system

Abstract

The system according to this embodiment aims to appropriately manage the speed of eating. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a decision unit, and a management unit. The reception unit inputs the contents of the spoon. The analysis unit analyzes the information input by the reception unit. The decision unit determines the time until the next bite based on the information analyzed by the analysis unit. The management unit manages the time spent on each bite based on the time determined by the decision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to appropriately manage the eating speed, and there is room for improvement to reduce health risks.

[0005] The system according to the embodiment aims to appropriately manage the eating speed.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a determination unit, and a management unit. The reception unit inputs the content of the spoon. The analysis unit analyzes the information input by the reception unit. The determination unit determines the time until the next bite based on the information analyzed by the analysis unit. The management unit manages the time taken for one bite based on the time determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately control the speed of eating. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meal management system according to an embodiment of the present invention is a system that manages the pace of eating using a generating AI agent. This meal management system receives input from the user regarding the contents of the food on their spoon, and the generating AI agent analyzes the input information to determine the time until the next bite. Furthermore, the generating AI agent manages the time spent on each bite, adjusting the speed of the meal. This mechanism allows people who eat quickly or those at risk of obesity or diabetes to manage their eating pace and develop healthy eating habits. For example, by following the instructions of the generating AI agent, the user can consciously control their eating speed and prevent a rapid rise in blood sugar levels. In this way, the meal management system can support the user in developing healthy eating habits.

[0029] The meal management system according to this embodiment comprises a reception unit, an analysis unit, a decision unit, and a management unit. The reception unit inputs the contents of the food the user has placed on the spoon. For example, the reception unit can input the type, quantity, and shape of the food the user has placed on the spoon. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit analyzes the eating speed and nutritional value based on the input contents of the food. The analysis unit can use a generation AI to analyze the contents of the food in detail. The decision unit determines the time until the next bite based on the information analyzed by the analysis unit. For example, the decision unit determines the time until the next bite based on the contents of the food and the user's health condition. The decision unit can use a generation AI to determine the optimal time. The management unit manages the time spent on each bite based on the time determined by the decision unit. For example, the management unit adjusts the time the user spends on each bite to manage the eating speed. The management unit can use AI to optimally manage the time spent on each bite. As a result, the meal management system according to this embodiment can help the user develop healthy eating habits.

[0030] The input system allows users to input the contents of the food they have placed on their spoon. For example, the input system can input the type, quantity, and shape of the food. Specifically, users can use a smartphone or tablet to select the type of food, input the quantity, and select the shape. For example, food types include vegetables, fruits, meat, fish, and grains, and the quantity can be entered in grams. Shape options include solid, liquid, and paste. Furthermore, the input system can use its camera function to take an image of the food on the spoon and analyze the image to automatically recognize the type and quantity of food. This allows users to easily input the contents of their food, saving them time and effort. The input system can also improve input efficiency by referring to the user's past input history and listing frequently eaten foods. For example, it can display frequently eaten foods in a list, allowing for one-click selection. This allows the input system to support users in smoothly inputting food contents, improving the convenience of the meal management system.

[0031] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit analyzes the eating speed and nutritional value based on the entered food content. The analysis unit can analyze the food content in detail using generative AI. Specifically, the generative AI analyzes the nutritional components of the food based on the type, quantity, and shape of the food entered. For example, in the case of vegetables, it analyzes the vitamin and mineral content, and in the case of meat, it analyzes the protein and fat content. Regarding eating speed, it analyzes the time the user spends on each bite and the pace at which they eat, and suggests an appropriate eating speed. The generative AI can learn the user's eating patterns by utilizing past data and statistical information, and can suggest the optimal eating speed and nutritional balance for each individual user. Furthermore, the analysis unit can also customize the content of meals according to the user's health condition and goals. For example, it can suggest low-calorie meals to users on a diet, and meals high in protein to users aiming to build muscle. In this way, the analysis unit can support the user's health management and provide meal plans that meet individual needs.

[0032] The decision unit determines the time to take the next bite based on the information analyzed by the analysis unit. For example, the decision unit determines the time to take the next bite based on the content of the food and the user's health condition. The decision unit can determine the optimal time using generative AI. Specifically, the generative AI calculates the optimal time to take the next bite based on the user's eating history and health data. For example, it adjusts the time to take the next bite considering the type and amount of food, the user's digestion speed, and fluctuations in blood sugar levels. This allows the user to eat at an appropriate pace, preventing overeating and eating too quickly. The decision unit can also adjust the time to take the next bite based on user feedback. For example, if the user feels that "the time to take the next bite is too long," the decision unit can reflect that feedback and shorten the time for the next bite. This allows the decision unit to respond flexibly to the individual needs of the user, enabling more effective meal management. Furthermore, the decision unit can also adjust the pace of eating according to the user's goals. For example, it can instruct users on a diet to eat slowly, and users aiming to build muscle to eat at a moderate pace. This allows the decision-making unit to support the user in achieving their goals and developing healthy eating habits.

[0033] The management unit controls the time spent on each bite based on the time determined by the decision unit. For example, the management unit adjusts the time a user spends on each bite to manage the speed of their meal. The management unit can use AI to optimally manage the time spent on each bite. Specifically, the management unit monitors the user's eating behavior and notifies them of the timing for the next bite based on the time determined by the decision unit. For example, it can notify the user of the timing for the next bite via voice or vibration through a smartphone or tablet app. The management unit can also record the user's eating behavior and analyze the speed and pace of their meal. This allows the user to objectively understand their eating habits and find areas for improvement. Furthermore, the management unit can provide real-time feedback on the user's eating behavior and adjust the speed of their meal. For example, if a user is eating too quickly, the management unit can notify them and instruct them to slow down. This allows the management unit to support the user in developing healthy eating habits and improve the quality of their meals. The management unit can also accumulate user meal data and perform long-term meal management. For example, based on past meal data, the system can analyze a user's eating patterns and nutritional balance, and propose an optimal meal plan for each individual user. This allows the management department to comprehensively support users' health management and help them achieve sustainable eating habits.

