System

Abstract

A system according to an embodiment is directed to enabling a user to cannibalize with an avatar at any time.SOLUTION: A system includes a reception unit, an analysis unit, an adjustment unit, and a response unit. The reception unit inputs a meal image of a user. The analysis unit analyzes the meal image input by the reception unit. The adjusting unit controls the eating speed of the avatar based on the information analyzed by the analyzing unit. The response unit causes the avatar adjusted by the adjustment unit to have a chat.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to coordinate meal times with other people and adjust eating speeds, making it difficult to realize meals that would alleviate feelings of loneliness.

[0005] The system according to the embodiment aims to enable users to eat together with their avatars at any time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an adjustment unit, and a response unit. The reception unit inputs a meal image of a user. The analysis unit analyzes the meal image input by the reception unit. The adjustment unit controls the eating speed of the avatar based on the information analyzed by the analysis unit. The response unit allows the avatar adjusted by the adjustment unit to chat. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to eat with an avatar at any time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A shared-dining system according to an embodiment of the present invention reduces a user's sense of loneliness by having an avatar eat with the user when the user eats. The shared-dining system reduces the user's sense of loneliness by having a user input an image of the meal, which is analyzed by a generation AI, and the avatar adjusts the eating speed and engages in natural conversation. For example, in a shared-dining system, a user inputs an image of the meal. For example, in a shared-dining system, the user inputs an image of the meal the user is eating, which is analyzed by a generation AI and selects a meal similar to the one the avatar will eat. Next, the shared-dining system uses the generation AI to analyze the user's eating speed in real time and adjust the avatar's eating speed. For example, the generation AI analyzes the user's eating speed and adjusts the avatar's eating speed. Next, in a shared-dining system, the generation AI analyzes the user's behavior while eating and engages in natural conversation. For example, the generation AI analyzes the user's facial expressions and the avatar engages in natural conversation. This allows the user to eat with the avatar at any time, thereby reducing the user's sense of loneliness. Furthermore, the user can enjoy a shared meal without worrying about differences in eating speed or food preferences. This allows the user to eat with the avatar at any time, thereby reducing the user's sense of loneliness. For example, when a user eats a meal, an avatar can eat with them, reducing feelings of loneliness. Also, users can enjoy eating together without worrying about differences in eating speed or what they want to eat.

[0029] The dining-community system according to the embodiment includes a reception unit, an analysis unit, an adjustment unit, and a response unit. The reception unit receives a meal image input by a user. The method for receiving the meal image by the user includes, but is not limited to, a photograph or a video. The reception unit receives, for example, a meal image taken by the user with a smartphone. The reception unit can also receive a meal image uploaded by the user from a personal computer. The reception unit can also receive a meal video taken by the user in real time. For example, the reception unit receives a meal image taken by the user with a smartphone. The reception unit can also receive a meal image uploaded by the user from a personal computer. The reception unit can also receive a meal video taken by the user in real time. The analysis unit uses a generation AI to analyze the meal image input by the reception unit. The analysis is performed, for example, based on image recognition technology or an analysis algorithm, but is not limited to, examples. For example, the generation AI uses image recognition technology to analyze the meal image and select a meal similar to the meal the avatar is eating. The analysis unit can also use the generation AI to perform analysis based on the resolution and format of the meal image. Furthermore, the analysis unit can use the generation AI to analyze the content of the food image and select a meal for the avatar to eat. For example, the generation AI can analyze the food image using image recognition technology and select a meal similar to the one the avatar will eat. The analysis unit can also use the generation AI to perform analysis based on the resolution and format of the food image. The analysis unit can also use the generation AI to analyze the content of the food image and select a meal for the avatar to eat. The adjustment unit can use the generation AI to adjust the avatar's eating speed based on the information analyzed by the analysis unit. The adjustment can be made based on, for example, the user's eating speed and the time taken for each bite of the meal, but is not limited to such examples. For example, the generation AI can analyze the user's eating speed and adjust the avatar's eating speed. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the time taken for each bite of the meal. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the user's overall eating time.For example, the generation AI analyzes the user's eating speed and adjusts the avatar's eating speed. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the time it takes for each bite of the user's meal. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the user's overall mealtime. The response unit uses the generation AI to cause the avatar adjusted by the adjustment unit to engage in natural chat. Chatting can be based on, for example, a topic selection method or a conversation flow, but is not limited to these examples. For example, the generation AI analyzes the user's facial expressions and causes the avatar to engage in natural chat. The response unit can also use the generation AI to analyze the user's voice and causes the avatar to engage in natural chat. The response unit can also use the generation AI to analyze the user's text and causes the avatar to engage in natural chat. For example, the generation AI analyzes the user's facial expressions and causes the avatar to engage in natural chat. The response unit can also use the generation AI to analyze the user's voice and causes the avatar to engage in natural chat. Furthermore, the response unit can use a generation AI to analyze the user's text and allow the avatar to engage in natural conversation. This allows the shared dining system according to the embodiment to allow the user to eat with the avatar at any time, reducing feelings of loneliness. For example, when a user eats, the avatar can eat with the user, reducing feelings of loneliness. Furthermore, users can enjoy shared dining without worrying about differences in eating speed or food preferences.

[0030] The reception unit can analyze the user's past meal image history and select an appropriate input method. For example, the reception unit can prioritize suggesting a food image format that the user has frequently input in the past. The reception unit can also analyze the user's past food image history to determine whether they tend to input food during specific time periods and suggest the optimal input timing. The reception unit can also prioritize suggesting an input method (voice, text, image, etc.) that the user has used in the past. In this way, by analyzing the past food image history, the optimal input method can be suggested for the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past food image data into the generation AI and have the generation AI select the optimal input method.

