system

Abstract

The system according to the embodiment aims to provide an optimal diet method based on the user's body shape and constitution data. [Solution] A system according to an embodiment includes an input unit, an analysis unit, an estimation unit, and a provision unit. The input unit inputs data on a user's body shape and constitution. The analysis unit analyzes the data input by the input unit. The estimation unit compares past data based on the data analyzed by the analysis unit and estimates an appropriate diet method. The provision unit provides the user with the diet method estimated by the estimation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to find an optimal diet method for each individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal diet method based on the user's body shape and constitution data. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, an estimation unit, and a provision unit. The input unit inputs data on the user's body shape and constitution. The analysis unit analyzes the data input by the input unit. The estimation unit compares past data based on the data analyzed by the analysis unit and estimates an appropriate diet method. The provision unit provides the user with the diet method estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal diet method based on the user's body shape and constitution data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A diet advice system according to an embodiment of the present invention recognizes a user's body shape and constitution data and provides detailed advice on a diet method suited to that individual. In the diet advice system, a user inputs their own body shape and constitution data, and an AI system compares the data with data from similar individuals who have successfully lost weight in the past. The system then statistically calculates the optimal diet method and provides it to the user as advice. For example, the diet advice system allows the user to input detailed data such as height, weight, body fat percentage, dietary habits, and exercise habits. The diet advice system then uses an AI system to analyze the input data and compare it with data from similar individuals who have successfully lost weight in the past. For example, if people with similar body shapes and constitutions have been successful with a low-carbohydrate diet, the system determines that this method is optimal. Finally, the diet advice system provides the calculated optimal diet method to the user as advice. For example, the system may provide specific advice to the user, such as "Try a low-carbohydrate diet." This advice may also include specific meal plans and exercise plans. This allows the diet advice system to identify a diet method suited to the user's body shape and constitution, enabling the user to effectively diet. This allows the diet advice system to provide the optimal diet method based on the user's body shape and constitution. For example, trying methods that have been successful for people with similar body types and constitutions in the past can increase your chances of success.

[0029] A diet advice system according to an embodiment includes an input unit, an analysis unit, an estimation unit, and a provision unit. The input unit inputs data on a user's body shape and constitution. The data on a user's body shape and constitution includes, but is not limited to, height, weight, body fat percentage, diet, and exercise habits. The input unit can input, for example, data such as a user's height of 170 cm, weight of 70 kg, body fat percentage of 25%, high-calorie diet, and exercise habit of about once a week. The analysis unit uses AI to analyze the data input by the input unit. The analysis unit includes, for example, a function to automatically acquire the user's body shape data. For example, the analysis unit can automatically acquire the user's body shape data using a sensor. The analysis unit can also acquire the user's body shape data using image analysis technology. The estimation unit uses AI to compare past data based on the data analyzed by the analysis unit and estimate an optimal diet method. For example, the estimation unit compares data from similar individuals who have successfully dieted in the past and statistically calculates an optimal diet method. For example, if people with similar body shapes and constitutions have been successful with a low-carbohydrate diet, the system determines that the method is optimal. The providing unit provides the user with the diet method estimated by the estimation unit. The providing unit provides the user with specific advice, such as "Try a low-carbohydrate diet." The providing unit can also provide specific meal plans and exercise plans. For example, a detailed plan such as "Eat mainly eggs and vegetables for breakfast, chicken and salad for lunch, and fish and vegetables for dinner" is provided. This allows the diet advice system according to the embodiment to provide the optimal diet method based on the user's body shape and constitution. For example, trying a method that has been successful for people with similar body shapes and constitutions in the past increases the probability of success.

[0030] The analysis unit can automatically acquire the user's body shape data. Examples of automatic acquisition include, but are not limited to, measurements using a sensor and image analysis technology. The analysis unit automatically acquires the user's body shape data using, for example, a sensor. For example, the analysis unit measures the user's height and weight using a sensor and automatically acquires the data. The analysis unit can also acquire the user's body shape data using image analysis technology. For example, the analysis unit takes a full-body photo of the user and estimates the height and weight using image analysis technology. This automatically acquires the user's body shape data, thereby eliminating the need for data input. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data acquired by a sensor into AI, which then analyzes the data to acquire body shape data.

[0031] The estimation unit can estimate the optimal diet method using AI. AI includes, but is not limited to, technologies such as neural networks and deep learning. The estimation unit estimates the optimal diet method using, for example, a neural network. For example, the estimation unit inputs past data into a neural network to estimate the optimal diet method. The estimation unit can also estimate the optimal diet method using deep learning. For example, the estimation unit inputs past data into a deep learning model to estimate the optimal diet method. In this way, by using AI, the optimal diet method can be estimated with high accuracy. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI. For example, the estimation unit inputs past data into AI, and the AI ​​can estimate the optimal diet method.