[0034] The meal management system includes an adjustment unit that controls the speed of eating. For example, the adjustment unit can adjust the speed at which the user eats. Using generative AI, the adjustment unit can optimally adjust the user's eating speed. For example, the adjustment unit can instruct the user to eat more slowly. Alternatively, the adjustment unit can instruct the user to eat more quickly. The adjustment unit can adjust the eating speed according to the user's health condition and the content of the meal. In this way, the meal management system can help users develop healthy eating habits.

[0035] The meal management system includes an instruction unit that guides the user through meals according to instructions from a generating AI agent. For example, the instruction unit might instruct the user to adjust their eating speed. Using the generating AI, the instruction unit can provide appropriate instructions to the user. For instance, it might instruct the user on the time to wait before taking the next bite. It can also instruct the user on the appropriate time to spend on each bite. The instruction unit can provide appropriate instructions based on the user's health condition and the content of their meal. This allows the meal management system to help users develop healthy eating habits.

[0036] The reception desk can analyze the user's past meal history and select the optimal input method. For example, the reception desk can suggest the optimal input method based on the food the user has previously entered. The reception desk can prioritize the input of frequently eaten foods based on the user's past meal history. The reception desk can analyze the user's past meal history and suggest an input method that promotes healthy eating. In this way, the optimal input method can be suggested by analyzing the past meal history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0037] The input system can filter food entries based on the user's current health status and dietary restrictions. For example, if the user has diabetes, the input system can filter out sugary foods. If the user has high blood pressure, the input system can filter out salty foods. If the user has allergies, the input system can filter out foods containing allergens. This allows for food filtering tailored to the user's health status and dietary restrictions. Some or all of the above processing in the input system may be performed using AI or not.

[0038] The reception system can prioritize inputting highly relevant foods by considering the user's geographical location when inputting food items. For example, if the user is in a cold region, the reception system can prioritize inputting hot foods. If the user is in a hot region, the reception system can prioritize inputting cold foods. If the user is in a specific region, the reception system can prioritize inputting local specialties of that region. This enables more appropriate meal management by inputting highly relevant foods based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI or not.

[0039] The reception desk can analyze the user's social media activity and input relevant foods when inputting food information. For example, the reception desk can prioritize inputting foods that the user has shared on social media. The reception desk can obtain information from food-related accounts that the user follows on social media and input relevant foods. The reception desk can prioritize inputting foods that the user has "liked" on social media. This allows for more appropriate dietary management by inputting relevant foods based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0040] The analysis unit can adjust the level of detail of the analysis based on the nutritional value of the food during the analysis. For example, the analysis unit can display detailed nutritional information for highly nutritious foods. For less nutritious foods, the analysis unit can display concise nutritional information. For foods containing a large amount of a particular nutrient, the analysis unit can display detailed information about that nutrient. By adjusting the level of detail of the analysis based on the nutritional value of the food, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0041] The analysis unit can apply different analysis algorithms depending on the food category during analysis. For example, in the case of vegetables, the analysis unit can apply analysis algorithms for vitamins and minerals. In the case of meat, the analysis unit can apply analysis algorithms for protein and fat. In the case of dessert, the analysis unit can apply analysis algorithms for sugar and calories. By applying different analysis algorithms depending on the food category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0042] The analysis unit can determine the priority of analysis based on the timing of food consumption. For example, the analysis unit may prioritize the analysis of food with an approaching expiration date. The analysis unit may prioritize the analysis of seasonal food. The analysis unit may prioritize the analysis of food that the user has recently consumed. By determining the priority of analysis based on the timing of food consumption, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0043] The analysis unit can adjust the order of analysis based on the relationships between foods during the analysis process. For example, the analysis unit can group foods of the same category together for analysis. The analysis unit can group foods with similar nutrients together for analysis. The analysis unit can prioritize the analysis of highly relevant foods based on the user's eating history. By adjusting the order of analysis based on the relationships between foods, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0044] The decision-making unit can adjust the time between bites based on the digestion rate of the food. For example, if the food is slow to digest, the decision-making unit can set a longer time between bites. If the food is fast to digest, the decision-making unit can set a shorter time between bites. If the food is moderately digestible, the decision-making unit can optimize the time between bites. This allows for more appropriate meal management by adjusting the time between bites based on the digestion rate of the food. Some or all of the above processing in the decision-making unit may be performed using generative AI, or it may be performed without using generative AI.

[0045] The decision-making unit can determine the optimal time by referring to the user's past eating pace. For example, if the user has eaten quickly in the past, the decision-making unit can set a longer time between bites. If the user has eaten slowly in the past, the decision-making unit can set a shorter time between bites. The decision-making unit can analyze the user's past eating pace and determine the optimal time. In this way, the optimal time can be determined by referring to the user's past eating pace. Some or all of the above processing in the decision-making unit may be performed using generative AI, or it may be performed without using generative AI.

[0046] The management unit can select the optimal management method by referring to the user's past eating pace during management. For example, if the user has eaten quickly in the past, the management unit can set a longer time for each bite. If the user has eaten slowly in the past, the management unit can set a shorter time for each bite. The management unit can analyze the user's past eating pace and select the optimal management method. In this way, the optimal management method can be selected by referring to the user's past eating pace. Some or all of the above processing in the management unit may be performed using generative AI, or it may be performed without using generative AI.