[0031] The reception unit can perform filtering based on the user's eating environment when inputting a meal image. For example, if the user is eating in a dark environment, the reception unit automatically adjusts the brightness of the image. Furthermore, if the user is eating in a noisy environment, the reception unit can also filter background sounds to make the input image clearer. Furthermore, if the user is eating in a quiet environment, the reception unit can also prioritize natural voice input. In this way, by performing filtering based on the user's eating environment, more appropriate meal images can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's eating environment data to the generation AI and have the generation AI perform filtering.

[0032] When inputting a meal image, the reception unit can select an appropriate input means based on the user's input method. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can provide a simple text input option. Furthermore, if the user prefers image input, the reception unit can provide an interface that allows the user to easily upload images. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0033] When inputting food images, the reception unit can prioritize inputting highly relevant food images based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting food images related to the food culture of that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting images of local specialty dishes. Furthermore, if the user is at home, the reception unit can prioritize inputting images of home-cooked meals. This allows highly relevant food images to be input by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant food images.

[0034] When inputting a meal image, the reception unit can analyze the user's social media activity and input related meal images. For example, the reception unit can preferentially input meal images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related meal images. The reception unit can also input related meal images by referring to the activity of the user's friends on social media. In this way, related meal images can be input by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and have the generation AI select related meal images.

[0035] The reception unit can customize the input method based on the user's past feedback when inputting a meal image. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also make adjustments to avoid input methods that the user has previously dissatisfied with. In this way, the input method can be customized by reflecting past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the nutritional value of the meal. For example, in the case of a highly nutritious meal, the analysis unit analyzes detailed nutritional information and reflects it in the avatar's meal. In addition, in the case of a low-nutritional meal, the analysis unit can perform a simplified analysis and quickly set the avatar's meal. In addition, in the case of a nutritionally balanced meal, the analysis unit can provide a well-balanced analysis result. Thus, by adjusting the level of detail of the analysis based on the nutritional value of the meal, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input nutritional value data of the meal to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms based on the food category during analysis. For example, in the case of Japanese food, the analysis unit applies an analysis algorithm dedicated to Japanese food. Furthermore, in the case of Western food, the analysis unit can also apply an analysis algorithm dedicated to Western food. Furthermore, in the case of Chinese food, the analysis unit can also apply an analysis algorithm dedicated to Chinese food. In this way, by applying different analysis algorithms depending on the food category, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit improves the analysis accuracy based on, for example, the analysis results of food images previously input by the user. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. The analysis unit can also analyze the user's past eating patterns to improve the analysis accuracy. This improves the analysis accuracy by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0039] During analysis, the analysis unit can set analysis priorities based on the time of meal submission. For example, if the user inputs breakfast, the analysis unit can prioritize the analysis of breakfast. Furthermore, if the user inputs lunch, the analysis unit can also prioritize the analysis of lunch. Furthermore, if the user inputs dinner, the analysis unit can also prioritize the analysis of dinner. In this way, by determining the analysis priorities based on the time of meal submission, more appropriate analysis can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal submission time data to the generation AI and have the generation AI set the analysis priorities.

[0040] During analysis, the analysis unit can adjust the order of analysis based on meal relevance. For example, if a user inputs a meal using a specific ingredient, the analysis unit prioritizes the analysis of meals related to that ingredient. Furthermore, if a user inputs a specific dish, the analysis unit can prioritize the analysis of meals related to that dish. Furthermore, if a user inputs a meal containing a specific nutrient, the analysis unit can prioritize the analysis of meals related to that nutrient. In this way, adjusting the order of analysis based on meal relevance allows for more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can control the use of technical terms in the analysis based on the user's level of expertise. For example, if the user is a nutritionist, the analysis unit can provide analysis results using detailed technical terms. Furthermore, if the user is a general consumer, the analysis unit can provide analysis results in simple language. Furthermore, if the user is a cooking enthusiast, the analysis unit can provide analysis results using appropriate technical terms. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms.

[0042] During adjustment, the adjustment unit can control the avatar's eating speed in real time based on the user's eating pace. For example, if the user eats slowly, the adjustment unit adjusts the avatar's eating speed to be slower. Furthermore, if the user eats quickly, the adjustment unit can also adjust the avatar's eating speed to be faster. Furthermore, if the user changes their eating pace, the adjustment unit can also adjust the avatar's eating speed in real time. This allows for real-time adjustments based on the user's eating pace, resulting in a more natural shared eating experience. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input the user's eating pace data into the generation AI and cause the generation AI to perform real-time adjustments of the avatar's eating speed.

[0043] During adjustment, the adjustment unit can adjust the avatar's eating speed based on the user's type of meal. For example, if the user is drinking soup, the adjustment unit can adjust the avatar's eating speed to be slow. Furthermore, if the user is eating a sandwich, the adjustment unit can also adjust the avatar's eating speed to be fast. Furthermore, if the user is eating dessert, the adjustment unit can also adjust the avatar's eating speed to a natural pace. This allows for a more natural shared meal experience by adjusting the eating speed according to the user's type of meal. Some or all of the above-described processing by the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input the user's meal type data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0044] During adjustment, the adjustment unit can optimize the avatar's eating speed based on the user's past eating speed. For example, if the user has tended to eat slowly in the past, the adjustment unit can adjust the avatar's eating speed to be slower. Furthermore, if the user has tended to eat quickly in the past, the adjustment unit can also adjust the avatar's eating speed to be faster. The adjustment unit can also analyze the user's past eating speed data and suggest an optimal eating speed. This allows the avatar's eating speed to be optimized by referring to the user's past eating speed. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past eating speed data into the generation AI and cause the generation AI to optimize the avatar's eating speed.