[0032] The providing unit can provide detailed meal plans and exercise plans. The detailed meal plans include, for example, a daily meal menu and nutritional balance, but are not limited to these examples. The providing unit provides, for example, a daily meal menu. For example, the providing unit advises the user to eat mainly eggs and vegetables for breakfast, chicken and salad for lunch, and fish and vegetables for dinner. The providing unit can also provide meal plans that take nutritional balance into consideration. For example, the providing unit provides a meal plan that takes into consideration the balance of protein, fat, and carbohydrates. The exercise plan includes, for example, a weekly exercise menu and the type and intensity of exercise, but are not limited to these examples. The providing unit provides, for example, a weekly exercise menu. For example, the providing unit advises the user to perform aerobic exercise three times a week and strength training twice a week. The providing unit can also provide an exercise plan that takes into consideration the type and intensity of exercise. For example, the providing unit adjusts the intensity of exercise according to the user's physical fitness level. By providing a specific plan, it is possible to provide a diet method that is easy for the user to follow. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide the user with a meal plan or exercise plan generated by AI.

[0033] The input unit may include a function that allows data to be input in the form of a simple question. Examples of simple question formats include, but are not limited to, multiple-choice questions and open-ended questions. The input unit inputs data using, for example, multiple-choice questions. For example, the input unit may display a question such as "How tall are you?" to the user and allow the user to select from options. The input unit may also input data using open-ended questions. For example, the input unit may display a question such as "Tell me about your diet" to the user and allow the user to freely write. This allows data to be input in a simple question format, thereby reducing the input burden on the user. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may display a question generated by AI to the user, and the AI ​​may analyze the user's answer.

[0034] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can automatically display data items that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest data items to be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method is provided to the user, improving input efficiency. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI, which can select the optimal input method.

[0035] The input unit can filter data based on the user's current health condition and lifestyle habits when inputting data. For example, when the user inputs their current health condition, the input unit displays only relevant data items. The input unit can also customize input items based on the user's lifestyle habits (diet, exercise, etc.). The input unit can also adjust the priority of input items according to the user's health condition and lifestyle habits. This allows highly relevant data to be input efficiently by filtering data based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's health condition and lifestyle habit data to a generation AI, which then performs filtering.

[0036] When inputting data, the input unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the input unit inputs data using voice recognition technology. Furthermore, if the user selects text input, the input unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the input unit can analyze and input data using image recognition technology. This improves the efficiency of data input by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's input method data to a generation AI, which then selects the optimal input means.

[0037] When inputting data, the input unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting data items related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting data items related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting data items related to daily life. In this way, highly relevant data can be efficiently input by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's geographical location information data to the generation AI, which can then prioritize inputting highly relevant data.

[0038] The input unit can analyze the user's social media activity and input relevant data when inputting data. For example, the input unit can automatically input meal details shared by the user on social media. The input unit can also automatically input the user's exercise records on social media. The input unit can also analyze the user's health-related posts on social media and input relevant data. In this way, by analyzing social media activity, relevant data can be automatically input. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI, and the generation AI can input the relevant data.

[0039] The input unit can customize the input method by reflecting the user's past feedback when inputting data. For example, the input unit preferentially suggests methods that the user found easy to input in the past. The input unit can also improve the input interface based on the user's past feedback. The input unit can also simplify items that the user found difficult to input in the past. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data into a generation AI, which can then customize the input method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data items. The analysis unit can also perform a simplified analysis on less important data items. The analysis unit can also adjust the display method of the analysis results according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, it is possible to perform a detailed analysis of important data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a nutrient analysis algorithm to dietary data. The analysis unit can also apply a calorie consumption analysis algorithm to exercise data. The analysis unit can also apply a weight fluctuation analysis algorithm to weight data. This allows for applying an appropriate analysis algorithm depending on the data category, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the data category to a generation AI, which can then apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, analyzes current data based on the user's past analysis results. The analysis unit can also extract trends from the user's past analysis results and reflect them in the current data. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI, and the generation AI can determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI, and the generation AI can adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0046] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationship between data during estimation. The estimation unit, for example, performs estimation by taking into account the interrelationship between dietary data and exercise data. The estimation unit can also perform estimation by taking into account the interrelationship between weight data and body fat percentage data. The estimation unit can also perform estimation by taking into account the interrelationship between health condition data and lifestyle habit data. In this way, by taking into account the interrelationship between data, the accuracy of the estimation can be improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit inputs data on the interrelationship between data to the generation AI, which can improve the accuracy of the estimation.

[0047] When making an estimation, the estimation unit can make an estimation taking into account attribute information of the data submitter. The estimation unit can make an estimation taking into account, for example, the submitter's age. The estimation unit can also make an estimation taking into account the submitter's gender. The estimation unit can also make an estimation taking into account the submitter's occupation. In this way, by taking into account the submitter's attribute information, it is possible to provide a more personalized estimation result. Some or all of the above-mentioned processing in the estimation unit can be performed using, for example, AI, or can be performed without using AI. For example, the estimation unit can input the submitter's attribute information data into a generation AI, which can then make an estimation.