[0047] The management unit can customize the time taken for each bite based on the user's current health condition during management. For example, if the user is tired, the management unit can set a longer time for each bite. If the user is healthy, the management unit can set a standard time for each bite. If the user is unwell, the management unit can set a shorter time for each bite. This allows for more appropriate meal management by customizing the time taken for each bite according to the user's health condition. Some or all of the above processing in the management unit may be performed using generative AI, or it may be performed without using generative AI.

[0048] The management unit can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user is in a cold region, the management unit can set a longer time per bite. If the user is in a hot region, the management unit can set a shorter time per bite. If the user is in a specific region, the management unit can optimize the time per bite according to the climate of that region. This enables more appropriate meal management by selecting the optimal management method based on the user's geographical location information. Some or all of the above processing in the management unit may be performed using generative AI, or it may be performed without using generative AI.

[0049] The management department can analyze the user's social media activity during management to manage the time spent on each bite. For example, the management department can adjust the time spent on each bite based on the food the user has shared on social media. The management department can obtain information from food-related accounts that the user follows on social media and adjust the time spent on each bite. The management department can adjust the time spent on each bite based on the food the user has "liked" on social media. This allows for more appropriate meal management by managing the time spent on each bite based on the user's social media activity. Some or all of the above processes in the management department may be performed using generative AI, or they may not be performed using generative AI.

[0050] The adjustment unit can select the optimal adjustment method by referring to the user's past eating pace during the adjustment process. For example, if the user has eaten quickly in the past, the adjustment unit can set the eating speed to be slower. If the user has eaten slowly in the past, the adjustment unit can set the eating speed to be faster. The adjustment unit can analyze the user's past eating pace and select the optimal adjustment method. This allows the optimal adjustment method to be selected by referring to the user's past eating pace. Some or all of the above processing in the adjustment unit may be performed using generative AI, or it may be performed without using generative AI.

[0051] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, if the user is in a cold region, the adjustment unit can set the meal speed to be slower. If the user is in a hot region, the adjustment unit can set the meal speed to be faster. If the user is in a specific region, the adjustment unit can optimize the meal speed to match the climate of that region. This enables more appropriate meal management by selecting the optimal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using generative AI, or it may be performed without using generative AI.

[0052] The instruction unit can select the optimal instruction method by referring to the user's past eating history when issuing instructions. For example, the instruction unit can suggest the optimal instruction method based on the instruction methods the user has followed in the past. The instruction unit can select an effective instruction method from the user's past eating history. The instruction unit can analyze the user's past eating history and suggest an instruction method that promotes healthy eating. In this way, the optimal instruction method can be selected by referring to the user's past eating history. Some or all of the above processing in the instruction unit may be performed using generative AI, or it may be performed without using generative AI.

[0053] The instruction unit can select the optimal instruction method when giving instructions, taking into account the user's device information. For example, if the user is using a smartphone, the instruction unit can provide an instruction method that matches the screen size. If the user is using a tablet, the instruction unit can provide an instruction method optimized for a larger screen. If the user is using a smartwatch, the instruction unit can provide a concise and highly visible instruction method. This enables more appropriate dietary management by selecting the optimal instruction method based on the user's device information. Some or all of the above processing in the instruction unit may be performed using generative AI, or it may be performed without using generative AI.

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

[0055] The reception system learns the user's food preferences and can input food items based on those preferences. For example, if a user likes a particular ingredient, the system will prioritize inputting foods containing that ingredient. If a user likes a particular dish, the system can prioritize inputting foods containing that dish. Furthermore, if a user likes a particular cooking method, the system can prioritize inputting foods prepared using that cooking method. This allows for more appropriate dietary management by inputting food items based on the user's food preferences.

[0056] The analysis unit can analyze the nutritional balance of the user's meal and determine the time to take the next bite based on that balance. For example, if the user's meal is nutritionally balanced, the time to take the next bite will be set shorter. If the user's meal is nutritionally unbalanced, the analysis unit can set the time to take the next bite longer. Furthermore, if the user's meal is skewed towards a particular nutrient, the analysis unit can adjust the time to take the next bite according to that nutrient. This allows for more appropriate meal management by determining the time to take the next bite based on the nutritional balance of the user's meal.

[0057] The instruction unit can monitor the user's eating progress and issue instructions based on that progress. For example, if the user is eating smoothly, it can instruct them to take the next bite. If the user is eating slowly, the instruction unit can instruct them to speed up their eating. Conversely, if the user is eating too quickly, it can instruct them to slow down their eating. This allows for more appropriate meal management by providing instructions based on the user's eating progress.

[0058] The system can input food information considering the user's dining environment. For example, if the user is dining in a quiet environment, it can input foods that have a relaxing effect. If the user is dining in a noisy environment, it can input easily digestible foods. Furthermore, if the user is dining in a specific location, it can input foods that are appropriate for that location. This allows for more appropriate dietary management by inputting food information based on the user's dining environment.

[0059] The analysis unit can analyze the user's eating history and adjust the eating speed based on that history. For example, if the user has a tendency to eat quickly in the past, the system can adjust the eating speed to be slower. If the user has a tendency to eat slowly in the past, the system can adjust the eating speed to be faster. The analysis unit can also suggest the optimal eating speed based on the user's eating history. This allows for more appropriate meal management by adjusting the eating speed based on the user's eating history.