[0045] During adjustment, the adjustment unit can adjust the eating speed of the avatar based on the user's geographical location information. For example, if the user is in a specific area, the adjustment unit can adjust the eating speed to suit the food culture of that area. Furthermore, if the user is traveling, the adjustment unit can also adjust the eating speed to suit the food culture of the travel destination. Furthermore, if the user is at home, the adjustment unit can also adjust the eating speed to suit home-cooked meals. This makes it possible to provide a more appropriate eating speed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0046] During the adjustment, the adjustment unit can analyze the user's social media activity and adjust the avatar's eating speed. For example, the adjustment unit can adjust the avatar's eating speed to match the eating speed shared by the user on social media. The adjustment unit can also analyze the content of the user's social media posts and adjust the related eating speed. The adjustment unit can also adjust the related eating speed with reference to the activity of the user's friends on social media. In this way, a more appropriate eating speed can be provided by analyzing social media activity. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's social media data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0047] During adjustment, the adjustment unit can customize the avatar's eating speed based on the user's past feedback. For example, the adjustment unit preferentially suggests eating speeds that the user has previously preferred. The adjustment unit can also customize the avatar's eating speed based on the user's past feedback. The adjustment unit can also make adjustments to avoid eating speeds that the user has previously dissatisfied with. In this way, the avatar's eating speed can be customized by reflecting past feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the avatar's eating speed.

[0048] When responding, the response unit can adjust the avatar's response content based on the user's facial expression while eating. For example, if the user is smiling while eating, the response unit can make the avatar respond with a smile. Furthermore, if the user is eating with a serious expression, the response unit can make the avatar respond with a serious response. Furthermore, if the user is eating with a tired expression, the response unit can make the avatar respond with a gentle response. By adjusting the response content based on the user's facial expression, more natural conversation can be achieved. Some or all of the above-described processing in the response unit can be performed using, for example, AI, or can be performed without using AI. For example, the response unit can input the user's facial expression data into a generation AI and cause the generation AI to adjust the avatar's response content.

[0049] When responding, the response unit can adjust the content of the avatar's chat based on the user's meal type. For example, if the user is eating Japanese food, the response unit can chat about topics related to Japanese food. Furthermore, if the user is eating Western food, the response unit can chat about topics related to Western food. Furthermore, if the user is eating Chinese food, the response unit can chat about topics related to Chinese food. This allows for more natural conversation by adjusting the content of the chat according to the user's meal type. Some or all of the above-mentioned processing in the response unit may be performed using, or without, AI, for example. For example, the response unit can input the user's meal type data into the generation AI and cause the generation AI to adjust the content of the avatar's chat.

[0050] When responding, the response unit can optimize the avatar's response content based on the user's past chat history. For example, the response unit prioritizes topics that the user has previously liked. The response unit can also suggest related topics based on the user's past chat history. The response unit can also adjust the response to avoid topics that the user has previously dissatisfied with. This allows the response content to be optimized by referring to the past chat history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past chat history data into a generation AI and cause the generation AI to optimize the avatar's response content.

[0051] When responding, the response unit can adjust the content of the avatar's chat based on the user's geographical location information. For example, if the user is in a specific area, the response unit can chat on topics related to that area. Furthermore, if the user is traveling, the response unit can chat on topics related to the travel destination. Furthermore, if the user is at home, the response unit can chat on topics related to the home. This allows for more appropriate chat by taking the user's geographical location information into consideration. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit can input the user's geographical location information data into the generation AI and cause the generation AI to adjust the content of the avatar's chat.

[0052] When responding, the response unit can analyze the user's social media activity and adjust the content of the avatar's chat. For example, the response unit can prioritize topics shared by the user on social media. The response unit can also analyze the content of the user's social media posts and chat on related topics. The response unit can also refer to the activity of the user's friends on social media and chat on related topics. In this way, analyzing social media activity can enable more appropriate chat. Some or all of the above-mentioned processing in the response unit can be performed using, for example, AI, or can be performed without using AI. For example, the response unit can input the user's social media data into a generation AI and cause the generation AI to adjust the content of the avatar's chat.

[0053] When responding, the response unit can customize the avatar's chat content based on the user's past feedback. For example, the response unit prioritizes topics that the user has previously liked. The response unit can also customize the avatar's chat content based on the user's past feedback. The response unit can also adjust the chat content to avoid topics that the user has previously dissatisfied with. In this way, the chat content can be customized by reflecting past feedback. Some or all of the above-mentioned processing in the response unit may be performed using, or without, AI. For example, the response unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the avatar's chat content.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The shared eating system can further include a health management unit that monitors the user's health condition. The health management unit can evaluate the user's health condition based on the user's eating content and eating speed, and provide dietary advice as needed. For example, if the user continues to eat high-calorie meals, the health management unit can suggest low-calorie meals. Also, if the user eats too quickly, the health management unit can advise the user to eat more slowly. Furthermore, if the user is lacking in a particular nutrient, the health management unit can suggest meals containing that nutrient. This allows for dietary advice that takes the user's health condition into consideration, supporting a healthier diet.

[0056] The shared dining system may further include an environmental adjustment unit that improves the user's dining environment. The environmental adjustment unit may adjust lighting, music, temperature, and the like based on the user's dining environment. For example, if the user is dining in a dark environment, the environmental adjustment unit may brighten the lights. Also, if the user is dining in a quiet environment, the environmental adjustment unit may play relaxing music. Furthermore, if the user is dining in a cold environment, the environmental adjustment unit may raise the temperature. This may optimize the user's dining environment and provide a more comfortable dining experience.

[0057] The shared dining system can further include a history analysis unit that analyzes the user's meal history. The history analysis unit can analyze the user's past meal history and understand their eating patterns and preferences. For example, if the user frequently eats a particular dish, the history analysis unit can preferentially suggest that dish. Also, if the user tends to eat during a particular time of day, the history analysis unit can also suggest meals for that time of day. Furthermore, the nutritional balance of dishes the user has eaten in the past can be analyzed to suggest balanced meals. This makes it possible to utilize the user's meal history to make more personalized meal suggestions.

