apply different design algorithms depending on the type of meal when generating designs. Design algorithms include, but are not limited to, generation algorithms and optimization algorithms. For example, in the case of Japanese food, the generation unit can apply an algorithm that generates traditional Japanese patterns. In the case of Western food, the generation unit can apply an algorithm that generates modern designs. Furthermore, in the case of Chinese food, the generation unit can apply an algorithm that generates Oriental-style designs. This allows the generation unit to apply design algorithms appropriate to the type of meal. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input meal type data into a generation AI and have the generation AI execute the application of design algorithms.

[0041] The generation unit can determine design priorities based on meal serving times during design generation. For example, the generation unit can determine design priorities based on meal serving times during design generation. Serving times include, but are not limited to, breakfast, lunch, and dinner. For example, in the case of breakfast, the generation unit may prioritize generating refreshing designs. In the case of dinner, the generation unit may prioritize generating calm designs. Furthermore, in the case of special events, the generation unit may prioritize generating luxurious designs. This allows for the determination of design priorities based on meal serving times. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input meal serving time data into a generation AI and have the generation AI perform the design priority determination.

[0042] The generation unit can adjust the order of designs based on the relationships between the dishes during design generation. For example, the generation unit can adjust the order of designs based on the relationships between the dishes during design generation. These relationships include, but are not limited to, combinations of ingredients or compatibility of dishes. For example, a design tailored to the main dish can be generated first. A design tailored to the dessert can be generated last. Furthermore, a design tailored to the side dishes can be generated in between. This allows the order of designs to be adjusted based on the relationships between the dishes. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input meal relationship data into a generation AI and have the generation AI perform the adjustment of the design order.

[0043] The application unit can analyze the user's past dining experiences to select an application method when applying a design. For example, the application unit can analyze the user's past dining experiences to select the optimal application method when applying a design. Dining experiences include, but are not limited to, past dining history and user feedback. For example, the application unit can select the optimal application method based on designs that the user has preferred in the past. It can also preferentially apply specific design patterns based on the user's past dining experiences. Furthermore, it can analyze the user's past dining experiences to select the design application method that yields the highest satisfaction. This allows for the selection of the optimal design application method based on past dining experiences. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's past dining experience data into a generating AI and have the generating AI select the optimal application method.

[0044] The application unit can customize the means of application based on the user's current living environment when applying a design. For example, the application unit customizes the means of application based on the user's current living environment when applying a design. The living environment includes, but is not limited to, the living environment, lifestyle, and family structure. For example, if the user is busy, the application unit can provide a simple and quick application method. If the user is relaxed, the application unit can provide a detailed and time-consuming application method. Furthermore, if the user is planning a special event, the application unit can provide a luxurious application method. In this way, the means of application can be customized based on the current living environment. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's living environment data into a generating AI and have the generating AI perform the customization of the means of application.

[0045] The application unit can select an application method based on the user's geographical location information when applying a design. For example, the application unit can select an application method based on the user's geographical location information when applying a design. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, the application unit can apply a design that is appropriate for the culture of that region. Also, if the user is traveling, the application unit can apply a design that is appropriate for the local culture. Furthermore, if the user is at home, the application unit can apply a home-like design. This allows the application unit to select the optimal design application method based on geographical location information. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal application method.

[0046] The application unit can analyze the user's social media activity and propose methods of application when applying a design. For example, the application unit can analyze the user's social media activity and propose methods of application when applying a design. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, the application unit can propose application methods based on designs shared by the user on social media. It can also apply designs introduced by influencers that the user follows. Furthermore, it can analyze trends in design-related groups and communities that the user participates in and propose application methods. This allows the application unit to propose methods of application based on social media activity. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of application methods.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The reception desk can refer to the user's past dining history, in addition to the type of meal and specific dish names entered by the user, and suggest designs that the user has liked in the past. For example, if the user liked a particular Japanese pattern when they enjoyed Japanese food in the past, it can suggest a similar Japanese pattern. Similarly, if the user liked a particular modern design when they enjoyed Western food in the past, it can suggest a similar design. Furthermore, it can even reuse designs that the user used for specific events in the past. This allows the system to suggest designs based on the user's past preferences.

[0049] The generation unit can also consider the user's current health status and nutritional balance when generating designs based on the type of food and the characteristics of the dishes. For example, if the user is on a diet, the generation unit will generate a simple design suitable for low-calorie meals. Furthermore, if the user needs to consume specific nutrients, it can generate a design that highlights those nutrients. Additionally, if the user has allergies, it can generate a design that avoids allergens. This allows for the customization of designs based on health status and nutritional balance.