[0060] The management unit can monitor the user's eating progress and adjust the time spent on each bite based on that progress. For example, if the user is eating smoothly, the management unit can set a shorter time per bite. If the user is eating slowly, the management unit can set a longer time per bite. Furthermore, if the user is eating quickly, the management unit can optimize the time per bite. This allows for more appropriate meal management by managing the time spent on each bite based on the user's eating progress.

[0061] The instruction unit can provide mealtime instructions that take into account the user's eating environment. For example, if the user is eating in a quiet environment, it can provide instructions that promote relaxation. If the user is eating in a noisy environment, the instruction unit can provide concise and clear instructions. Furthermore, if the user is eating in a specific location, the instruction unit can provide instructions appropriate to that location. This allows for more appropriate mealtime management by providing mealtime instructions based on the user's eating environment.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk inputs the contents of the food the user has placed on the spoon. For example, the user can input the type, quantity, and shape of the food on the spoon. Step 2: The analysis unit analyzes the information entered by the reception unit. For example, it analyzes the speed of eating and nutritional value based on the entered food information. The analysis unit can use generating AI to analyze the food information in detail. Step 3: The decision unit determines the time until the next bite based on the information analyzed by the analysis unit. For example, it may determine the time until the next bite based on the type of food and the user's health condition. The decision unit can use generative AI to determine the optimal time. Step 4: The management unit manages the time spent on each bite based on the time determined by the decision unit. For example, the user can adjust the time spent on each bite to manage the speed of eating. The management unit can use AI to optimally manage the time spent on each bite.

[0064] (Example of form 2) The meal management system according to an embodiment of the present invention is a system that manages the pace of eating using a generating AI agent. This meal management system receives input from the user regarding the contents of the food on their spoon, and the generating AI agent analyzes the input information to determine the time until the next bite. Furthermore, the generating AI agent manages the time spent on each bite, adjusting the speed of the meal. This mechanism allows people who eat quickly or those at risk of obesity or diabetes to manage their eating pace and develop healthy eating habits. For example, by following the instructions of the generating AI agent, the user can consciously control their eating speed and prevent a rapid rise in blood sugar levels. In this way, the meal management system can support the user in developing healthy eating habits.

[0065] The meal management system according to this embodiment comprises a reception unit, an analysis unit, a decision unit, and a management unit. The reception unit inputs the contents of the food the user has placed on the spoon. For example, the reception unit can input the type, quantity, and shape of the food the user has placed on the spoon. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit analyzes the eating speed and nutritional value based on the input contents of the food. The analysis unit can use a generation AI to analyze the contents of the food in detail. The decision unit determines the time until the next bite based on the information analyzed by the analysis unit. For example, the decision unit determines the time until the next bite based on the contents of the food and the user's health condition. The decision unit can use a generation AI to determine the optimal time. The management unit manages the time spent on each bite based on the time determined by the decision unit. For example, the management unit adjusts the time the user spends on each bite to manage the eating speed. The management unit can use AI to optimally manage the time spent on each bite. As a result, the meal management system according to this embodiment can help the user develop healthy eating habits.

[0066] The input system allows users to input the contents of the food they have placed on their spoon. For example, the input system can input the type, quantity, and shape of the food. Specifically, users can use a smartphone or tablet to select the type of food, input the quantity, and select the shape. For example, food types include vegetables, fruits, meat, fish, and grains, and the quantity can be entered in grams. Shape options include solid, liquid, and paste. Furthermore, the input system can use its camera function to take an image of the food on the spoon and analyze the image to automatically recognize the type and quantity of food. This allows users to easily input the contents of their food, saving them time and effort. The input system can also improve input efficiency by referring to the user's past input history and listing frequently eaten foods. For example, it can display frequently eaten foods in a list, allowing for one-click selection. This allows the input system to support users in smoothly inputting food contents, improving the convenience of the meal management system.

[0067] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit analyzes the eating speed and nutritional value based on the entered food content. The analysis unit can analyze the food content in detail using generative AI. Specifically, the generative AI analyzes the nutritional components of the food based on the type, quantity, and shape of the food entered. For example, in the case of vegetables, it analyzes the vitamin and mineral content, and in the case of meat, it analyzes the protein and fat content. Regarding eating speed, it analyzes the time the user spends on each bite and the pace at which they eat, and suggests an appropriate eating speed. The generative AI can learn the user's eating patterns by utilizing past data and statistical information, and can suggest the optimal eating speed and nutritional balance for each individual user. Furthermore, the analysis unit can also customize the content of meals according to the user's health condition and goals. For example, it can suggest low-calorie meals to users on a diet, and meals high in protein to users aiming to build muscle. In this way, the analysis unit can support the user's health management and provide meal plans that meet individual needs.

[0068] The decision unit determines the time to take the next bite based on the information analyzed by the analysis unit. For example, the decision unit determines the time to take the next bite based on the content of the food and the user's health condition. The decision unit can determine the optimal time using generative AI. Specifically, the generative AI calculates the optimal time to take the next bite based on the user's eating history and health data. For example, it adjusts the time to take the next bite considering the type and amount of food, the user's digestion speed, and fluctuations in blood sugar levels. This allows the user to eat at an appropriate pace, preventing overeating and eating too quickly. The decision unit can also adjust the time to take the next bite based on user feedback. For example, if the user feels that "the time to take the next bite is too long," the decision unit can reflect that feedback and shorten the time for the next bite. This allows the decision unit to respond flexibly to the individual needs of the user, enabling more effective meal management. Furthermore, the decision unit can also adjust the pace of eating according to the user's goals. For example, it can instruct users on a diet to eat slowly, and users aiming to build muscle to eat at a moderate pace. This allows the decision-making unit to support the user in achieving their goals and developing healthy eating habits.