[0058] The shared eating system can further include a posture monitoring unit that monitors the user's posture while eating. The posture monitoring unit can analyze the user's posture while eating in real time and advise the user to maintain correct posture. For example, if the user is slouching, the posture monitoring unit can advise the user to straighten their back. Also, if the user is slouching while eating, the posture monitoring unit can encourage the user to maintain correct posture. Furthermore, if the user has been eating in the same position for a long time, the posture monitoring unit can advise the user to change their posture appropriately. This can improve the user's posture while eating and support healthy eating habits.

[0059] The dining system may further include a conversation recording unit that records conversations between users during meals and plays them back later. The conversation recording unit records conversations between users and avatars, allowing the users to play them back later and enjoy them. For example, if a user has an enjoyable conversation, the conversation can be recorded and played back later. Also, if a user speaks important information, the information can be recorded and checked later. Furthermore, if a user wants to play back a specific conversation, the conversation recording unit can search for and play back that conversation. This allows the user to enjoy the conversations they had during meals later, providing a more fulfilling dining experience.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a meal image input by the user. Methods for the user to input a meal image include, but are not limited to, a photograph or a video. The reception unit receives, for example, a meal image taken by the user with a smartphone. The reception unit can also receive a meal image uploaded by the user from a PC. Furthermore, the reception unit can also receive a meal video taken by the user in real time. Step 2: The analysis unit uses the generation AI to analyze the food image input by the reception unit. The analysis is performed, for example, based on image recognition technology or an analysis algorithm, but is not limited to such examples. For example, the generation AI analyzes the food image using image recognition technology and selects a food similar to the food the avatar will eat. The analysis unit can also use the generation AI to perform analysis based on the resolution and format of the food image. Furthermore, the analysis unit can also use the generation AI to analyze the content of the food image and select a food the avatar will eat. Step 3: The adjustment unit uses the generation AI to adjust the avatar's eating speed based on the information analyzed by the analysis unit. The adjustment is made based on, for example, the user's eating speed and the time taken for each bite of the meal, but is not limited to such examples. For example, the generation AI analyzes the user's eating speed and adjusts the avatar's eating speed. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the time taken for each bite of the meal. Furthermore, the adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the user's overall eating time. Step 4: The response unit uses the generation AI to have the avatar adjusted by the adjustment unit engage in natural chat. The chat is conducted based on, for example, a topic selection method and a conversation flow, but is not limited to such examples. For example, the generation AI analyzes the user's facial expressions, and the avatar engages in natural chat. The response unit can also use the generation AI to analyze the user's voice, and the avatar can engage in natural chat. Furthermore, the response unit can also use the generation AI to analyze the user's text, and the avatar can engage in natural chat.

[0062] (Example 2) A shared-dining system according to an embodiment of the present invention reduces a user's sense of loneliness by having an avatar eat with the user when the user eats. The shared-dining system reduces the user's sense of loneliness by having a user input an image of the meal, which is analyzed by a generation AI, and the avatar adjusts the eating speed and engages in natural conversation. For example, in a shared-dining system, a user inputs an image of the meal. For example, in a shared-dining system, the user inputs an image of the meal the user is eating, which is analyzed by a generation AI and selects a meal similar to the one the avatar will eat. Next, the shared-dining system uses the generation AI to analyze the user's eating speed in real time and adjust the avatar's eating speed. For example, the generation AI analyzes the user's eating speed and adjusts the avatar's eating speed. Next, in a shared-dining system, the generation AI analyzes the user's behavior while eating and engages in natural conversation. For example, the generation AI analyzes the user's facial expressions and the avatar engages in natural conversation. This allows the user to eat with the avatar at any time, thereby reducing the user's sense of loneliness. Furthermore, the user can enjoy a shared meal without worrying about differences in eating speed or food preferences. This allows the user to eat with the avatar at any time, thereby reducing the user's sense of loneliness. For example, when a user eats a meal, an avatar can eat with them, reducing feelings of loneliness. Also, users can enjoy eating together without worrying about differences in eating speed or what they want to eat.

[0063] The dining-community system according to the embodiment includes a reception unit, an analysis unit, an adjustment unit, and a response unit. The reception unit receives a meal image input by a user. The method for receiving the meal image by the user includes, but is not limited to, a photograph or a video. The reception unit receives, for example, a meal image taken by the user with a smartphone. The reception unit can also receive a meal image uploaded by the user from a personal computer. The reception unit can also receive a meal video taken by the user in real time. For example, the reception unit receives a meal image taken by the user with a smartphone. The reception unit can also receive a meal image uploaded by the user from a personal computer. The reception unit can also receive a meal video taken by the user in real time. The analysis unit uses a generation AI to analyze the meal image input by the reception unit. The analysis is performed, for example, based on image recognition technology or an analysis algorithm, but is not limited to, examples. For example, the generation AI uses image recognition technology to analyze the meal image and select a meal similar to the meal the avatar is eating. The analysis unit can also use the generation AI to perform analysis based on the resolution and format of the meal image. Furthermore, the analysis unit can use the generation AI to analyze the content of the food image and select a meal for the avatar to eat. For example, the generation AI can analyze the food image using image recognition technology and select a meal similar to the one the avatar will eat. The analysis unit can also use the generation AI to perform analysis based on the resolution and format of the food image. The analysis unit can also use the generation AI to analyze the content of the food image and select a meal for the avatar to eat. The adjustment unit can use the generation AI to adjust the avatar's eating speed based on the information analyzed by the analysis unit. The adjustment can be made based on, for example, the user's eating speed and the time taken for each bite of the meal, but is not limited to such examples. For example, the generation AI can analyze the user's eating speed and adjust the avatar's eating speed. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the time taken for each bite of the meal. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the user's overall eating time.For example, the generation AI analyzes the user's eating speed and adjusts the avatar's eating speed. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the time it takes for each bite of the user's meal. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the user's overall mealtime. The response unit uses the generation AI to cause the avatar adjusted by the adjustment unit to engage in natural chat. Chatting can be based on, for example, a topic selection method or a conversation flow, but is not limited to these examples. For example, the generation AI analyzes the user's facial expressions and causes the avatar to engage in natural chat. The response unit can also use the generation AI to analyze the user's voice and causes the avatar to engage in natural chat. The response unit can also use the generation AI to analyze the user's text and causes the avatar to engage in natural chat. For example, the generation AI analyzes the user's facial expressions and causes the avatar to engage in natural chat. The response unit can also use the generation AI to analyze the user's voice and causes the avatar to engage in natural chat. Furthermore, the response unit can use a generation AI to analyze the user's text and allow the avatar to engage in natural conversation. This allows the shared dining system according to the embodiment to allow the user to eat with the avatar at any time, reducing feelings of loneliness. For example, when a user eats, the avatar can eat with the user, reducing feelings of loneliness. Furthermore, users can enjoy shared dining without worrying about differences in eating speed or food preferences.