[0050] The generation unit can also consider the user's geographical location when generating designs tailored to Japanese or Western cuisine. For example, if the user is in a specific region, it can generate a traditional design for that region. If the user is traveling, it can generate a design that reflects the local culture. Furthermore, if the user is at home, it can generate a homey design. This allows for design customization based on geographical location information.

[0051] The application unit can analyze the user's social media activity and suggest relevant designs when applying generated designs to tableware. For example, based on the meal content shared by the user on social media, the application unit can suggest relevant designs. It can also apply designs introduced by influencers the user follows. Furthermore, it can analyze trends in design-related groups and communities the user participates in and suggest application methods. This allows for design suggestions based on social media activity.

[0052] The reception desk can analyze the user's past meal history and suggest input methods. For example, it can automatically display meal contents that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest meal contents that the user tends to eat at specific times of the day based on their past meal history. This allows the system to suggest the optimal input method based on past meal history.

[0053] The input system can filter meal entries based on the user's current health status and nutritional balance. For example, if a user is on a diet, the system will prioritize displaying low-calorie meal options. If a user needs to consume a specific nutrient, the system can suggest meals rich in that nutrient. Furthermore, if a user has allergies, the system will filter and display meals that do not contain those allergens. This allows for meal filtering based on health status and nutritional balance.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk receives the user's meal details. These details include, for example, the name of the dish, ingredients, and cooking method. The reception desk accepts the genre and specific dish name entered by the user. Step 2: The generation unit uses generation AI to generate the optimal design based on the information received by the reception unit. The generation unit generates designs based on the genre of food and the characteristics of the dishes; for example, it generates traditional Japanese patterns for Japanese food and modern designs for Western food. Step 3: The application unit applies the generated design to the tableware. The application unit projects the generated design onto the tableware, for example, using a smart projector. The processing in the application unit can also optimize the design application method using AI.

[0056] (Example of form 2) The tableware design generation system according to an embodiment of the present invention is a system that generates and applies the optimal pattern for tableware based on the content of a meal. This system comprises a reception unit in which the user inputs the content of the meal, a generation unit in which a generation AI analyzes the input information and generates the optimal design, and an application unit in which the generated design is applied to the tableware. For example, when a user is enjoying a Japanese dinner, the generation AI generates a traditional Japanese pattern and projects it onto the tableware. This enhances the atmosphere of the Japanese meal and increases the enjoyment of the meal. When a user is enjoying a Western lunch, the generation AI generates a modern design and projects it onto the tableware. This enhances the atmosphere of the Western meal and improves the satisfaction of the meal. Thus, a system that generates and applies the optimal pattern for tableware tailored to the content of a meal is a groundbreaking solution that improves the dining experience for all of humanity. As a result, the tableware design generation system can generate and apply the optimal pattern for tableware based on the user's meal content.

[0057] The tableware design generation system according to this embodiment comprises a reception unit, a generation unit, and an application unit. The reception unit receives input from the user regarding the contents of a meal. The contents of the meal entered by the user include, for example, the name of the dish, ingredients, and cooking method, but are not limited to such examples. The reception unit receives, for example, the genre of the meal and the specific name of the dish entered by the user. The generation unit uses a generation AI to generate an optimal design based on the information received by the reception unit. The generation unit generates a design based, for example, the genre of the meal and the characteristics of the dish. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a design according to the genre and characteristics of the meal. For example, the generation AI generates traditional Japanese patterns for Japanese food and modern designs for Western food. The application unit applies the generated design to the tableware. The application unit projects the generated design onto the tableware using, for example, a smart projector. This allows the user to enjoy their meal with beautiful tableware that matches the contents of the meal, improving visual satisfaction. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or without using AI. For example, the application unit can use AI to optimize the application method of the generated design when applying it to tableware. As a result, the tableware design generation system according to the embodiment can generate and apply the most suitable tableware pattern based on the user's meal content.

[0058] The reception unit can accept the genre of meal or the name of a specific dish entered by the user. For example, the reception unit accepts the genre of meal or the name of a specific dish entered by the user. The genre of meal includes, but is not limited to, Japanese food, Western food, Chinese food, etc. The name of a specific dish includes, but is not limited to, sushi, pasta, mapo tofu, etc. This allows the user to enter a specific genre of meal or the name of a dish. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the genre of meal or the name of a specific dish entered by the user into the AI, which can then use that information as basic data to generate an optimal design.