[0069] The management unit controls the time spent on each bite based on the time determined by the decision unit. For example, the management unit adjusts the time a user spends on each bite to manage the speed of their meal. The management unit can use AI to optimally manage the time spent on each bite. Specifically, the management unit monitors the user's eating behavior and notifies them of the timing for the next bite based on the time determined by the decision unit. For example, it can notify the user of the timing for the next bite via voice or vibration through a smartphone or tablet app. The management unit can also record the user's eating behavior and analyze the speed and pace of their meal. This allows the user to objectively understand their eating habits and find areas for improvement. Furthermore, the management unit can provide real-time feedback on the user's eating behavior and adjust the speed of their meal. For example, if a user is eating too quickly, the management unit can notify them and instruct them to slow down. This allows the management unit to support the user in developing healthy eating habits and improve the quality of their meals. The management unit can also accumulate user meal data and perform long-term meal management. For example, based on past meal data, the system can analyze a user's eating patterns and nutritional balance, and propose an optimal meal plan for each individual user. This allows the management department to comprehensively support users' health management and help them achieve sustainable eating habits.

[0070] The meal management system includes an adjustment unit that controls the speed of eating. For example, the adjustment unit can adjust the speed at which the user eats. Using generative AI, the adjustment unit can optimally adjust the user's eating speed. For example, the adjustment unit can instruct the user to eat more slowly. Alternatively, the adjustment unit can instruct the user to eat more quickly. The adjustment unit can adjust the eating speed according to the user's health condition and the content of the meal. In this way, the meal management system can help users develop healthy eating habits.

[0071] The meal management system includes an instruction unit that guides the user through meals according to instructions from a generating AI agent. For example, the instruction unit might instruct the user to adjust their eating speed. Using the generating AI, the instruction unit can provide appropriate instructions to the user. For instance, it might instruct the user on the time to wait before taking the next bite. It can also instruct the user on the appropriate time to spend on each bite. The instruction unit can provide appropriate instructions based on the user's health condition and the content of their meal. This allows the meal management system to help users develop healthy eating habits.

[0072] The reception desk can estimate the user's emotions and adjust the timing of food input based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the timing of food input. If the user is relaxed, the reception desk can speed up the timing of food input. If the user is in a hurry, the reception desk can optimize the timing of food input. This allows for more appropriate meal management by adjusting the timing of food input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The reception desk can analyze the user's past meal history and select the optimal input method. For example, the reception desk can suggest the optimal input method based on the food the user has previously entered. The reception desk can prioritize the input of frequently eaten foods based on the user's past meal history. The reception desk can analyze the user's past meal history and suggest an input method that promotes healthy eating. In this way, the optimal input method can be suggested by analyzing the past meal history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0074] The input system can filter food entries based on the user's current health status and dietary restrictions. For example, if the user has diabetes, the input system can filter out sugary foods. If the user has high blood pressure, the input system can filter out salty foods. If the user has allergies, the input system can filter out foods containing allergens. This allows for food filtering tailored to the user's health status and dietary restrictions. Some or all of the above processing in the input system may be performed using AI or not.

[0075] The reception system can estimate the user's emotions and determine the priority of food input based on those emotions. For example, if the user is stressed, the reception system will prioritize foods with relaxing effects. If the user is relaxed, the reception system can prioritize foods with good nutritional balance. If the user is in a hurry, the reception system can prioritize easily digestible foods. This allows for more appropriate meal management by prioritizing food according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception system can prioritize inputting highly relevant foods by considering the user's geographical location when inputting food items. For example, if the user is in a cold region, the reception system can prioritize inputting hot foods. If the user is in a hot region, the reception system can prioritize inputting cold foods. If the user is in a specific region, the reception system can prioritize inputting local specialties of that region. This enables more appropriate meal management by inputting highly relevant foods based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI or not.

[0077] The reception desk can analyze the user's social media activity and input relevant foods when inputting food information. For example, the reception desk can prioritize inputting foods that the user has shared on social media. The reception desk can obtain information from food-related accounts that the user follows on social media and input relevant foods. The reception desk can prioritize inputting foods that the user has "liked" on social media. This allows for more appropriate dietary management by inputting relevant foods based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in detail. If the user is in a hurry, the analysis unit can display the analysis results concisely. If the user is stressed, the analysis unit can display the analysis results in a visually easy-to-understand manner. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can adjust the level of detail of the analysis based on the nutritional value of the food during the analysis. For example, the analysis unit can display detailed nutritional information for highly nutritious foods. For less nutritious foods, the analysis unit can display concise nutritional information. For foods containing a large amount of a particular nutrient, the analysis unit can display detailed information about that nutrient. By adjusting the level of detail of the analysis based on the nutritional value of the food, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0080] The analysis unit can apply different analysis algorithms depending on the food category during analysis. For example, in the case of vegetables, the analysis unit can apply analysis algorithms for vitamins and minerals. In the case of meat, the analysis unit can apply analysis algorithms for protein and fat. In the case of dessert, the analysis unit can apply analysis algorithms for sugar and calories. By applying different analysis algorithms depending on the food category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can display the analysis results briefly and to the point. If the user is relaxed, the analysis unit can display detailed analysis results. If the user is stressed, the analysis unit can display the analysis results concisely. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can determine the priority of analysis based on the timing of food consumption. For example, the analysis unit may prioritize the analysis of food with an approaching expiration date. The analysis unit may prioritize the analysis of seasonal food. The analysis unit may prioritize the analysis of food that the user has recently consumed. By determining the priority of analysis based on the timing of food consumption, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0083] The analysis unit can adjust the order of analysis based on the relationships between foods during the analysis process. For example, the analysis unit can group foods of the same category together for analysis. The analysis unit can group foods with similar nutrients together for analysis. The analysis unit can prioritize the analysis of highly relevant foods based on the user's eating history. By adjusting the order of analysis based on the relationships between foods, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0084] The decision-making unit can estimate the user's emotions and determine the time until the next bite based on the estimated emotions. For example, if the user is relaxed, the decision-making unit can set a longer time until the next bite. If the user is in a hurry, the decision-making unit can set a shorter time until the next bite. If the user is stressed, the decision-making unit can optimize the time until the next bite. This allows for more appropriate meal management by determining the time until the next bite according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The decision-making unit can adjust the time between bites based on the digestion rate of the food. For example, if the food is slow to digest, the decision-making unit can set a longer time between bites. If the food is fast to digest, the decision-making unit can set a shorter time between bites. If the food is moderately digestible, the decision-making unit can optimize the time between bites. This allows for more appropriate meal management by adjusting the time between bites based on the digestion rate of the food. Some or all of the above processing in the decision-making unit may be performed using generative AI, or it may be performed without using generative AI.