[0064] The reception unit can analyze the user's emotions and adjust the timing of inputting meal images based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input meal images at a time when the user is able to relax. Furthermore, if the user is enjoying themselves, the reception unit can prompt the user to input meal images immediately, thereby increasing the enjoyment of eating together. Furthermore, if the user is tired, the reception unit can simplify the input of meal images, reducing the effort required. This allows the input of meal images at a more appropriate time by adjusting the input timing of meal images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0065] The reception unit can analyze the user's past meal image history and select an appropriate input method. For example, the reception unit can prioritize suggesting a food image format that the user has frequently input in the past. The reception unit can also analyze the user's past food image history to determine whether they tend to input food during specific time periods and suggest the optimal input timing. The reception unit can also prioritize suggesting an input method (voice, text, image, etc.) that the user has used in the past. In this way, by analyzing the past food image history, the optimal input method can be suggested for the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past food image data into the generation AI and have the generation AI select the optimal input method.

[0066] The reception unit can perform filtering based on the user's eating environment when inputting a meal image. For example, if the user is eating in a dark environment, the reception unit automatically adjusts the brightness of the image. Furthermore, if the user is eating in a noisy environment, the reception unit can also filter background sounds to make the input image clearer. Furthermore, if the user is eating in a quiet environment, the reception unit can also prioritize natural voice input. In this way, by performing filtering based on the user's eating environment, more appropriate meal images can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's eating environment data to the generation AI and have the generation AI perform filtering.

[0067] When inputting a meal image, the reception unit can select an appropriate input means based on the user's input method. For example, if the user prefers voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the reception unit can provide a simple text input option. Furthermore, if the user prefers image input, the reception unit can provide an interface that allows the user to easily upload images. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0068] The reception unit can analyze the user's emotions and determine the priority of the food images to be input based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can prioritize inputting relaxing food images. Furthermore, if the user is enjoying themselves, the reception unit can prioritize inputting fun food images. Furthermore, if the user is tired, the reception unit can prioritize inputting simple and easy food images. Thus, by determining the priority of food images according to the user's emotions, more appropriate food images can be input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI estimate the user's emotions.

[0069] When inputting food images, the reception unit can prioritize inputting highly relevant food images based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting food images related to the food culture of that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting images of local specialty dishes. Furthermore, if the user is at home, the reception unit can prioritize inputting images of home-cooked meals. This allows highly relevant food images to be input by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant food images.

[0070] When inputting a meal image, the reception unit can analyze the user's social media activity and input related meal images. For example, the reception unit can preferentially input meal images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related meal images. The reception unit can also input related meal images by referring to the activity of the user's friends on social media. In this way, related meal images can be input by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and have the generation AI select related meal images.

[0071] The reception unit can customize the input method based on the user's past feedback when inputting a meal image. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also make adjustments to avoid input methods that the user has previously dissatisfied with. In this way, the input method can be customized by reflecting past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0072] The analysis unit can analyze the user's emotions and adjust the analysis method of the food image based on the analyzed user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and accurately reproduce the avatar's meal. If the user is in a hurry, the analysis unit can also perform a simplified analysis and quickly set the avatar's meal. If the user is excited, the analysis unit can also provide a visually appealing analysis result. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the nutritional value of the meal. For example, in the case of a highly nutritious meal, the analysis unit analyzes detailed nutritional information and reflects it in the avatar's meal. In addition, in the case of a low-nutritional meal, the analysis unit can perform a simplified analysis and quickly set the avatar's meal. In addition, in the case of a nutritionally balanced meal, the analysis unit can provide a well-balanced analysis result. Thus, by adjusting the level of detail of the analysis based on the nutritional value of the meal, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input nutritional value data of the meal to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms based on the food category during analysis. For example, in the case of Japanese food, the analysis unit applies an analysis algorithm dedicated to Japanese food. Furthermore, in the case of Western food, the analysis unit can also apply an analysis algorithm dedicated to Western food. Furthermore, in the case of Chinese food, the analysis unit can also apply an analysis algorithm dedicated to Chinese food. In this way, by applying different analysis algorithms depending on the food category, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input food category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit improves the analysis accuracy based on, for example, the analysis results of food images previously input by the user. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. The analysis unit can also analyze the user's past eating patterns to improve the analysis accuracy. This improves the analysis accuracy by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0076] The analysis unit can analyze the user's emotions and determine analysis priorities based on the analyzed user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing relaxing food images. Furthermore, if the user is having fun, the analysis unit can prioritize analyzing fun food images. Furthermore, if the user is tired, the analysis unit can prioritize analyzing simple and easy food images. This allows for more appropriate analysis by determining analysis priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0077] During analysis, the analysis unit can set analysis priorities based on the time of meal submission. For example, if the user inputs breakfast, the analysis unit can prioritize the analysis of breakfast. Furthermore, if the user inputs lunch, the analysis unit can also prioritize the analysis of lunch. Furthermore, if the user inputs dinner, the analysis unit can also prioritize the analysis of dinner. In this way, by determining the analysis priorities based on the time of meal submission, more appropriate analysis can be performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal submission time data to the generation AI and have the generation AI set the analysis priorities.