[0059] The generation unit can generate designs based on the genre of food or the characteristics of the dishes. For example, the generation unit generates designs based on the genre of food or the characteristics of the dishes. The genre of food includes, but is not limited to, Japanese food, Western food, Chinese food, etc. The characteristics of the dishes include, but is not limited to, spicy, sweet, healthy, etc. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates designs according to the genre and characteristics of the food. For example, the generation AI generates traditional Japanese patterns for Japanese food and modern designs for Western food. This makes it possible to generate designs according to the genre and characteristics of the food. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, when the generation unit generates designs based on the genre and characteristics of the food, it may use AI to optimize the design generation method.

[0060] The application unit can project a design generated using a smart projector onto tableware. For example, the application unit projects a design generated using a smart projector onto tableware. The smart projector may include, but is not limited to, features such as high resolution, short throw, and wireless connectivity. This allows the design to be projected onto tableware using a smart projector. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can connect a smart projector to an AI and send the AI-generated design to the projector for projection onto tableware.

[0061] The generation unit can generate Japanese patterns for Japanese food and designs for Western food. For example, the generation unit can generate traditional Japanese patterns for Japanese food and modern designs for Western food. Japanese patterns include, but are not limited to, cherry blossom patterns, wave patterns, and crane patterns. Modern designs include, but are not limited to, geometric patterns and minimalist designs. This makes it possible to generate appropriate designs for Japanese and Western food. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, when generating designs for Japanese and Western food, the generation unit can use AI to optimize the design generation method.

[0062] The application unit can improve user satisfaction by applying the generated design to tableware. For example, the application unit can improve the user's visual satisfaction by applying the generated design to tableware. The criteria for evaluating satisfaction include, but are not limited to, surveys and user feedback. This can improve the user's visual satisfaction. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, when applying the generated design to tableware, the application unit can use AI to evaluate user satisfaction and optimize the application method based on the evaluation results.

[0063] The reception desk can analyze the user's emotions and adjust the input method for meal details based on the analyzed emotions. For example, the reception desk can analyze the user's emotions and adjust the input method for meal details based on the analyzed emotions. Emotion analysis includes, but is not limited to, technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of meal details. This allows the input method to be adjusted according to the user's emotions. Emotion analysis is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform an analysis of those emotions.

[0064] The reception desk can analyze the user's past meal history and suggest input methods. For example, the reception desk analyzes the user's past meal history and suggests the optimal input method. Meal history includes, but is not limited to, past meal records and order history. For example, it can automatically display meal contents that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest meal contents that the user tends to eat at specific times of the day based on their past meal history. This allows the reception desk to suggest the optimal input method based on past meal history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past meal history data into a generating AI and have the generating AI suggest the optimal input method.

[0065] The reception unit can filter meal entries based on the user's current health status and nutritional balance. For example, the reception unit can filter meal entries based on the user's current health status and nutritional balance. Health status includes, but is not limited to, weight, blood pressure, and blood sugar levels. Nutritional balance includes, but is not limited to, calories, vitamins, and minerals. For example, if the user is on a diet, the reception unit will prioritize displaying low-calorie meal options. Also, if the user needs to consume a specific nutrient, the reception unit can suggest meal options that are rich in that nutrient. Furthermore, if the user has allergies, the reception unit will filter and display meal options that do not contain allergens. In this way, meal options can be filtered based on health status and nutritional balance. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's health status and nutritional balance data into a generating AI and have the generating AI perform the filtering.

[0066] The reception desk can analyze the user's emotions and determine the priority of the meal content to be entered based on the analyzed emotions. For example, the reception desk can analyze the user's emotions and determine the priority of the meal content to be entered based on the analyzed emotions. Emotion analysis includes, but is not limited to, technologies such as facial recognition, voice analysis, and text analysis. For example, if the user is tired, the reception desk will prioritize displaying easy-to-prepare meals. If the user is energetic, the reception desk may prioritize suggesting more elaborate dishes. Furthermore, if the user is stressed, the reception desk will prioritize displaying meals with a relaxing effect. In this way, the priority of meal content can be determined based on the user's emotions. Emotion analysis is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform an analysis of those emotions.

[0067] The reception desk can prioritize inputting meal options that are highly relevant based on the user's geographical location when the user enters meal details. For example, when the user enters meal details, the reception desk prioritizes inputting meal options that are highly relevant based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, the reception desk can suggest meal options that use local specialties. Also, if the user is traveling, the reception desk can prioritize displaying local specialty dishes. Furthermore, if the user is at home, the reception desk can suggest meal options that use ingredients available at nearby supermarkets. In this way, highly relevant meal options can be suggested based on geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant meal options.