[0048] The estimation unit can weight the estimation based on the frequency of data submission during estimation. For example, the estimation unit can weight data that is frequently submitted more highly. The estimation unit can also weight data that is less frequently submitted less highly. The estimation unit can also adjust the reliability of the estimation result based on the submission frequency. Thus, by weighting the estimation based on the frequency of data submission, it is possible to provide a highly reliable estimation result. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input data on the frequency of data submission to a generation AI, which then weights the estimation.

[0049] The estimation unit can make the estimation taking into account the geographical distribution of the data. The estimation unit can make the estimation taking into account, for example, the climate of the area where the user lives. The estimation unit can also make the estimation taking into account the food culture of the area where the user lives. The estimation unit can also make the estimation taking into account the exercise habits of the area where the user lives. In this way, by taking the geographical distribution of the data into account, it is possible to provide estimation results that correspond to regional characteristics. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input geographical distribution data of the data to a generation AI, which then makes the estimation.

[0050] During estimation, the estimation unit can improve the accuracy of the estimation by referring to literature related to the data. The estimation unit, for example, makes the estimation by referring to related academic papers. The estimation unit can also make the estimation by referring to related specialist books. The estimation unit can also make the estimation by referring to related research data. In this way, by referring to related literature, the accuracy of the estimation can be improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit inputs related literature data into the generation AI, which can improve the accuracy of the estimation.

[0051] The estimation unit can make an estimation taking into account the market value of the data. For example, the estimation unit can assign a higher weight to data with a high market value. The estimation unit can also assign a lower weight to data with a low market value. The estimation unit can also adjust the reliability of the estimation result based on the market value. In this way, by taking the market value of the data into consideration, it is possible to provide an estimation result based on highly valuable data. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input market value data of the data to a generation AI, which then performs the estimation.

[0052] The providing unit can adjust the level of detail of the advice provided based on the importance of the advice when providing the advice. For example, the providing unit provides a detailed explanation for important advice. The providing unit can also provide a simplified explanation for advice with low importance. The providing unit can also adjust the level of detail of the advice provided according to the importance of the advice. In this way, by adjusting the level of detail of the advice provided based on the importance of the advice, it is possible to provide a detailed explanation for important advice. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input importance data of the advice to a generating AI, which can adjust the level of detail of the advice provided.

[0053] The providing unit can apply different providing algorithms depending on the category of advice when providing the advice. For example, the providing unit can apply a nutrient analysis algorithm to advice regarding diet. The providing unit can also apply a calorie consumption analysis algorithm to advice regarding exercise. The providing unit can also apply a behavior change algorithm to advice regarding lifestyle habits. In this way, by applying an appropriate providing algorithm depending on the category of advice, it is possible to provide optimal advice for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input advice category data to a generation AI, which can then apply an appropriate providing algorithm.

[0054] The providing unit can improve the accuracy of the advice provided by referring to the user's past advice results. The providing unit provides current advice based on, for example, the user's past advice results. The providing unit can also extract trends from the user's past advice results and reflect them in the current advice. The providing unit can also adjust the providing algorithm by referring to the user's past advice results. In this way, the accuracy of the advice provided can be improved by referring to the past advice results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI, which can improve the accuracy of the advice provided.

[0055] The providing unit can determine the priority of advice provision based on the time of submission of the advice when it is provided. For example, the providing unit can provide the most recent advice preferentially. The providing unit can also provide advice that has been submitted earlier later. The providing unit can also adjust the order of advice provision based on the time of submission. In this way, by determining the priority of advice provision based on the time of submission of the advice, the most recent advice can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the time of submission of advice to a generating AI, and the generating AI can determine the priority of advice provision.

[0056] The providing unit can adjust the order of advice provision based on the relevance of the advice when providing the advice. For example, the providing unit can provide highly relevant advice preferentially. The providing unit can also postpone the provision of less relevant advice. The providing unit can also adjust the order of advice provision based on the relevance of the advice. In this way, by adjusting the order of advice provision based on the relevance of the advice, highly relevant advice can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the advice to a generation AI, and the generation AI can adjust the order of advice provision.

[0057] The providing unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. For example, if the user has technical expertise, the providing unit can provide advice that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can also provide advice in simple language. Furthermore, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI, which can adjust the use of technical terms in the advice.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The diet advice system can also acquire the user's sleep data and analyze it with an analysis unit. For example, the user's sleep time and sleep quality can be measured with a sensor and the data can be automatically acquired. The analysis unit can also estimate the user's physical condition and stress level based on the sleep data, and predict the optimal diet method. This allows the system to provide more effective diet advice based on the user's sleep patterns.

[0060] The analysis unit can acquire and analyze the user's dietary data in real time. For example, when the user eats a meal, the unit takes a photo of the meal with a smartphone camera and automatically recognizes the meal contents using image analysis technology. The analysis unit can also evaluate the user's nutritional balance based on the dietary data and provide advice on supplementing necessary nutrients. This allows the system to provide more specific diet advice based on the user's dietary contents.