[0078] During analysis, the analysis unit can adjust the order of analysis based on meal relevance. For example, if a user inputs a meal using a specific ingredient, the analysis unit prioritizes the analysis of meals related to that ingredient. Furthermore, if a user inputs a specific dish, the analysis unit can prioritize the analysis of meals related to that dish. Furthermore, if a user inputs a meal containing a specific nutrient, the analysis unit can prioritize the analysis of meals related to that nutrient. In this way, adjusting the order of analysis based on meal relevance allows for more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] During analysis, the analysis unit can control the use of technical terms in the analysis based on the user's level of expertise. For example, if the user is a nutritionist, the analysis unit can provide analysis results using detailed technical terms. Furthermore, if the user is a general consumer, the analysis unit can provide analysis results in simple language. Furthermore, if the user is a cooking enthusiast, the analysis unit can provide analysis results using appropriate technical terms. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms.

[0080] The adjustment unit can analyze the user's emotions and adjust the avatar's eating speed based on the analyzed user's emotions. For example, if the user is relaxed, the adjustment unit can adjust the avatar's eating speed to be slow. If the user is in a hurry, the adjustment unit can also adjust the avatar's eating speed to be fast. If the user is enjoying themselves, the adjustment unit can also adjust the avatar's eating speed to a natural pace. This allows for a more natural eating experience by adjusting the avatar's eating speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the adjustment unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0081] During adjustment, the adjustment unit can control the avatar's eating speed in real time based on the user's eating pace. For example, if the user eats slowly, the adjustment unit adjusts the avatar's eating speed to be slower. Furthermore, if the user eats quickly, the adjustment unit can also adjust the avatar's eating speed to be faster. Furthermore, if the user changes their eating pace, the adjustment unit can also adjust the avatar's eating speed in real time. This allows for real-time adjustments based on the user's eating pace, resulting in a more natural shared eating experience. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input the user's eating pace data into the generation AI and cause the generation AI to perform real-time adjustments of the avatar's eating speed.

[0082] During adjustment, the adjustment unit can adjust the avatar's eating speed based on the user's type of meal. For example, if the user is drinking soup, the adjustment unit can adjust the avatar's eating speed to be slow. Furthermore, if the user is eating a sandwich, the adjustment unit can also adjust the avatar's eating speed to be fast. Furthermore, if the user is eating dessert, the adjustment unit can also adjust the avatar's eating speed to a natural pace. This allows for a more natural shared meal experience by adjusting the eating speed according to the user's type of meal. Some or all of the above-described processing by the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input the user's meal type data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0083] During adjustment, the adjustment unit can optimize the avatar's eating speed based on the user's past eating speed. For example, if the user has tended to eat slowly in the past, the adjustment unit can adjust the avatar's eating speed to be slower. Furthermore, if the user has tended to eat quickly in the past, the adjustment unit can also adjust the avatar's eating speed to be faster. The adjustment unit can also analyze the user's past eating speed data and suggest an optimal eating speed. This allows the avatar's eating speed to be optimized by referring to the user's past eating speed. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past eating speed data into the generation AI and cause the generation AI to optimize the avatar's eating speed.

[0084] The adjustment unit can analyze the user's emotions and prioritize the avatar's eating speed based on the analyzed user's emotions. For example, if the user is feeling stressed, the adjustment unit can prioritize a relaxing eating speed. Furthermore, if the user is enjoying themselves, the adjustment unit can prioritize a fun eating speed. Furthermore, if the user is tired, the adjustment unit can prioritize a simple and easy eating speed. Thus, by prioritizing the avatar's eating speed according to the user's emotions, a more appropriate eating speed can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the avatar's eating speed.

[0085] During adjustment, the adjustment unit can adjust the eating speed of the avatar based on the user's geographical location information. For example, if the user is in a specific area, the adjustment unit can adjust the eating speed to suit the food culture of that area. Furthermore, if the user is traveling, the adjustment unit can also adjust the eating speed to suit the food culture of the travel destination. Furthermore, if the user is at home, the adjustment unit can also adjust the eating speed to suit home-cooked meals. This makes it possible to provide a more appropriate eating speed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0086] During the adjustment, the adjustment unit can analyze the user's social media activity and adjust the avatar's eating speed. For example, the adjustment unit can adjust the avatar's eating speed to match the eating speed shared by the user on social media. The adjustment unit can also analyze the content of the user's social media posts and adjust the related eating speed. The adjustment unit can also adjust the related eating speed with reference to the activity of the user's friends on social media. In this way, a more appropriate eating speed can be provided by analyzing social media activity. Some or all of the above-described processing by the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's social media data into the generation AI and cause the generation AI to adjust the avatar's eating speed.

[0087] During adjustment, the adjustment unit can customize the avatar's eating speed based on the user's past feedback. For example, the adjustment unit preferentially suggests eating speeds that the user has previously preferred. The adjustment unit can also customize the avatar's eating speed based on the user's past feedback. The adjustment unit can also make adjustments to avoid eating speeds that the user has previously dissatisfied with. In this way, the avatar's eating speed can be customized by reflecting past feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the avatar's eating speed.

[0088] The response unit can analyze the user's emotions and adjust the avatar's chat content based on the analyzed user's emotions. For example, if the user is relaxed, the response unit can chat in a relaxed manner. If the user is in a hurry, the response unit can also chat briefly and to the point. If the user is having fun, the response unit can also chat mainly about fun topics. This allows for more natural conversation by adjusting the chat content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the response unit can be performed using, for example, an AI, or without an AI. For example, the response unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the avatar's chat content.