[0086] The decision-making unit can determine the optimal time by referring to the user's past eating pace. For example, if the user has eaten quickly in the past, the decision-making unit can set a longer time between bites. If the user has eaten slowly in the past, the decision-making unit can set a shorter time between bites. The decision-making unit can analyze the user's past eating pace and determine the optimal time. In this way, the optimal time can be determined by referring to the user's past eating pace. Some or all of the above processing in the decision-making unit may be performed using generative AI, or it may be performed without using generative AI.

[0087] The management unit can estimate the user's emotions and manage the time spent on each bite based on those emotions. For example, if the user is relaxed, the management unit can set a longer bite time. If the user is in a hurry, the management unit can set a shorter bite time. If the user is stressed, the management unit can optimize the bite time. This allows for more appropriate meal management by managing the time spent on each bite according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The management unit can select the optimal management method by referring to the user's past eating pace during management. For example, if the user has eaten quickly in the past, the management unit can set a longer time for each bite. If the user has eaten slowly in the past, the management unit can set a shorter time for each bite. The management unit can analyze the user's past eating pace and select the optimal management method. In this way, the optimal management method can be selected by referring to the user's past eating pace. Some or all of the above processing in the management unit may be performed using generative AI, or it may be performed without using generative AI.

[0089] The management unit can customize the time taken for each bite based on the user's current health condition during management. For example, if the user is tired, the management unit can set a longer time for each bite. If the user is healthy, the management unit can set a standard time for each bite. If the user is unwell, the management unit can set a shorter time for each bite. This allows for more appropriate meal management by customizing the time taken for each bite according to the user's health condition. Some or all of the above processing in the management unit may be performed using generative AI, or it may be performed without using generative AI.

[0090] The management system can estimate the user's emotions and, based on those emotions, prioritize the time spent on each bite. For example, if the user is relaxed, the management system can set a longer time for each bite. If the user is in a hurry, the management system can set a shorter time for each bite. If the user is stressed, the management system can optimize the time spent on each bite. This allows for more appropriate meal management by prioritizing the time spent on each bite according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The management unit can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user is in a cold region, the management unit can set a longer time per bite. If the user is in a hot region, the management unit can set a shorter time per bite. If the user is in a specific region, the management unit can optimize the time per bite according to the climate of that region. This enables more appropriate meal management by selecting the optimal management method based on the user's geographical location information. Some or all of the above processing in the management unit may be performed using generative AI, or it may be performed without using generative AI.

[0092] The management department can analyze the user's social media activity during management to manage the time spent on each bite. For example, the management department can adjust the time spent on each bite based on the food the user has shared on social media. The management department can obtain information from food-related accounts that the user follows on social media and adjust the time spent on each bite. The management department can adjust the time spent on each bite based on the food the user has "liked" on social media. This allows for more appropriate meal management by managing the time spent on each bite based on the user's social media activity. Some or all of the above processes in the management department may be performed using generative AI, or they may not be performed using generative AI.

[0093] The adjustment unit can estimate the user's emotions and adjust the meal speed based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can set the meal speed slower. If the user is in a hurry, the adjustment unit can set the meal speed faster. If the user is stressed, the adjustment unit can optimize the meal speed. This allows for more appropriate meal management by adjusting the meal speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The adjustment unit can select the optimal adjustment method by referring to the user's past eating pace during the adjustment process. For example, if the user has eaten quickly in the past, the adjustment unit can set the eating speed to be slower. If the user has eaten slowly in the past, the adjustment unit can set the eating speed to be faster. The adjustment unit can analyze the user's past eating pace and select the optimal adjustment method. This allows the optimal adjustment method to be selected by referring to the user's past eating pace. Some or all of the above processing in the adjustment unit may be performed using generative AI, or it may be performed without using generative AI.

[0095] The adjustment unit can estimate the user's emotions and determine the priority of the meal speed based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can set the meal speed slower. If the user is in a hurry, the adjustment unit can set the meal speed faster. If the user is stressed, the adjustment unit can optimize the meal speed. This allows for more appropriate meal management by determining the priority of the meal speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The adjustment unit can select the optimal adjustment method during adjustment, taking into account the user's geographical location information. For example, if the user is in a cold region, the adjustment unit can set the meal speed to be slower. If the user is in a hot region, the adjustment unit can set the meal speed to be faster. If the user is in a specific region, the adjustment unit can optimize the meal speed to match the climate of that region. This enables more appropriate meal management by selecting the optimal adjustment method based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using generative AI, or it may be performed without using generative AI.