[0061] The providing unit can acquire the user's exercise data in real time and analyze it with the analyzing unit. For example, when the user exercises, the exercise data is automatically acquired using a smartwatch or fitness tracker. The analyzing unit can also evaluate the effect of the user's exercise based on the exercise data and provide an optimal exercise plan. This makes it possible to provide more effective diet advice based on the user's exercise status.

[0062] The analysis unit can estimate a diet method specific to a region by taking into account the user's geographical location information. For example, it can estimate the optimal diet method by taking into account the climate and food culture of the region where the user lives. The analysis unit can also provide an exercise plan by taking into account the exercise habits and available facilities in the region. This allows it to provide diet advice tailored to the characteristics of the region and suggest a diet method that is easy for the user to follow.

[0063] The providing unit can analyze the user's social media activity and acquire related data. For example, the data on meals and exercise records shared by the user on social media can be automatically acquired and analyzed by the analysis unit. The providing unit can also provide optimal advice to the user based on health-related posts on social media. This makes it possible to utilize social media activity to provide more specific diet advice.

[0064] The providing unit can customize current advice based on the user's past advice results. For example, the providing unit can provide current advice by referring to diet methods that the user has had success with in the past. It can also advise the user to avoid methods that have failed in the past. This makes it possible to provide more effective diet advice by utilizing past advice results.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The input unit inputs the user's body shape and constitution data. The user's body shape and constitution data includes, for example, height, weight, body fat percentage, diet, exercise habits, etc. Specifically, the user can input data such as height 170 cm, weight 70 kg, body fat percentage 25%, high-calorie diet, and exercise habit about once a week. Step 2: The analysis unit uses AI to analyze the data input by the input unit. The analysis unit can automatically acquire the user's body shape data using a sensor, for example. It can also acquire the user's body shape data using image analysis technology. Step 3: The estimation unit uses AI to compare past data based on the data analyzed by the analysis unit and estimate the optimal diet method. For example, it compares data from similar people who have successfully dieted in the past and calculates the statistically optimal diet method. For example, if people with similar body types and constitutions have been successful with a low-carbohydrate diet, it determines that this method is optimal. Step 4: The providing unit provides the user with the diet method estimated by the estimating unit. For example, specific advice such as "Try a low-carbohydrate diet" is provided to the user. Specific meal plans and exercise plans can also be provided. For example, a detailed plan such as "Eat mainly eggs and vegetables for breakfast, chicken and salad for lunch, and fish and vegetables for dinner" is provided.

[0067] (Example 2) A diet advice system according to an embodiment of the present invention recognizes a user's body shape and constitution data and provides detailed advice on a diet method suited to that individual. In the diet advice system, a user inputs their own body shape and constitution data, and an AI system compares the data with data from similar individuals who have successfully lost weight in the past. The system then statistically calculates the optimal diet method and provides it to the user as advice. For example, the diet advice system allows the user to input detailed data such as height, weight, body fat percentage, dietary habits, and exercise habits. The diet advice system then uses an AI system to analyze the input data and compare it with data from similar individuals who have successfully lost weight in the past. For example, if people with similar body shapes and constitutions have been successful with a low-carbohydrate diet, the system determines that this method is optimal. Finally, the diet advice system provides the calculated optimal diet method to the user as advice. For example, the system may provide specific advice to the user, such as "Try a low-carbohydrate diet." This advice may also include specific meal plans and exercise plans. This allows the diet advice system to identify a diet method suited to the user's body shape and constitution, enabling the user to effectively diet. This allows the diet advice system to provide the optimal diet method based on the user's body shape and constitution. For example, trying methods that have been successful for people with similar body types and constitutions in the past can increase your chances of success.

[0068] A diet advice system according to an embodiment includes an input unit, an analysis unit, an estimation unit, and a provision unit. The input unit inputs data on a user's body shape and constitution. The data on a user's body shape and constitution includes, but is not limited to, height, weight, body fat percentage, diet, and exercise habits. The input unit can input, for example, data such as a user's height of 170 cm, weight of 70 kg, body fat percentage of 25%, high-calorie diet, and exercise habit of about once a week. The analysis unit uses AI to analyze the data input by the input unit. The analysis unit includes, for example, a function to automatically acquire the user's body shape data. For example, the analysis unit can automatically acquire the user's body shape data using a sensor. The analysis unit can also acquire the user's body shape data using image analysis technology. The estimation unit uses AI to compare past data based on the data analyzed by the analysis unit and estimate an optimal diet method. For example, the estimation unit compares data from similar individuals who have successfully dieted in the past and statistically calculates an optimal diet method. For example, if people with similar body shapes and constitutions have been successful with a low-carbohydrate diet, the system determines that the method is optimal. The providing unit provides the user with the diet method estimated by the estimation unit. The providing unit provides the user with specific advice, such as "Try a low-carbohydrate diet." The providing unit can also provide specific meal plans and exercise plans. For example, a detailed plan such as "Eat mainly eggs and vegetables for breakfast, chicken and salad for lunch, and fish and vegetables for dinner" is provided. This allows the diet advice system according to the embodiment to provide the optimal diet method based on the user's body shape and constitution. For example, trying a method that has been successful for people with similar body shapes and constitutions in the past increases the probability of success.