[0089] When responding, the response unit can adjust the avatar's response content based on the user's facial expression while eating. For example, if the user is smiling while eating, the response unit can make the avatar respond with a smile. Furthermore, if the user is eating with a serious expression, the response unit can make the avatar respond with a serious response. Furthermore, if the user is eating with a tired expression, the response unit can make the avatar respond with a gentle response. By adjusting the response content based on the user's facial expression, more natural conversation can be achieved. Some or all of the above-described processing in the response unit can be performed using, for example, AI, or can be performed without using AI. For example, the response unit can input the user's facial expression data into a generation AI and cause the generation AI to adjust the avatar's response content.

[0090] When responding, the response unit can adjust the content of the avatar's chat based on the user's meal type. For example, if the user is eating Japanese food, the response unit can chat about topics related to Japanese food. Furthermore, if the user is eating Western food, the response unit can chat about topics related to Western food. Furthermore, if the user is eating Chinese food, the response unit can chat about topics related to Chinese food. This allows for more natural conversation by adjusting the content of the chat according to the user's meal type. Some or all of the above-mentioned processing in the response unit may be performed using, or without, AI, for example. For example, the response unit can input the user's meal type data into the generation AI and cause the generation AI to adjust the content of the avatar's chat.

[0091] When responding, the response unit can optimize the avatar's response content based on the user's past chat history. For example, the response unit prioritizes topics that the user has previously liked. The response unit can also suggest related topics based on the user's past chat history. The response unit can also adjust the response to avoid topics that the user has previously dissatisfied with. This allows the response content to be optimized by referring to the past chat history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past chat history data into a generation AI and cause the generation AI to optimize the avatar's response content.

[0092] The response unit can analyze the user's emotions and determine the priority of the avatar's chat based on the analyzed user's emotions. For example, if the user is feeling stressed, the response unit can prioritize relaxing topics. Furthermore, if the user is having fun, the response unit can prioritize fun topics. Furthermore, if the user is tired, the response unit can prioritize easy and simple topics. This allows for more appropriate chat by determining the priority of chats according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the response unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the avatar's chats.

[0093] When responding, the response unit can adjust the content of the avatar's chat based on the user's geographical location information. For example, if the user is in a specific area, the response unit can chat on topics related to that area. Furthermore, if the user is traveling, the response unit can chat on topics related to the travel destination. Furthermore, if the user is at home, the response unit can chat on topics related to the home. This allows for more appropriate chat by taking the user's geographical location information into consideration. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit can input the user's geographical location information data into the generation AI and cause the generation AI to adjust the content of the avatar's chat.

[0094] When responding, the response unit can analyze the user's social media activity and adjust the content of the avatar's chat. For example, the response unit can prioritize topics shared by the user on social media. The response unit can also analyze the content of the user's social media posts and chat on related topics. The response unit can also refer to the activity of the user's friends on social media and chat on related topics. In this way, analyzing social media activity can enable more appropriate chat. Some or all of the above-mentioned processing in the response unit can be performed using, for example, AI, or can be performed without using AI. For example, the response unit can input the user's social media data into a generation AI and cause the generation AI to adjust the content of the avatar's chat.

[0095] When responding, the response unit can customize the avatar's chat content based on the user's past feedback. For example, the response unit prioritizes topics that the user has previously liked. The response unit can also customize the avatar's chat content based on the user's past feedback. The response unit can also adjust the chat content to avoid topics that the user has previously dissatisfied with. In this way, the chat content can be customized by reflecting past feedback. Some or all of the above-mentioned processing in the response unit may be performed using, or without, AI. For example, the response unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the avatar's chat content. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, adjustment unit, and response unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives food images taken by a user with a smartphone. The reception unit can also receive food images uploaded from a personal computer via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the food images using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the avatar's eating speed based on the user's eating speed. The response unit is realized, for example, by the control unit 46A of the smart device 14 and allows the avatar to engage in natural conversation using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, adjustment unit, and response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives food images taken by a user with a smartphone. The reception unit can also receive food images uploaded from a personal computer via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the food images using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the avatar's eating speed based on the user's eating speed. The response unit is realized, for example, by the control unit 46A of the smart glasses 214 and allows the avatar to engage in natural conversation using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, adjustment unit, and response unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives food images taken by a user with a smartphone. The reception unit can also receive food images uploaded from a personal computer via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the food images using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the eating speed of the avatar based on the user's eating speed. The response unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and allows the avatar to engage in natural conversation using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, adjustment unit, and response unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives food images taken by a user with a smartphone. The reception unit can also receive food images uploaded from a personal computer via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the food images using a generation AI. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the avatar's eating speed based on the user's eating speed. The response unit is realized, for example, by the control unit 46A of the robot 414 and allows the avatar to engage in natural conversation using a generation AI.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The shared eating system can further include a health management unit that monitors the user's health condition. The health management unit can evaluate the user's health condition based on the user's eating content and eating speed, and provide dietary advice as needed. For example, if the user continues to eat high-calorie meals, the health management unit can suggest low-calorie meals. Also, if the user eats too quickly, the health management unit can advise the user to eat more slowly. Furthermore, if the user is lacking in a particular nutrient, the health management unit can suggest meals containing that nutrient. This allows for dietary advice that takes the user's health condition into consideration, supporting a healthier diet.

[0098] The shared dining system may further include an environmental adjustment unit that improves the user's dining environment. The environmental adjustment unit may adjust lighting, music, temperature, and the like based on the user's dining environment. For example, if the user is dining in a dark environment, the environmental adjustment unit may brighten the lights. Also, if the user is dining in a quiet environment, the environmental adjustment unit may play relaxing music. Furthermore, if the user is dining in a cold environment, the environmental adjustment unit may raise the temperature. This may optimize the user's dining environment and provide a more comfortable dining experience.

[0099] The shared dining system can further include a history analysis unit that analyzes the user's meal history. The history analysis unit can analyze the user's past meal history and understand their eating patterns and preferences. For example, if the user frequently eats a particular dish, the history analysis unit can preferentially suggest that dish. Also, if the user tends to eat during a particular time of day, the history analysis unit can also suggest meals for that time of day. Furthermore, the nutritional balance of dishes the user has eaten in the past can be analyzed to suggest balanced meals. This makes it possible to utilize the user's meal history to make more personalized meal suggestions.