[0068] The reception desk can analyze the user's social media activity when they input their meal details and suggest relevant meal options. For example, when a user inputs their meal details, the reception desk can analyze their social media activity and suggest relevant meal options. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, based on meals the user has shared on social media, the reception desk can suggest relevant recipes. It can also suggest meals introduced by influencers the user follows. Furthermore, it can analyze trends in food-related groups and communities the user participates in and suggest relevant meal options. This allows the reception desk to suggest relevant meal options based on social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant meal options.

[0069] The generation unit can analyze the user's emotions and adjust the design generation method based on the analyzed emotions. For example, the generation unit can analyze the user's emotions and adjust the design generation method based on the analyzed emotions. Emotion analysis includes, but is not limited to, techniques such as facial recognition, voice analysis, and text analysis. For example, if the user is relaxed, the generation unit can generate a design with calm colors. If the user is excited, the generation unit can generate a design with vibrant colors. Furthermore, if the user is stressed, the generation unit can generate a simple and visually calming design. This allows the design generation method to be adjusted based on the user's emotions. Emotion analysis is achieved using emotion estimation functions, for example, with an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the design generation method.

[0070] The generation unit can adjust the level of detail of a design based on the priority of the meal during design generation. For example, the generation unit can adjust the level of detail of a design based on the priority of the meal during design generation. Priority includes, but is not limited to, importance, urgency, and frequency. For example, in the case of a special event meal, the generation unit can generate a detailed and luxurious design. In the case of an everyday meal, the generation unit can generate a simple and practical design. Furthermore, if the user invites friends, the generation unit can generate a flashy and eye-catching design. This allows the level of detail of the design to be adjusted based on the importance of the meal. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input meal priority data into a generation AI and have the generation AI perform the adjustment of the level of detail of the design.

[0071] The generation unit can apply different design algorithms depending on the type of meal when generating designs. For example, the generation unit can apply different design algorithms depending on the type of meal when generating designs. Design algorithms include, but are not limited to, generation algorithms and optimization algorithms. For example, in the case of Japanese food, the generation unit can apply an algorithm that generates traditional Japanese patterns. In the case of Western food, the generation unit can apply an algorithm that generates modern designs. Furthermore, in the case of Chinese food, the generation unit can apply an algorithm that generates Oriental-style designs. This allows the generation unit to apply design algorithms appropriate to the type of meal. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input meal type data into a generation AI and have the generation AI execute the application of design algorithms.

[0072] The generation unit can analyze the user's emotions and adjust the length of the design based on the analyzed emotions. For example, the generation unit can analyze the user's emotions and adjust the length of the design based on the analyzed emotions. Emotional analysis includes, but is not limited to, techniques such as facial recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the generation unit can generate a design that can be produced quickly. If the user is relaxed, the generation unit can take more time to generate a detailed design. Furthermore, if the user is excited, the generation unit can generate a visually stimulating design. This allows the design length to be adjusted based on the user's emotions. Emotional analysis is implemented using emotion estimation functions, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the design length.

[0073] The generation unit can determine design priorities based on meal serving times during design generation. For example, the generation unit can determine design priorities based on meal serving times during design generation. Serving times include, but are not limited to, breakfast, lunch, and dinner. For example, in the case of breakfast, the generation unit may prioritize generating refreshing designs. In the case of dinner, the generation unit may prioritize generating calm designs. Furthermore, in the case of special events, the generation unit may prioritize generating luxurious designs. This allows for the determination of design priorities based on meal serving times. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input meal serving time data into a generation AI and have the generation AI perform the design priority determination.

[0074] The generation unit can adjust the order of designs based on the relationships between the dishes during design generation. For example, the generation unit can adjust the order of designs based on the relationships between the dishes during design generation. These relationships include, but are not limited to, combinations of ingredients or compatibility of dishes. For example, a design tailored to the main dish can be generated first. A design tailored to the dessert can be generated last. Furthermore, a design tailored to the side dishes can be generated in between. This allows the order of designs to be adjusted based on the relationships between the dishes. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input meal relationship data into a generation AI and have the generation AI perform the adjustment of the design order.