[0069] The analysis unit can automatically acquire the user's body shape data. Examples of automatic acquisition include, but are not limited to, measurements using a sensor and image analysis technology. The analysis unit automatically acquires the user's body shape data using, for example, a sensor. For example, the analysis unit measures the user's height and weight using a sensor and automatically acquires the data. The analysis unit can also acquire the user's body shape data using image analysis technology. For example, the analysis unit takes a full-body photo of the user and estimates the height and weight using image analysis technology. This automatically acquires the user's body shape data, thereby eliminating the need for data input. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data acquired by a sensor into AI, which then analyzes the data to acquire body shape data.

[0070] The estimation unit can estimate the optimal diet method using AI. AI includes, but is not limited to, technologies such as neural networks and deep learning. The estimation unit estimates the optimal diet method using, for example, a neural network. For example, the estimation unit inputs past data into a neural network to estimate the optimal diet method. The estimation unit can also estimate the optimal diet method using deep learning. For example, the estimation unit inputs past data into a deep learning model to estimate the optimal diet method. In this way, by using AI, the optimal diet method can be estimated with high accuracy. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI. For example, the estimation unit inputs past data into AI, and the AI ​​can estimate the optimal diet method.

[0071] The providing unit can provide detailed meal plans and exercise plans. The detailed meal plans include, for example, a daily meal menu and nutritional balance, but are not limited to these examples. The providing unit provides, for example, a daily meal menu. For example, the providing unit advises the user to eat mainly eggs and vegetables for breakfast, chicken and salad for lunch, and fish and vegetables for dinner. The providing unit can also provide meal plans that take nutritional balance into consideration. For example, the providing unit provides a meal plan that takes into consideration the balance of protein, fat, and carbohydrates. The exercise plan includes, for example, a weekly exercise menu and the type and intensity of exercise, but are not limited to these examples. The providing unit provides, for example, a weekly exercise menu. For example, the providing unit advises the user to perform aerobic exercise three times a week and strength training twice a week. The providing unit can also provide an exercise plan that takes into consideration the type and intensity of exercise. For example, the providing unit adjusts the intensity of exercise according to the user's physical fitness level. By providing a specific plan, it is possible to provide a diet method that is easy for the user to follow. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide the user with a meal plan or exercise plan generated by AI.

[0072] The input unit may include a function that allows data to be input in the form of a simple question. Examples of simple question formats include, but are not limited to, multiple-choice questions and open-ended questions. The input unit inputs data using, for example, multiple-choice questions. For example, the input unit may display a question such as "How tall are you?" to the user and allow the user to select from options. The input unit may also input data using open-ended questions. For example, the input unit may display a question such as "Tell me about your diet" to the user and allow the user to freely write. This allows data to be input in a simple question format, thereby reducing the input burden on the user. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may display a question generated by AI to the user, and the AI ​​may analyze the user's answer.

[0073] The input unit can estimate the user's emotions and adjust the timing of data input based on the emotion data. For example, if the user is feeling stressed, the input unit temporarily suspends input and prompts the user to input again when the user is able to relax. Furthermore, if the user is relaxed, the input unit can continuously prompt the user to input data, thereby efficiently collecting information. Furthermore, if the user is in a hurry, the input unit can provide a simplified input form to enable quick input. This adjusts the timing of data input according to the user's emotions, thereby reducing stress and realizing efficient data input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or without AI. For example, the input unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and adjust the timing of data input.

[0074] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can automatically display data items that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest data items to be used in a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method is provided to the user, improving input efficiency. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past input history data to a generation AI, which can select the optimal input method.

[0075] The input unit can filter data based on the user's current health condition and lifestyle habits when inputting data. For example, when the user inputs their current health condition, the input unit displays only relevant data items. The input unit can also customize input items based on the user's lifestyle habits (diet, exercise, etc.). The input unit can also adjust the priority of input items according to the user's health condition and lifestyle habits. This allows highly relevant data to be input efficiently by filtering data based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's health condition and lifestyle habit data to a generation AI, which then performs filtering.

[0076] When inputting data, the input unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the input unit inputs data using voice recognition technology. Furthermore, if the user selects text input, the input unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the input unit can analyze and input data using image recognition technology. This improves the efficiency of data input by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's input method data to a generation AI, which then selects the optimal input means.

[0077] The input unit can estimate the user's emotions and determine the priority of data to be input based on the emotion data. For example, when the user is stressed, the input unit can prioritize input of important data items. Furthermore, when the user is relaxed, the input unit can also prompt the user to input detailed data items. Furthermore, when the user is in a hurry, the input unit can also prompt the user to input the minimum number of data items. This allows important data to be input preferentially by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and determine the priority of the data.