[0100] The shared eating system can further include a posture monitoring unit that monitors the user's posture while eating. The posture monitoring unit can analyze the user's posture while eating in real time and advise the user to maintain correct posture. For example, if the user is slouching, the posture monitoring unit can advise the user to straighten their back. Also, if the user is slouching while eating, the posture monitoring unit can encourage the user to maintain correct posture. Furthermore, if the user has been eating in the same position for a long time, the posture monitoring unit can advise the user to change their posture appropriately. This can improve the user's posture while eating and support healthy eating habits.

[0101] The dining system may further include a conversation recording unit that records conversations between users during meals and plays them back later. The conversation recording unit records conversations between users and avatars, allowing the users to play them back later and enjoy them. For example, if a user has an enjoyable conversation, the conversation can be recorded and played back later. Also, if a user speaks important information, the information can be recorded and checked later. Furthermore, if a user wants to play back a specific conversation, the conversation recording unit can search for and play back that conversation. This allows the user to enjoy the conversations they had during meals later, providing a more fulfilling dining experience.

[0102] The eating together system can further analyze the user's emotions and adjust the avatar's facial expression based on the analyzed user's emotions. For example, if the user is relaxed, the avatar will also have a relaxed expression. If the user is having fun, the avatar will also have a happy expression. If the user is sad, the avatar will also have a sympathetic expression. This allows the avatar's facial expression to be adjusted according to the user's emotions, providing a more natural eating together experience.

[0103] The eating together system can further analyze the user's emotions and adjust the tone of the avatar's voice based on the analyzed user's emotions. For example, if the user is relaxed, the avatar's voice can be made to have a relaxed tone. If the user is having fun, the avatar's voice can be made to have a happy tone. If the user is sad, the avatar's voice can be made to have a sympathetic tone. This allows the avatar's voice tone to be adjusted according to the user's emotions, providing a more natural eating together experience.

[0104] The eating together system can further analyze the user's emotions and adjust the avatar's behavior based on the analyzed user's emotions. For example, if the user is relaxed, the avatar will also behave in a relaxed manner. If the user is having fun, the avatar will also behave in a happy manner. If the user is sad, the avatar will also behave in a sympathetic manner. In this way, by adjusting the avatar's behavior according to the user's emotions, a more natural eating together experience can be provided.

[0105] The dining-together system can further analyze the user's emotions and select a topic for the avatar based on the analyzed user's emotions. For example, if the user is relaxed, a relaxing topic can be selected. If the user is having fun, a fun topic can be selected. If the user is sad, a topic that the user can empathize with can be selected. This allows for a more natural dining-together experience by selecting a topic for the avatar based on the user's emotions.

[0106] The shared eating system can further analyze the user's emotions and adjust the avatar's gestures based on the analyzed user's emotions. For example, if the user is relaxed, the avatar can make a relaxed gesture. If the user is having fun, the avatar can make a happy gesture. If the user is sad, the avatar can make a sympathetic gesture. This allows the avatar's gestures to be adjusted according to the user's emotions, providing a more natural shared eating experience.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives a meal image input by the user. Methods for the user to input a meal image include, but are not limited to, a photograph or a video. The reception unit receives, for example, a meal image taken by the user with a smartphone. The reception unit can also receive a meal image uploaded by the user from a PC. Furthermore, the reception unit can also receive a meal video taken by the user in real time. Step 2: The analysis unit uses the generation AI to analyze the food image input by the reception unit. The analysis is performed, for example, based on image recognition technology or an analysis algorithm, but is not limited to such examples. For example, the generation AI analyzes the food image using image recognition technology and selects a food similar to the food the avatar will eat. The analysis unit can also use the generation AI to perform analysis based on the resolution and format of the food image. Furthermore, the analysis unit can also use the generation AI to analyze the content of the food image and select a food the avatar will eat. Step 3: The adjustment unit uses the generation AI to adjust the avatar's eating speed based on the information analyzed by the analysis unit. The adjustment is made based on, for example, the user's eating speed and the time taken for each bite of the meal, but is not limited to such examples. For example, the generation AI analyzes the user's eating speed and adjusts the avatar's eating speed. The adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the time taken for each bite of the meal. Furthermore, the adjustment unit can also use the generation AI to adjust the avatar's eating speed based on the user's overall eating time. Step 4: The response unit uses the generation AI to have the avatar adjusted by the adjustment unit engage in natural chat. The chat is conducted based on, for example, a topic selection method and a conversation flow, but is not limited to such examples. For example, the generation AI analyzes the user's facial expressions, and the avatar engages in natural chat. The response unit can also use the generation AI to analyze the user's voice, and the avatar can engage in natural chat. Furthermore, the response unit can also use the generation AI to analyze the user's text, and the avatar can engage in natural chat.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a meal image of a user; an analysis unit that analyzes the food image input by the reception unit; an adjustment unit that controls the eating speed of the avatar based on the information analyzed by the analysis unit; a response unit that allows the avatar adjusted by the adjustment unit to chat with the user. A system characterized by:

2. The reception unit Analyzes the user's emotions and adjusts the timing of food image input based on the analyzed user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past food image history and select the appropriate input method 2. The system of claim 1.

4. The reception unit When inputting food images, filtering is performed based on the user's eating environment.

2. The system of claim 1.

5. The reception unit When inputting food images, select an appropriate input method based on the user's input method.

2. The system of claim 1.

6. The reception unit Analyzes the user's emotions and prioritizes the food images to be input based on the analyzed user's emotions.

2. The system of claim 1.

7. The reception unit When inputting food images, the system prioritizes inputting food images that are highly relevant based on the user's geographical location information.

2. The system of claim 1.

8. The reception unit When a user inputs a food image, the system analyzes the user's social media activity and inputs related food images.

2. The system of claim 1.

Images