[0075] The application unit can analyze the user's emotions and adjust the application method of the design based on the analyzed emotions. For example, the application unit can analyze the user's emotions and adjust the application method of the design based on the analyzed emotions. Emotion analysis includes, but is not limited to, techniques such as facial recognition, voice analysis, and text analysis. For example, if the user is relaxed, the application unit can apply the design at a slow pace. If the user is in a hurry, the application unit can apply the design quickly. Furthermore, if the user is excited, the application unit can apply the design with visually stimulating effects. This allows the application method of the design to be adjusted based on the user's emotions. Emotion analysis is achieved using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input user emotion data into a generative AI and have the generative AI adjust the application method of the design.

[0076] The application unit can analyze the user's past dining experiences to select an application method when applying a design. For example, the application unit can analyze the user's past dining experiences to select the optimal application method when applying a design. Dining experiences include, but are not limited to, past dining history and user feedback. For example, the application unit can select the optimal application method based on designs that the user has preferred in the past. It can also preferentially apply specific design patterns based on the user's past dining experiences. Furthermore, it can analyze the user's past dining experiences to select the design application method that yields the highest satisfaction. This allows for the selection of the optimal design application method based on past dining experiences. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's past dining experience data into a generating AI and have the generating AI select the optimal application method.

[0077] The application unit can customize the means of application based on the user's current living environment when applying a design. For example, the application unit customizes the means of application based on the user's current living environment when applying a design. The living environment includes, but is not limited to, the living environment, lifestyle, and family structure. For example, if the user is busy, the application unit can provide a simple and quick application method. If the user is relaxed, the application unit can provide a detailed and time-consuming application method. Furthermore, if the user is planning a special event, the application unit can provide a luxurious application method. In this way, the means of application can be customized based on the current living environment. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's living environment data into a generating AI and have the generating AI perform the customization of the means of application.

[0078] The application unit can analyze the user's emotions and determine the priority of design application based on the analyzed emotions. For example, the application unit can analyze the user's emotions and determine the priority of design application based on the analyzed emotions. Emotion analysis includes, but is not limited to, techniques such as facial recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the application unit can prioritize applying designs to the most important tableware. If the user is relaxed, the application unit can apply designs evenly to all tableware. Furthermore, if the user is planning a special event, the application unit can prioritize applying designs to the most luxurious tableware. This allows the application unit to determine the priority of design application based on the user's emotions. Emotion analysis is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can input user emotion data into a generative AI and have the generative AI determine the priority of design application.

[0079] The application unit can select an application method based on the user's geographical location information when applying a design. For example, the application unit can select an application method based on the user's geographical location information when applying a design. Geographical location information includes, but is not limited to, GPS data and location services. For example, if the user is in a specific region, the application unit can apply a design that is appropriate for the culture of that region. Also, if the user is traveling, the application unit can apply a design that is appropriate for the local culture. Furthermore, if the user is at home, the application unit can apply a home-like design. This allows the application unit to select the optimal design application method based on geographical location information. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal application method.

[0080] The application unit can analyze the user's social media activity and propose methods of application when applying a design. For example, the application unit can analyze the user's social media activity and propose methods of application when applying a design. Social media activity includes, but is not limited to, posts, the number of likes, and comments. For example, the application unit can propose application methods based on designs shared by the user on social media. It can also apply designs introduced by influencers that the user follows. Furthermore, it can analyze trends in design-related groups and communities that the user participates in and propose application methods. This allows the application unit to propose methods of application based on social media activity. Some or all of the above processing in the application unit may be performed using, for example, AI, or not using AI. For example, the application unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of application methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, and application unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit inputs the user's meal details using the touch panel 38A and microphone 38B of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal design using generation AI. The application unit projects the generated design onto the tableware using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, and application unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit inputs the user's meal details using the microphone 238 of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal design using generation AI. The application unit projects the generated design onto the tableware using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, and application unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit inputs the user's meal details using the microphone 238 of the headset terminal 314. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal design using generation AI. The application unit projects the generated design onto the tableware using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, and application unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit inputs the user's meal details using the microphone 238 of the robot 414. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an optimal design using generation AI. The application unit projects the generated design onto the tableware using the controlled object 443 of the robot 414.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The reception area can also provide an option for users to input their current mood or emotions in addition to their meal details. For example, if a user inputs "I want to relax," the generator will produce a relaxing design. If a user inputs "I want to cheer up," the generator can produce a cheerful design that will lift their spirits. Furthermore, if a user inputs "It's a special day," the generator can produce a luxurious and special design. This allows for the customization of designs based on the user's emotions.