[0078] When inputting data, the input unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit can prioritize inputting data items related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting data items related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting data items related to daily life. In this way, highly relevant data can be efficiently input by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's geographical location information data to the generation AI, which can then prioritize inputting highly relevant data.

[0079] The input unit can analyze the user's social media activity and input relevant data when inputting data. For example, the input unit can automatically input meal details shared by the user on social media. The input unit can also automatically input the user's exercise records on social media. The input unit can also analyze the user's health-related posts on social media and input relevant data. In this way, by analyzing social media activity, relevant data can be automatically input. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI, and the generation AI can input the relevant data.

[0080] The input unit can customize the input method by reflecting the user's past feedback when inputting data. For example, the input unit preferentially suggests methods that the user found easy to input in the past. The input unit can also improve the input interface based on the user's past feedback. The input unit can also simplify items that the user found difficult to input in the past. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past feedback data into a generation AI, which can then customize the input method.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotion data. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the way the analysis is presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into a generation AI, which can estimate the emotion and adjust the way the analysis is presented.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data items. The analysis unit can also perform a simplified analysis on less important data items. The analysis unit can also adjust the display method of the analysis results according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, it is possible to perform a detailed analysis of important data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a nutrient analysis algorithm to dietary data. The analysis unit can also apply a calorie consumption analysis algorithm to exercise data. The analysis unit can also apply a weight fluctuation analysis algorithm to weight data. This allows for applying an appropriate analysis algorithm depending on the data category, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the data category to a generation AI, which can then apply an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, analyzes current data based on the user's past analysis results. The analysis unit can also extract trends from the user's past analysis results and reflect them in the current data. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotion data. For example, if the user is nervous, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the length of the analysis according to the user's emotions, an analysis result of an appropriate length for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI, and the generation AI can determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI, and the generation AI can adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0089] The estimation unit can estimate the user's emotions and adjust the estimation criteria based on the emotion data. For example, if the user is nervous, the estimation unit can adopt a conservative estimation criterion. Also, if the user is relaxed, the estimation unit can adopt an aggressive estimation criterion. Also, if the user is in a hurry, the estimation unit can adopt a criterion that provides a quick estimation result. By adjusting the estimation criteria according to the user's emotions, it is possible to provide an appropriate estimation result for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the estimation unit can be performed using, for example, AI, or without AI. For example, the estimation unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the estimation criteria.

[0090] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationship between data during estimation. The estimation unit, for example, performs estimation by taking into account the interrelationship between dietary data and exercise data. The estimation unit can also perform estimation by taking into account the interrelationship between weight data and body fat percentage data. The estimation unit can also perform estimation by taking into account the interrelationship between health condition data and lifestyle habit data. In this way, by taking into account the interrelationship between data, the accuracy of the estimation can be improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit inputs data on the interrelationship between data to the generation AI, which can improve the accuracy of the estimation.

[0091] When making an estimation, the estimation unit can make an estimation taking into account attribute information of the data submitter. The estimation unit can make an estimation taking into account, for example, the submitter's age. The estimation unit can also make an estimation taking into account the submitter's gender. The estimation unit can also make an estimation taking into account the submitter's occupation. In this way, by taking into account the submitter's attribute information, it is possible to provide a more personalized estimation result. Some or all of the above-mentioned processing in the estimation unit can be performed using, for example, AI, or can be performed without using AI. For example, the estimation unit can input the submitter's attribute information data into a generation AI, which can then make an estimation.

[0092] The estimation unit can weight the estimation based on the frequency of data submission during estimation. For example, the estimation unit can weight data that is frequently submitted more highly. The estimation unit can also weight data that is less frequently submitted less highly. The estimation unit can also adjust the reliability of the estimation result based on the submission frequency. Thus, by weighting the estimation based on the frequency of data submission, it is possible to provide a highly reliable estimation result. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input data on the frequency of data submission to a generation AI, which then weights the estimation.

[0093] The estimation unit can estimate the user's emotions and adjust the display order of the estimation results based on the emotion data. For example, if the user is nervous, the estimation unit can display important results first. Furthermore, if the user is relaxed, the estimation unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the estimation unit can display results that highlight the main points first. By adjusting the display order of the estimation results according to the user's emotions, it is possible to provide results that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the user's facial expression data into the generation AI, which then estimates the emotion and adjusts the display order of the estimation results.

[0094] The estimation unit can make the estimation taking into account the geographical distribution of the data. The estimation unit can make the estimation taking into account, for example, the climate of the area where the user lives. The estimation unit can also make the estimation taking into account the food culture of the area where the user lives. The estimation unit can also make the estimation taking into account the exercise habits of the area where the user lives. In this way, by taking the geographical distribution of the data into account, it is possible to provide estimation results that correspond to regional characteristics. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input geographical distribution data of the data to a generation AI, which then makes the estimation.