[0097] The instruction unit can estimate the user's emotions and adjust its meal instructions based on those emotions. For example, if the user is relaxed, the instruction unit will give instructions in a calm tone. If the user is in a hurry, the instruction unit can give quick and concise instructions. If the user is stressed, the instruction unit can give instructions in a gentle tone. This allows for more appropriate meal management by adjusting meal instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The instruction unit can select the optimal instruction method by referring to the user's past eating history when issuing instructions. For example, the instruction unit can suggest the optimal instruction method based on the instruction methods the user has followed in the past. The instruction unit can select an effective instruction method from the user's past eating history. The instruction unit can analyze the user's past eating history and suggest an instruction method that promotes healthy eating. In this way, the optimal instruction method can be selected by referring to the user's past eating history. Some or all of the above processing in the instruction unit may be performed using generative AI, or it may be performed without using generative AI.

[0099] The instruction unit can estimate the user's emotions and prioritize meal instructions based on those emotions. For example, if the user is relaxed, the instruction unit will give instructions in a calm tone. If the user is in a hurry, the instruction unit can give quick and concise instructions. If the user is stressed, the instruction unit can give instructions in a gentle tone. This allows for more appropriate meal management by prioritizing meal instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The instruction unit can select the optimal instruction method when giving instructions, taking into account the user's device information. For example, if the user is using a smartphone, the instruction unit can provide an instruction method that matches the screen size. If the user is using a tablet, the instruction unit can provide an instruction method optimized for a larger screen. If the user is using a smartwatch, the instruction unit can provide a concise and highly visible instruction method. This enables more appropriate dietary management by selecting the optimal instruction method based on the user's device information. Some or all of the above processing in the instruction unit may be performed using generative AI, or it may be performed without using generative AI.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception system learns the user's food preferences and can input food items based on those preferences. For example, if a user likes a particular ingredient, the system will prioritize inputting foods containing that ingredient. If a user likes a particular dish, the system can prioritize inputting foods containing that dish. Furthermore, if a user likes a particular cooking method, the system can prioritize inputting foods prepared using that cooking method. This allows for more appropriate dietary management by inputting food items based on the user's food preferences.

[0103] The analysis unit can analyze the nutritional balance of the user's meal and determine the time to take the next bite based on that balance. For example, if the user's meal is nutritionally balanced, the time to take the next bite will be set shorter. If the user's meal is nutritionally unbalanced, the analysis unit can set the time to take the next bite longer. Furthermore, if the user's meal is skewed towards a particular nutrient, the analysis unit can adjust the time to take the next bite according to that nutrient. This allows for more appropriate meal management by determining the time to take the next bite based on the nutritional balance of the user's meal.

[0104] The management department can estimate the user's satisfaction level with their meal and manage the time spent on each bite based on that satisfaction level. For example, if the user is satisfied with their meal, the management department can set a longer time per bite. If the user is not satisfied with their meal, the management department can set a shorter time per bite. Furthermore, if the user's satisfaction level is moderate, the management department can optimize the time spent on each bite. This allows for more appropriate meal management by managing the time spent on each bite based on the user's satisfaction level with their meal.

[0105] The instruction unit can monitor the user's eating progress and issue instructions based on that progress. For example, if the user is eating smoothly, it can instruct them to take the next bite. If the user is eating slowly, the instruction unit can instruct them to speed up their eating. Conversely, if the user is eating too quickly, it can instruct them to slow down their eating. This allows for more appropriate meal management by providing instructions based on the user's eating progress.

[0106] The system can input food information considering the user's dining environment. For example, if the user is dining in a quiet environment, it can input foods that have a relaxing effect. If the user is dining in a noisy environment, it can input easily digestible foods. Furthermore, if the user is dining in a specific location, it can input foods that are appropriate for that location. This allows for more appropriate dietary management by inputting food information based on the user's dining environment.

[0107] The analysis unit can analyze the user's eating history and adjust the eating speed based on that history. For example, if the user has a tendency to eat quickly in the past, the system can adjust the eating speed to be slower. If the user has a tendency to eat slowly in the past, the system can adjust the eating speed to be faster. The analysis unit can also suggest the optimal eating speed based on the user's eating history. This allows for more appropriate meal management by adjusting the eating speed based on the user's eating history.

[0108] The decision-making unit can estimate the user's satisfaction level with their meal and determine the time until the next bite based on that satisfaction level. For example, if the user is satisfied with their meal, the unit can set a shorter time until the next bite. If the user is not satisfied with their meal, the unit can set a longer time until the next bite. Furthermore, if the user's satisfaction level is moderate, the unit can optimize the time until the next bite. This allows for more appropriate meal management by determining the time until the next bite based on the user's satisfaction level with their meal.

[0109] The management unit can monitor the user's eating progress and adjust the time spent on each bite based on that progress. For example, if the user is eating smoothly, the management unit can set a shorter time per bite. If the user is eating slowly, the management unit can set a longer time per bite. Furthermore, if the user is eating quickly, the management unit can optimize the time per bite. This allows for more appropriate meal management by managing the time spent on each bite based on the user's eating progress.

[0110] The instruction unit can provide mealtime instructions that take into account the user's eating environment. For example, if the user is eating in a quiet environment, it can provide instructions that promote relaxation. If the user is eating in a noisy environment, the instruction unit can provide concise and clear instructions. Furthermore, if the user is eating in a specific location, the instruction unit can provide instructions appropriate to that location. This allows for more appropriate mealtime management by providing mealtime instructions based on the user's eating environment.