[0083] The reception desk can refer to the user's past dining history, in addition to the type of meal and specific dish names entered by the user, and suggest designs that the user has liked in the past. For example, if the user liked a particular Japanese pattern when they enjoyed Japanese food in the past, it can suggest a similar Japanese pattern. Similarly, if the user liked a particular modern design when they enjoyed Western food in the past, it can suggest a similar design. Furthermore, it can even reuse designs that the user used for specific events in the past. This allows the system to suggest designs based on the user's past preferences.

[0084] The generation unit can also consider the user's current health status and nutritional balance when generating designs based on the type of food and the characteristics of the dishes. For example, if the user is on a diet, the generation unit will generate a simple design suitable for low-calorie meals. Furthermore, if the user needs to consume specific nutrients, it can generate a design that highlights those nutrients. Additionally, if the user has allergies, it can generate a design that avoids allergens. This allows for the customization of designs based on health status and nutritional balance.

[0085] The application unit can adjust the projection method based on the user's emotions when projecting designs generated using a smart projector onto tableware. For example, if the user is relaxed, the application unit will project the design at a slow pace. If the user is in a hurry, the application unit can project the design quickly. Furthermore, if the user is excited, the application unit can project the design with visually stimulating effects. In this way, the projection method can be adjusted based on the user's emotions.

[0086] The generation unit can also consider the user's geographical location when generating designs tailored to Japanese or Western cuisine. For example, if the user is in a specific region, it can generate a traditional design for that region. If the user is traveling, it can generate a design that reflects the local culture. Furthermore, if the user is at home, it can generate a homey design. This allows for design customization based on geographical location information.

[0087] The application unit can analyze the user's social media activity and suggest relevant designs when applying generated designs to tableware. For example, based on the meal content shared by the user on social media, the application unit can suggest relevant designs. It can also apply designs introduced by influencers the user follows. Furthermore, it can analyze trends in design-related groups and communities the user participates in and suggest application methods. This allows for design suggestions based on social media activity.

[0088] The reception desk can analyze the user's emotions and adjust the way they input their meal details based on those emotions. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick input of meal details. This allows the input method to be adjusted according to the user's emotions.

[0089] The reception desk can analyze the user's past meal history and suggest input methods. For example, it can automatically display meal contents that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest meal contents that the user tends to eat at specific times of the day based on their past meal history. This allows the system to suggest the optimal input method based on past meal history.

[0090] The input system can filter meal entries based on the user's current health status and nutritional balance. For example, if a user is on a diet, the system will prioritize displaying low-calorie meal options. If a user needs to consume a specific nutrient, the system can suggest meals rich in that nutrient. Furthermore, if a user has allergies, the system will filter and display meals that do not contain those allergens. This allows for meal filtering based on health status and nutritional balance.

[0091] The reception desk can analyze the user's emotions and prioritize the meal options based on those emotions. For example, if the user is tired, the reception desk will prioritize displaying easy-to-prepare meals. If the user is energetic, the reception desk can prioritize suggesting more elaborate dishes. Furthermore, if the user is stressed, the reception desk will prioritize displaying meals with relaxing effects. In this way, the system can prioritize meal options based on the user's emotions.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The reception desk receives the user's meal details. These details include, for example, the name of the dish, ingredients, and cooking method. The reception desk accepts the genre and specific dish name entered by the user. Step 2: The generation unit uses generation AI to generate the optimal design based on the information received by the reception unit. The generation unit generates designs based on the genre of food and the characteristics of the dishes; for example, it generates traditional Japanese patterns for Japanese food and modern designs for Western food. Step 3: The application unit applies the generated design to the tableware. The application unit projects the generated design onto the tableware, for example, using a smart projector. The processing in the application unit can also optimize the design application method using AI.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives meal requests from users, Based on the information received by the aforementioned reception unit, a generation unit generates patterns for tableware, An application unit for applying the design generated by the generation unit to tableware, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is The system accepts the user's input for the type of meal or the specific dish name. The system according to feature 1.

3. The generating unit is Generate designs based on the type of food or the characteristics of the cuisine. The system according to feature 1.

4. The aforementioned application unit is The design generated using a smart projector is projected onto the tableware. The system according to feature 1.

5. The generating unit is For Japanese food, we create traditional Japanese patterns; for Western food, we create designs. The system according to feature 1.

6. The aforementioned application unit is Applying the generated design to tableware improves user satisfaction. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes user emotions and adjusts how users input their meal details based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past meal history and suggests input methods. The system according to feature 1.

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