[0095] During estimation, the estimation unit can improve the accuracy of the estimation by referring to literature related to the data. The estimation unit, for example, makes the estimation by referring to related academic papers. The estimation unit can also make the estimation by referring to related specialist books. The estimation unit can also make the estimation by referring to related research data. In this way, by referring to related literature, the accuracy of the estimation can be improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit inputs related literature data into the generation AI, which can improve the accuracy of the estimation.

[0096] The estimation unit can make an estimation taking into account the market value of the data. For example, the estimation unit can assign a higher weight to data with a high market value. The estimation unit can also assign a lower weight to data with a low market value. The estimation unit can also adjust the reliability of the estimation result based on the market value. In this way, by taking the market value of the data into consideration, it is possible to provide an estimation result based on highly valuable data. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input market value data of the data to a generation AI, which then performs the estimation.

[0097] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the emotion data. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. Furthermore, if the user is relaxed, the providing unit can also provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can also provide advice that is more to the point. By adjusting the way the advice is presented according to the user's emotions, it is possible to provide advice that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the way the advice is presented.

[0098] The providing unit can adjust the level of detail of the advice provided based on the importance of the advice when providing the advice. For example, the providing unit provides a detailed explanation for important advice. The providing unit can also provide a simplified explanation for advice with low importance. The providing unit can also adjust the level of detail of the advice provided according to the importance of the advice. In this way, by adjusting the level of detail of the advice provided based on the importance of the advice, it is possible to provide a detailed explanation for important advice. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input importance data of the advice to a generating AI, which can adjust the level of detail of the advice provided.

[0099] The providing unit can apply different providing algorithms depending on the category of advice when providing the advice. For example, the providing unit can apply a nutrient analysis algorithm to advice regarding diet. The providing unit can also apply a calorie consumption analysis algorithm to advice regarding exercise. The providing unit can also apply a behavior change algorithm to advice regarding lifestyle habits. In this way, by applying an appropriate providing algorithm depending on the category of advice, it is possible to provide optimal advice for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input advice category data to a generation AI, which can then apply an appropriate providing algorithm.

[0100] The providing unit can improve the accuracy of the advice provided by referring to the user's past advice results. The providing unit provides current advice based on, for example, the user's past advice results. The providing unit can also extract trends from the user's past advice results and reflect them in the current advice. The providing unit can also adjust the providing algorithm by referring to the user's past advice results. In this way, the accuracy of the advice provided can be improved by referring to the past advice results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI, which can improve the accuracy of the advice provided.

[0101] The providing unit can estimate the user's emotions and adjust the length of the advice to be provided based on the emotion data. For example, if the user is nervous, the providing unit can provide short, to-the-point advice. The providing unit can also provide detailed advice if the user is relaxed. The providing unit can also provide concise advice if the user is in a hurry. By adjusting the length of the advice according to the user's emotions, it is possible to provide advice of an appropriate length for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the length of the advice.

[0102] The providing unit can determine the priority of advice provision based on the time of submission of the advice when it is provided. For example, the providing unit can provide the most recent advice preferentially. The providing unit can also provide advice that has been submitted earlier later. The providing unit can also adjust the order of advice provision based on the time of submission. In this way, by determining the priority of advice provision based on the time of submission of the advice, the most recent advice can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the time of submission of advice to a generating AI, and the generating AI can determine the priority of advice provision.

[0103] The providing unit can adjust the order of advice provision based on the relevance of the advice when providing the advice. For example, the providing unit can provide highly relevant advice preferentially. The providing unit can also postpone the provision of less relevant advice. The providing unit can also adjust the order of advice provision based on the relevance of the advice. In this way, by adjusting the order of advice provision based on the relevance of the advice, highly relevant advice can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the advice to a generation AI, and the generation AI can adjust the order of advice provision.

[0104] The providing unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. For example, if the user has technical expertise, the providing unit can provide advice that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can also provide advice in simple language. Furthermore, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the user's level of expertise, it is possible to provide advice that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI, which can adjust the use of technical terms in the advice. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, estimation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and the user inputs data such as height, weight, body fat percentage, dietary habits, and exercise habits. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collates past data to estimate an optimal diet method. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides specific advice to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and the user inputs data such as height, weight, body fat percentage, dietary habits, and exercise habits by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collates past data to estimate the optimal diet method. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides specific advice to the user by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs data such as height, weight, body fat percentage, dietary habits, and exercise habits by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collates past data to estimate the optimal diet method. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314, and provides specific advice to the user by voice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and the user inputs data such as height, weight, body fat percentage, dietary habits, and exercise habits by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using AI. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collates past data to estimate the optimal diet method. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides specific advice to the user by voice.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The diet advice system can also acquire the user's sleep data and analyze it with an analysis unit. For example, the user's sleep time and sleep quality can be measured with a sensor and the data can be automatically acquired. The analysis unit can also estimate the user's physical condition and stress level based on the sleep data, and predict the optimal diet method. This allows the system to provide more effective diet advice based on the user's sleep patterns.