[0111] The adjustment unit can estimate the user's satisfaction with their meal and adjust the eating speed based on that satisfaction level. For example, if the user is satisfied with their meal, the eating speed can be set to be faster. If the user is not satisfied with their meal, the adjustment unit can set the eating speed to be slower. Furthermore, if the user's satisfaction level is moderate, the adjustment unit can optimize the eating speed. This allows for more appropriate meal management by adjusting the eating speed based on the user's satisfaction level.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk inputs the contents of the food the user has placed on the spoon. For example, the user can input the type, quantity, and shape of the food on the spoon. Step 2: The analysis unit analyzes the information entered by the reception unit. For example, it analyzes the speed of eating and nutritional value based on the entered food information. The analysis unit can use generating AI to analyze the food information in detail. Step 3: The decision unit determines the time until the next bite based on the information analyzed by the analysis unit. For example, it may determine the time until the next bite based on the type of food and the user's health condition. The decision unit can use generative AI to determine the optimal time. Step 4: The management unit manages the time spent on each bite based on the time determined by the decision unit. For example, the user can adjust the time spent on each bite to manage the speed of eating. The management unit can use AI to optimally manage the time spent on each bite.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, decision unit, management unit, adjustment unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the contents of the food the user has placed on the spoon. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, which determines the time until the next bite. The management unit is implemented by the control unit 46A of the smart device 14, which manages the time spent on each bite. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the speed at which the user eats. The instruction unit is implemented by the control unit 46A of the smart device 14, which instructs the user to adjust the speed at which they eat. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, decision unit, management unit, adjustment unit, and instruction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs the contents of the food the user has placed on the spoon. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input information. The decision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which determines the time until the next bite. The management unit is implemented, for example, by the control unit 46A of the smart glasses 214, which manages the time spent on each bite. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which adjusts the speed at which the user eats. The instruction unit is implemented, for example, by the control unit 46A of the smart glasses 214, which instructs the user to adjust the speed at which they eat. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, decision unit, management unit, adjustment unit, and instruction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which inputs the contents of the food the user has placed on the spoon. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which determines the time until the next bite. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314, which manages the time spent on each bite. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which adjusts the speed at which the user eats. The instruction unit is implemented by, for example, the control unit 46A of the headset terminal 314, which instructs the user to adjust the speed at which they eat. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, decision unit, management unit, adjustment unit, and instruction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs the contents of the food the user has placed on the spoon. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The decision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which determines the time until the next bite. The management unit is implemented, for example, by the control unit 46A of the robot 414, which manages the time taken for each bite. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which adjusts the speed at which the user eats. The instruction unit is implemented, for example, by the control unit 46A of the robot 414, which instructs the user to adjust the speed at which they eat. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The reception area where you enter the details of the spoon, An analysis unit analyzes the information input by the reception unit, A determination unit determines the time until the next bite based on the information analyzed by the aforementioned analysis unit, The system includes a management unit that manages the time spent on each bite based on the time determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) It has an adjustment unit to control the speed of eating. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a control unit that allows the user to eat according to the instructions of a generated AI agent. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of food input based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system analyzes the user's past meal history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter food items, filtering is performed based on their current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of food items to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter food items, the system prioritizes highly relevant food items by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter food information, the system analyzes their social media activity and inputs relevant food items. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the nutritional value of the food. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the food category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the food was consumed. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between foods. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned determination unit, It estimates the user's emotions and determines the time until the next bite based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned determination unit, When making a decision, adjust the time between bites based on the rate at which food is digested. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned determination unit, When making a decision, the system will refer to the user's past eating habits to determine the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates the user's emotions and manages the amount of time spent on each bite based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the system selects the optimal management method by referring to the user's past eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, the amount of time spent per bite is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, It estimates the user's emotions and, based on those emotions, determines the priority of how long each bite should take. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, we analyze users' social media activity to manage the time they spend on each task. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It estimates the user's emotions and adjusts the meal speed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The adjustment unit is, During the adjustment process, the system selects the optimal adjustment method by referring to the user's past eating habits. The system described in Appendix 2, characterized by the features described herein. (Note 27) The adjustment unit is, It estimates the user's emotions and determines the priority of the meal speed based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The indicator unit is, It estimates the user's emotions and adjusts the meal instructions based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The indicator unit is, When giving instructions, the system selects the most appropriate instruction method by referring to the user's past meal history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The indicator unit is, It estimates the user's emotions and prioritizes meal instructions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The indicator unit is, When giving instructions, the system selects the optimal instruction method, taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where you enter the details of the spoon, An analysis unit analyzes the information input by the reception unit, A determination unit determines the time until the next bite based on the information analyzed by the aforementioned analysis unit, The system includes a management unit that manages the time spent on each bite based on the time determined by the aforementioned determination unit. A system characterized by the following features.

2. It has an adjustment unit to control the speed of eating. The system according to feature 1.

3. It includes a control unit that allows the user to eat according to the instructions of a generated AI agent. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of food input based on those emotions. The system according to feature 1.

5. The aforementioned reception unit is The system analyzes the user's past meal history and selects the optimal input method. The system according to feature 1.

6. The aforementioned reception unit is When users enter food items, filtering is performed based on their current health status and dietary restrictions. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of food items to input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When users enter food items, the system prioritizes highly relevant food items by considering their geographical location. The system according to feature 1.

9. The aforementioned reception unit is When users enter food information, the system analyzes their social media activity and inputs relevant food items. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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