[0107] The analysis unit can acquire and analyze the user's dietary data in real time. For example, when the user eats a meal, the unit takes a photo of the meal with a smartphone camera and automatically recognizes the meal contents using image analysis technology. The analysis unit can also evaluate the user's nutritional balance based on the dietary data and provide advice on supplementing necessary nutrients. This allows the system to provide more specific diet advice based on the user's dietary contents.

[0108] The estimation unit can estimate the user's emotions and adjust the estimated diet method based on the estimated emotions. For example, if the user is feeling stressed, the estimation unit can estimate a diet method aimed at reducing stress. Also, if the user is relaxed, the estimation unit can estimate an aggressive diet method. This allows for a more effective diet by providing a diet method that matches the user's emotions.

[0109] The providing unit can acquire the user's exercise data in real time and analyze it with the analyzing unit. For example, when the user exercises, the exercise data is automatically acquired using a smartwatch or fitness tracker. The analyzing unit can also evaluate the effect of the user's exercise based on the exercise data and provide an optimal exercise plan. This makes it possible to provide more effective diet advice based on the user's exercise status.

[0110] The input unit can estimate the user's emotions and customize the data entry interface based on the emotion data. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, detailed input items can be displayed. This reduces the burden of data entry by providing an interface that corresponds to the user's emotions, making it possible to achieve efficient data collection.

[0111] The analysis unit can estimate a diet method specific to a region by taking into account the user's geographical location information. For example, it can estimate the optimal diet method by taking into account the climate and food culture of the region where the user lives. The analysis unit can also provide an exercise plan by taking into account the exercise habits and available facilities in the region. This allows it to provide diet advice tailored to the characteristics of the region and suggest a diet method that is easy for the user to follow.

[0112] The estimation unit can estimate the user's emotions and adjust the estimation algorithm based on the emotion data. For example, if the user is nervous, a conservative algorithm can be adopted. On the other hand, if the user is relaxed, an aggressive algorithm can be adopted. In this way, by providing an estimation algorithm according to the user's emotions, a more appropriate diet method can be estimated.

[0113] The providing unit can analyze the user's social media activity and acquire related data. For example, the data on meals and exercise records shared by the user on social media can be automatically acquired and analyzed by the analysis unit. The providing unit can also provide optimal advice to the user based on health-related posts on social media. This makes it possible to utilize social media activity to provide more specific diet advice.

[0114] The providing unit can estimate the user's emotions and adjust the content of advice based on the emotion data. For example, if the user is feeling stressed, the providing unit can suggest meals and exercise that have a relaxing effect. Also, if the user is relaxed, the providing unit can suggest an active diet method. In this way, by providing advice according to the user's emotions, a more effective diet can be achieved.

[0115] The providing unit can customize current advice based on the user's past advice results. For example, the providing unit can provide current advice by referring to diet methods that the user has had success with in the past. It can also advise the user to avoid methods that have failed in the past. This makes it possible to provide more effective diet advice by utilizing past advice results.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The input unit inputs the user's body shape and constitution data. The user's body shape and constitution data includes, for example, height, weight, body fat percentage, diet, exercise habits, etc. Specifically, the user can input data such as height 170 cm, weight 70 kg, body fat percentage 25%, high-calorie diet, and exercise habit about once a week. Step 2: The analysis unit uses AI to analyze the data input by the input unit. The analysis unit can automatically acquire the user's body shape data using a sensor, for example. It can also acquire the user's body shape data using image analysis technology. Step 3: The estimation unit uses AI to compare past data based on the data analyzed by the analysis unit and estimate the optimal diet method. For example, it compares data from similar people who have successfully dieted in the past and calculates the statistically optimal diet method. For example, if people with similar body types and constitutions have been successful with a low-carbohydrate diet, it determines that this method is optimal. Step 4: The providing unit provides the user with the diet method estimated by the estimating unit. For example, specific advice such as "Try a low-carbohydrate diet" is provided to the user. Specific meal plans and exercise plans can also be provided. For example, a detailed plan such as "Eat mainly eggs and vegetables for breakfast, chicken and salad for lunch, and fish and vegetables for dinner" is provided.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting data on the user's body shape and constitution; an analysis unit that analyzes the data input by the input unit; An estimation unit that compares past data based on the data analyzed by the analysis unit and estimates an appropriate diet method; a providing unit that provides the diet method estimated by the estimation unit to a user. A system characterized by:

2. The analysis unit Includes a function to automatically acquire user body shape data 2. The system of claim 1.

3. The estimation unit Using AI to predict the optimal diet method 2. The system of claim 1.

4. The providing unit Providing detailed meal and exercise plans 2. The system of claim 1.

5. The input unit Includes the ability to enter data in the form of simple questions 2. The system of claim 1.

6. The input unit Estimate the user's emotions and adjust the timing of data input based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

8. The input unit Filtering data entry based on the user's current health status and lifestyle habits 2. The system of claim 1.

9. The input unit When entering data, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

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